C-reactive protein to albumin ratio in critical care settings

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=> true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 3454 "Ancho" => 2175 "Tamanyo" => 436691 ] ] "descripcion" => array:1 [ "en" => "Comparison of receiver operating characteristic (ROC) curves of the C-reactive protein to albumin ratio (CAR) on Day 3 for in-hospital mortality by COVID-19 diagnosis." ] ] ] "textoCompleto" => "IntroductionRisk assessment for potential complications and in-hospital mortality is standard practice in critical care settings.<a class="elsevierStyleCrossRefs" href="#bib0190">1,2</a> Many predictive models, such as the Acute Physiology and Chronic Health Disease Classification System II (APACHE II)<a class="elsevierStyleCrossRef" href="#bib0200">3</a> and Sequential Organ Failure Assessment (SOFA),<a class="elsevierStyleCrossRef" href="#bib0205">4</a> require numerous clinical and laboratory parameters. This situation may limit their applicability in low- to middle-income countries, especially regarding time and resource requirements.<a class="elsevierStyleCrossRefs" href="#bib0210">5,6</a> For this, other prognostic markers have emerged to simplify the risk assessment, reflecting the dynamics of the inflammatory response.<a class="elsevierStyleCrossRef" href="#bib0220">7</a> The C-reactive protein to albumin ratio (CAR), expressed as a fraction between the measurement units of its components (mg/L or mg/dL) over (g/L or g/dL),<a class="elsevierStyleCrossRefs" href="#bib0225">8,9</a> has been used to assess hospital stay and mortality in critically ill septic and nonseptic patients.<a class="elsevierStyleCrossRefs" href="#bib0235">10,11</a>Moreover, serum C-reactive protein (CRP) and albumin are used separately to predict mortality in critically ill patients.<a class="elsevierStyleCrossRef" href="#bib0245">12</a> CRP is an acute-phase protein produced in response to infection, ischemia, trauma, and other inflammatory conditions and correlates with inflammation during the early course of illness.<a class="elsevierStyleCrossRefs" href="#bib0250">13,14</a> In contrast, serum albumin is an acute-phase protein that is inversely correlated with the intensity of the inflammatory response.<a class="elsevierStyleCrossRef" href="#bib0260">15</a>Recent retrospective studies involved CAR as a prognostic marker in COVID-19 patients<a class="elsevierStyleCrossRefs" href="#bib0265">16–18</a> and found differential CAR values in non-survivors with nonsevere COVID-19 compared to survivors with the same condition.<a class="elsevierStyleCrossRefs" href="#bib0280">19,20</a> However, no studies have examined the differences in CAR cut-off points between patients with and without COVID-19. Thus, we aimed to assess differences in CAR to predict in-hospital death in a mixed critical care setting, including patients with and without COVID-19, and explore potential cut-off points for predicting adverse outcomes, such as in-hospital death.Materials and methodsStudy designWe conducted a prospective cohort study in two tertiary reference critical care facilities in Cartagena, Colombia. Adult patients (age ≥18 years) with and without COVID-19 admitted to the intensive care unit (ICU) in a critical condition from November 1, 2020, to May 31, 2021, were included. We excluded patients with chronic hepatic illness, malnutrition, or pregnancy due to uncertain effects on albumin levels. Patients who died within 24h of ICU admission were also excluded from the final analysis. Approval was obtained from the ethics committee of Universidad de Cartagena (Act Number: DI-FM-CERT-2906-2021). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study.Sample sizeWe estimated the frequency of survival in patients with low IFC (cut-off point of <88.55) according to the results of the study published by Palacios et al.<a class="elsevierStyleCrossRef" href="#bib0290">21</a> Based on this, we calculated a sample size of 156 patients (level of confidence of 95% and alpha error of 5%). We estimated a lost-to-follow-up rate of 10%, so we included 171 patients in the study.Data collectionBaseline characteristics, laboratory findings, and disease severity scores, including APACHE II and SOFA scores, were obtained upon admission to the ICU. CRP (mg/dl) and albumin concentrations (g/dl) were obtained at admission and on Day 3. Blood parameters were assessed by an Automated Analyser Mindray BC-240 (Mindray Corporation, Shenzhen, China). CRP was analyzed using an immunoturbidimetric method, and albumin was assayed using a photoelectric colorimeter. We considered a normal albumin concentration from 3.5 to 5.0g/dL and CRP from 0 to 5mg/L. CAR was calculated by dividing CRP levels by albumin (mg/g). Fluid balance data were obtained for the first five days after admission to the ICU. The primary outcome was all-cause in-hospital death. Secondary outcomes were required invasive mechanical ventilation (IMV), vasopressor therapy, presence of hospital-acquired infections, and length of stay.Statistical analysisCategorical variables are presented with absolute and relative frequencies, and quantitative variables are presented as medians and interquartile ranges. Bivariate analysis was conducted using a dependent variable classifying in-hospital death as non-survivors using the chi-square test for categorical variables and the Wilcoxon test for quantitative variables. We assessed CAR on days 1 and 3, along with APACHE II and SOFA scores at admission, to predict all-cause in-hospital death, vasopressor requirement, need for mechanical ventilation, and hospital-acquired infection. We used Youden's index to determine the optimal CAR cut-off point for each outcome for participants with and without COVID-19. To assess the cause of differences between COVID-19 and non-COVID CAR results, we compared albumin and CRP values between the two groups. We compared diagnostic performance between groups using the Mann–Whitney U test for equality between receiver operating characteristic areas. We also calculated sensitivity (SE), specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) at cut-off points for CAR.To avoid possible bias in mortality prediction between COVID-19 patients with and without COVID-19, we independently compared AUC and diagnostic accuracy as a sensitivity analysis. A p value of less than 0.05 was considered statistically significant. All data analyses were performed using Stata 17.0 software (Stata Corp, Texas, USA).ResultsThe study cohort included 275 adults admitted to the ICU in the study period; 99 were excluded. The data of 176 patients were included for analysis; 85 patients (48.3%) had COVID-19. CAR was not available for 17 patients on the third day after admission due to early discharge or death (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>).<elsevierMultimedia ident="fig0005"></elsevierMultimedia>Baseline characteristics at ICU admission are presented in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>. The median age was 62.5 years (interquartile range [IQR] 49, 75.5). The most common diagnosis at admission was COVID-19 infection (n=85, 48.3%). The median APACHE II and SOFA scores were 10 (IQR 7, 16) and 3 points (IQR 2, 4), respectively. All-cause in-hospital death was 31.3% (n=55). Non-survivors were older (72 years vs. 58 years; p<0.001) and had a higher frequency of chronic hypertension (74.5% vs. 52.1%; p=0.005). At admission, non-survivors had a higher frequency of COVID-19 infection (67.3% vs. 39.7%; p<0.001) and a lower proportion of cardiovascular diagnosis (1.8% vs. 17.4%; p=0.004). The APACHE II and SOFA scores were higher among non-survivors (15 points vs. 9 points; p<0.001, and 4 points vs. 2 points; p<0.001, respectively). Non-survivors had lower albumin levels (3.5g/dL vs. 3.8g/dL; p=0.0031), higher CRP levels (96mg/dL vs. 24mg/dL; p≤0.001), and higher CAR at admission (21.8 vs. 5.8; p≤0.001) and on Day 3 (29.2 vs. 6.6; p≤0.001) (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>). We assessed variation in albumin and CRP levels on Day 3 compared to that at admission to evaluate CAR dynamics. CRP at admission was higher in COVID-19 patients (48mg/dL vs. 12mg/dL, respectively). We did not find significant changes between groups in CRP and albumin levels from Day 1 to 3, even when comparing patients with and without COVID-19 (<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>).<elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia>We described the vital signs and laboratory tests to calculate the APACHE II and SOFA scores at ICU admission in <a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Table 1</a>. In <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>, we present the analysis of CRP, albumin, and CAR based on COVID-19 diagnosis and survival status. We found that non-survivors had a significant decline in albumin on Day 3 (−0.25g/dL vs. −0.11g/dL, p=0.007) and a significant increase in CRP levels (40mg/dL vs. 12mg/dL; p=0.017) compared with survivors.<elsevierMultimedia ident="fig0010"></elsevierMultimedia>Among participants without COVID-19, on Day 3, CRP levels were higher (median 116.5 IQR 70, 157.5 vs. 23.1 IQR 6, 91; p<0.001), and albumin levels were lower (median 2.9 IQR 2.8, 1.4 vs. 3.6 IQR 3.3, 3.9, p<0.001) in non-survivors compared to survivors. In the COVID-19 group, CRP on Day 3 was higher in non-survivors than survivors (median 96 IQR 38.7, 117.5 vs. 24 IQR 12, 96). We did not find any differences in albumin between survivors and non-survivors in the COVID-19 group.Fluid balances were available for 164 (93%) patients in the first 24h, 149 (84%) on Day 2, 123 (69%) on Day 3, 96 (54%) on Day 4, and 75 (42%) on Day 5. The differences in fluid balance data availability were explained due to death or transfer to another hospital unit (e.g., general ward). The comparison of fluid balances between survivors and non-survivors is also shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>. Nonsurvivors had higher rates of positive fluid balance throughout follow-up, with differences on Days 2–4 (Day 2: 1876mL vs. 800mL, p<0.001; Day 3: 2311mL vs. 1170mL, p<0.001; and Day 4: 3198mL vs. 2018mL, p=0.007). When assessing differences in fluid balance between participants with and without COVID-19, we found the daily fluid balance to be higher, and cumulative fluid balance increased over time (on Days 2–4) for non-survivors in the non-COVID group. In the COVID-19 group, differences in fluid balances were only observed on Day 2; this difference was not maintained over time (<a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>).<elsevierMultimedia ident="fig0015"></elsevierMultimedia><a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Table 2</a> describes different reported outcomes. Vasopressor therapy was required in 34.7% (n=61) of patients, 12.5% (n=22) required more than one vasopressor, 31.2% (n=55) required IMV, and 18.2% (n=32) acquired hospital infections. The median ICU length was 10 days (IQR 4.5, 16). Non-survivors required vasopressor therapy (46% vs. 15%; p=0.001), inotropic therapy (6% vs. 1%; p=0.002), and IMV (47% vs. 20%; p<0.001) more frequently and had higher rates of hospital-acquired infections (18% vs. 14%; p=0.001) than survivors. The length of ICU stay among groups did not differ.<a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> shows the diagnostic performance of CAR at admission and on Day 3 for in-hospital death, including the cut-off point analysis for patients with and without COVID-19. In non-COVID-19 patients, CAR cut-off points were 28.5mg/g and 31.6mg/g at admission and on Day 3, respectively. For COVID-19 patients, CAR cut-off points were lower: 13.5mg/g at admission and 7.4mg/g on Day 3. Compared to CAR at admission, CAR on Day 3 was better, with a higher area under the curve (AUC) for all-cause in-hospital death in the non-COVID and COVID-19 groups (0.81 [0.69–0.92] vs. 0.71 [0.58–0.85]; and 0.73 [0.62–0.84] vs. 0.61[0.49–0.740]), respectively. CAR on Day 3 had higher sensitivity in both groups compared to CAR at admission (non-COVID 75.0% vs. 55.6%; and COVID-1983.3% vs. 59.5%). When we compared the AUC of CAR Day 3 according to COVID-19 status, even though a higher AUC was observed in the non-COVID group, no significant differences were found compared to the AUC in the COVID-19 group (AUC 0.81 vs. 0.73, respectively, p=0.347; <a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a>).<elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia>When comparing the diagnostic performance of CAR on Day 3 with CAR at admission, APACHE II, and SOFA scores, we found no differences between CAR on Day 3 and APACHE or SOFA scores in the COVID-19 and non-COVID groups (<a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Figure 1</a>). The AUC for CAR at admission and on Day 3 for vasopressor requirement, mechanical ventilation, and hospital-acquired infection in the COVID-19 and non-COVID groups are shown in <a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Figure 2</a>.DiscussionOur results showed that CAR is a prognostic factor in critical care settings independent of COVID-19 status. We found CAR measured at admission and on Day 3 to predict mortality in critically ill patients, both with and without COVID-19. However, the kinetics of the ratio and its components differed between groups, with lower CAR cut-off points on Day 3 for predicting adverse outcomes, especially in-hospital death, for COVID-19 participants (7.4mg/g vs. 31.6mg/g, respectively). Additionally, we found a percentage decrease in serum albumin (delta of albumin) and CAR changes associated with higher fluid balance, a greater need for vasopressors and IMV, and higher mortality in critically ill patients with and without COVID-19.CAR is a recognized predictor of adverse clinical outcomes in various clinical settings.<a class="elsevierStyleCrossRef" href="#bib0295">22</a> Before COVID-19, Findikli et al.<a class="elsevierStyleCrossRef" href="#bib0300">23</a> found CAR at admission to be an independent predictor of 60-day mortality in critically ill medical patients. In our study, CAR at admission, and more significantly, on Day 3, predicted mortality. This biphasic presentation could reflect the expected changes reported in COVID-19.<a class="elsevierStyleCrossRef" href="#bib0305">24</a> Regardless, in the non-COVID population, Cuthbertson<a class="elsevierStyleCrossRef" href="#bib0310">25</a> introduced the concept of a dual metabolic response in critically ill patients in 1942, which promoted fluid resuscitation in the first three days. Twenty years ago, Malbrain<a class="elsevierStyleCrossRef" href="#bib0315">26</a> referred to the CRP to albumin ratio as the capillary leak index (CLI). Thirteen years later, he reported a correlation between CLI, fluid balance with the presentation of organ dysfunction, and poor prognosis, introducing the concept of globally increased permeability syndrome (GIPS).<a class="elsevierStyleCrossRef" href="#bib0320">27</a>In 2012, Cordemans et al.<a class="elsevierStyleCrossRef" href="#bib0325">28</a> reported a biphasic course of CLI, with a maximum value on the third day. In this retrospective study, CLI was related to positive fluid balance, high extravascular pulmonary water, multiple organ failure, and mortality. Thus, GIPS would represent a “third hit” of shock following acute injury and multiorgan dysfunction syndrome, affected by the amounts of fluid administered and/or the fluid balance on the third day, variables associated with higher mortality in the literature. However, these findings need to be confirmed by additional studies. Nevertheless, our results suggest that CAR measurements should continue beyond admission, especially on the third day, as this parameter may be more accurate in predicting mortality. One possible explanation for this finding is that the ratio correlated with inflammation dynamics, irrespective of the etiology of the initial injury. In addition, the association of CAR and fluid balance on the third day with the highest mortality supports the importance of timely interventions to prevent fluid overload, reducing the probability of GIPS and mortality. Furthermore, CAR could provide a standard of care for predicting response to initial resuscitation, maintaining adequate fluid balance, and avoiding GIPS, multiorgan failure, and death.CAR components (CRP and albumin) had differential performances among the observed groups. For instance, in our non-survivor group of patients without COVID-19, their dynamics could reflect the level of inflammation related to a critical condition. In this group, albumin significantly decreased on the third day, demarcating a difference between COVID-19 and non-COVID populations. Previous studies linked hypoalbuminemia with poor outcomes in non-COVID-19<a class="elsevierStyleCrossRefs" href="#bib0305">24,29</a> and COVID-19<a class="elsevierStyleCrossRef" href="#bib0335">30</a> populations. Nevertheless, hypoalbuminemia seems to be a marker of severe COVID-19 infection related to hyperinflammation.<a class="elsevierStyleCrossRef" href="#bib0340">31</a> Based on our results, albumin kinetics seem more critical to assess in patients without COVID-19.The comparison between CAR at admission and Day 3 with SOFA and APACHE II scores showed no significant differences in the observed AUC. These results suggest that CAR performs as well as established severity scores to predict mortality. Moreover, CAR offers an additional advantage, as it requires only two laboratory tests, is easily obtained in the emergency room, general ward, or critical care settings and is easier to calculate. Therefore, CAR may be time-saving and, if standardized, its interpretation could be as valuable as other widely used measures.When assessing COVID-19 status, this condition is an independent predictor of mortality. Previous studies have shown increased mortality in critically ill COVID-19 patients with a marked hyperinflammatory condition. Considering the influence of COVID-19 infection in interpreting widely used tools in critical care, we also assessed CAR differences between patients with and without COVID-19. In our study, CAR performance was similar between the two groups. Several studies have evaluated CAR performance in the COVID-19 setting; Bannaga<a class="elsevierStyleCrossRef" href="#bib0345">32</a> evaluated CRP and albumin independently, not as a ratio, and a recent meta-analysis showed that elevated CRP and hypoalbuminemia were independent predictors of mortality in COVID-19 patients.<a class="elsevierStyleCrossRef" href="#bib0350">33</a> Karakoyun et al., <a class="elsevierStyleCrossRef" href="#bib0265">16</a> Güney et al.,<a class="elsevierStyleCrossRef" href="#bib0285">20</a> Kalabin et al.,<a class="elsevierStyleCrossRef" href="#bib0355">34</a> and Xue et al.<a class="elsevierStyleCrossRef" href="#bib0360">35</a> used CAR to discriminate between severe and nonsevere COVID-19 patients and assessed the ratio to predict mortality. In addition, Kelesoglu et al. used CAR to predict new-onset atrial fibrillation in COVID-19 patients.<a class="elsevierStyleCrossRef" href="#bib0360">35</a> However, none of these studies established a cut-off point, and all were retrospective. Finally, Saylik et al.<a class="elsevierStyleCrossRef" href="#bib0275">18</a> found a CAR cut-off point of 20.75 to predict mortality in COVID-19 patients with hypertension but not without this comorbidity. Our analysis correlating CAR levels to predict in-hospital death showed that compared to COVID-19 patients, in patients without COVID-19, a higher CAR cut-off level was needed to estimate the same probability of in-hospital death. These differences probably reflect the disease characteristics of the included patients, especially the leading cause of admission.<a class="elsevierStyleCrossRefs" href="#bib0235">10,36,37</a> Previously, Oh et al.<a class="elsevierStyleCrossRef" href="#bib0240">11</a> proposed setting CAR reference values considering patient characteristics, as infected patients with sepsis or septic shock have higher CAR values to predict mortality<a class="elsevierStyleCrossRefs" href="#bib0235">10,36</a> compared to the cut-off values in postoperative patients.<a class="elsevierStyleCrossRef" href="#bib0240">11</a> Thus, more studies are needed to reproduce these findings, including identifying differential cut-off points according to the population studied.Strengths and limitationsTo the best of our knowledge, this is the first prospective study describing CAR's performance in predicting mortality among patients with and without COVID-19. Similarly, we propose measuring CAR beyond admission, as it may become more accurate to predict adverse outcomes in a broader population of critically ill patients. Nevertheless, the cut-off values for most studies were calculated on the first day of admission. Thus, more studies are needed to establish an optimal baseline and the most precise time to measure CAR to obtain the best performance.This study is not without limitations. The fluid balances obtained in our study were not accurate throughout the first five admission days. This is because, as a standard practice in our ICU, urinary catheters are removed as soon as they are no longer needed, resulting in inaccurate fluid balance measurements on the fourth and fifth days. Finally, we only measured CAR at ICU admission and on Day 3. Therefore, we could not describe the CAR kinetics from hospital admission or quantify it at other moments of hospitalization, especially in patients not directly admitted to the ICU. More studies are needed to clarify the optimal time for calculating the ratio.ConclusionIn conclusion, CAR predicted in-hospital death and adverse outcomes in patients with and without COVID-19. The diagnostic performance of the ratio was similar for both groups, with lower cut-off points in the COVID-19 population. The dynamic evolution of CAR and its components may be associated with a higher cumulative fluid balance and more significant morbidity and mortality. More studies are needed to establish the optimal cut-off point for critically ill medical patients and the optimal time for calculating the ratio throughout the disease.FundingThis work was financed by the Universidad de Cartagena, Colombia [grant No. 00528-2019]. The sponsor had no role in study design, collecting, analyzing, and interpreting data, or report writing.Authors’ contributionsAll authors contributed to the conceptualization; María Vargas-Hernandez, Miguel Aguilar-Schotborgh and Liseth García-Uribe contributed to data collection; Sandra Contreras-Arrieta and José Rojas-Suarez performed the statistical analysis; José Rojas-Suarez, Liseth García-Uribe, Wilfrido Coronell-Rodriguez, and Carmelo Dueñas-Castell contributed to funding acquisition; María Vargas-Hernandez, Miguel Aguilar-Schotborgh and Liseth García-Uribe contributed to project administration. All authors contributed to the writing, review & editing of the original draft.Conflicts of interestNone declared." "textoCompletoSecciones" => array:1 [ "secciones" => array:15 [ 0 => array:3 [ "identificador" => "xres1952325" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Purpose" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1680597" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "xpalclavsec1680598" "titulo" => "Abbreviations" ] 3 => array:3 [ "identificador" => "xres1952324" "titulo" => "Resumen" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Materiales y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] 4 => array:2 [ "identificador" => "xpalclavsec1680596" "titulo" => "Palabras clave" ] 5 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 6 => array:3 [ "identificador" => "sec0010" "titulo" => "Materials and methods" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Study design" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Sample size" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Data collection" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Statistical analysis" ] ] ] 7 => array:2 [ "identificador" => "sec0035" "titulo" => "Results" ] 8 => array:3 [ "identificador" => "sec0040" "titulo" => "Discussion" "secciones" => array:1 [ 0 => array:2 [ "identificador" => "sec0045" "titulo" => "Strengths and limitations" ] ] ] 9 => array:2 [ "identificador" => "sec0050" "titulo" => "Conclusion" ] 10 => array:2 [ "identificador" => "sec0055" "titulo" => "Funding" ] 11 => array:2 [ "identificador" => "sec0060" "titulo" => "Authors’ contributions" ] 12 => array:2 [ "identificador" => "sec0065" "titulo" => "Conflicts of interest" ] 13 => array:2 [ "identificador" => "xack684261" "titulo" => "Acknowledgements" ] 14 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2023-03-01" "fechaAceptado" => "2023-05-12" "PalabrasClave" => array:2 [ "en" => array:2 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1680597" "palabras" => array:5 [ 0 => "COVID-19" 1 => "Critical care" 2 => "Mortality" 3 => "C-reactive protein" 4 => "Albumin" ] ] 1 => array:4 [ "clase" => "abr" "titulo" => "Abbreviations" "identificador" => "xpalclavsec1680598" "palabras" => array:14 [ 0 => "AUC" 1 => "APACHE II" 2 => "CAR" 3 => "CLI" 4 => "CRP" 5 => "GIPS" 6 => "ICU" 7 => "IMV" 8 => "IQR" 9 => "NPV" 10 => "PPV" 11 => "SE" 12 => "SOFA" 13 => "SP" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1680596" "palabras" => array:5 [ 0 => "COVID-19" 1 => "Cuidado intensivo" 2 => "Mortalidad" 3 => "Proteína C reactiva" 4 => "Albúmina" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "PurposeCompare the performance of the C-reactive protein to albumin ratio (CAR) in predicting in-hospital death between patients with and without COVID-19. Materials and methodsThis prospective cohort study included critically ill patients with and without COVID-19. CAR was obtained at admission and on Day 3, along with fluid balance and other predictive scores. The primary outcome was all-cause in-hospital death, and the Youden index was used to determine CAR cut-off points. ResultsWe included the data of 176 patients; 85 had COVID-19. All-cause in-hospital death was 31.3%. The CAR cut-off points for patients without COVID-19 were 28.5mg/g and 31.6mg/g at admission and on Day 3, respectively, and 13.5mg/g and 7.4mg/g for COVID-19 patients. Day 3 CAR was a better predictor of all-cause in-hospital death than CAR at admission in both groups. Non-survivors in the COVID and non-COVID groups had higher positive fluid balance rates than survivors. ConclusionsThe CAR predicted in-hospital death in both groups, with different cut-off points. CAR on Day 3 had better diagnostic performance than that at admission. Cumulative fluid balance may influence CAR kinetics. An optimal CAR cut-off point for critically ill patients, optimal measuring time, and impact of fluids therapy still need to be established." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Purpose" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "ObjetivoComparar el rendimiento del índice proteína C reactiva-albúmina (CAR) en predicción de mortalidad hospitalaria entre pacientes con y sin COVID-19. Materiales y métodosEstudio de cohorte prospectivo, que incluyó pacientes críticamente enfermos con y sin COVID-19. CAR fue obtenido al ingreso y el día 3, además del balance hídrico y otras escalas predictoras. El desenlace primario fue un compuesto de mortalidad hospitalaria por todas las causas, y se utilizó el índice de Youden para determinar los puntos de corte de CAR. ResultadosSe incluyeron 176 pacientes, 85 con COVID-19. La mortalidad hospitalaria fue del 31,3%. El punto de corte de CAR para pacientes con COVID-19 fue de 28,5 y 31,6mg/g a la admisión y el día 3, respectivamente, y para pacientes sin COVID-19 fue de 13,5 y 7,4mg/g. En ambos grupos, el CAR el día 3 fue mejor predictor de mortalidad hospitalaria que el tomado a la admisión. Los no-sobrevivientes en los grupos de COVID y no-COVID tuvieron mayores niveles de balance de hídricos comparados con los sobrevivientes. ConclusionesEl CAR predijo la mortalidad hospitalaria en ambos grupos, con puntos de corte diferentes. CAR el día 3 tuvo mejor rendimiento pronóstico que el tomado a la admisión. El balance hídrico acumulado pudo haber influido en la cinética del CAR. Aún se necesita establecer un punto de corte óptimo del CAR en pacientes críticamente enfermos, el tiempo de medición óptimo y el impacto de la terapia hídrica." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0025" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0030" "titulo" => "Materiales y métodos" ] 2 => array:2 [ "identificador" => "abst0035" "titulo" => "Resultados" ] 3 => array:2 [ "identificador" => "abst0040" "titulo" => "Conclusiones" ] ] ] ] "apendice" => array:1 [ 0 => array:1 [ "seccion" => array:1 [ 0 => array:4 [ "apendice" => "The following are the supplementary data to this article:<elsevierMultimedia ident="upi0005"></elsevierMultimedia>" "etiqueta" => "Appendix A" "titulo" => "Supplementary data" "identificador" => "sec0075" ] ] ] ] "multimedia" => array:8 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 2158 "Ancho" => 3333 "Tamanyo" => 230477 ] ] "descripcion" => array:1 [ "en" => "Flow chart of participants included in the study." ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Figure 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 2201 "Ancho" => 3341 "Tamanyo" => 496742 ] ] "descripcion" => array:1 [ "en" => "C-reactive protein, albumin, and CRP/albumin ratio on days 1 and 3 in critically ill participants with and without COVID-19. Note: ns: not significant; *p<0.05; **p<0.01; ***p<0.001." ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Figure 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 2020 "Ancho" => 3341 "Tamanyo" => 303014 ] ] "descripcion" => array:1 [ "en" => "Day-by-day comparison of daily fluid intake between survivors and non-survivors. Fluid balance and cumulative fluid for the first five days after intensive care unit (ICU) admission of patients without (a–c) and with (d–f) COVID-19. Note: Solid lines represent non-survivors and dotted lines represent survivors; *p<0.05." ] ] 3 => array:7 [ "identificador" => "fig0020" "etiqueta" => "Figure 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 3454 "Ancho" => 2175 "Tamanyo" => 436691 ] ] "descripcion" => array:1 [ "en" => "Comparison of receiver operating characteristic (ROC) curves of the C-reactive protein to albumin ratio (CAR) on Day 3 for in-hospital mortality by COVID-19 diagnosis." ] ] 4 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:3 [ "leyenda" => "BMI: body mass index; COPD: chronic obstructive pulmonary disease; CKD: chronic kidney disease; APACHE II: Acute Physiology and Chronic Health Disease Classification System II; SOFA: Sequential Organ Failure Assessment; IQR: interquartile range." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Variable \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Totaln=176 (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Survivors(n=121, 68.8%)N (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Non-survivors(n=55, 31.3%)N (%) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age (years), median (IQR) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">62.5 (49, 75.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58 (44, 71) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">72 (60, 80) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Sex \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.728 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Male \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">99 (56.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">54 (44.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23 (41.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Female \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">77 (43.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">67 (55.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">32 (58.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t"> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">BMI (kg/m2), median (IQR) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.5 (24.5, 28.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.6 (24.7, 29.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25.7 (24.1, 27.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.216 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Comorbidities</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Diabetes \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">47 (26.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">29 (24.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (32.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.223 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Chronic arterial hypertension \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">104 (59.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">63 (52.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">41 (74.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.005 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Ischemic cardiopathy \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (7.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (6.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 (9.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.560 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Heart failure \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14 (7.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 (9.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (3.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.153 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">COPD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 (4.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (2.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (7.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.131 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CKD \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">32 (18.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">20 (16.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 (21.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.399 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Stroke \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (5.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 (5.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (5.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.930 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Cancer \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13 (7.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 (9.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (3.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.200 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Obesity \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">35 (19.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">27 (22.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">8 (14.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.231 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Admission diagnosis</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">COVID-19 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">85 (48.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">48 (39.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">37 (67.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Cardiovascular \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">22 (12.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21 (17.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.004 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Non-COVID sepsis \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23 (13.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">14 (11.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 (16.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.382 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Neurological \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">17 (9.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">12 (9.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5 (9.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.863 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Postsurgical<a class="elsevierStyleCrossRef" href="#tblfn0005">a</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">11 (6.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (8.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (1.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.102 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Other<a class="elsevierStyleCrossRef" href="#tblfn0010">b</a> \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18 (10.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">16 (13.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (3.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.052 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">APACHE II, median (IQR) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 (7, 16) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">9 (6, 13) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">15 (10, 19) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">SOFA, median (IQR) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3 (2, 4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 (1, 4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4 (3, 6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Days since hospital admission until ICU admission, median (IQR) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0, 2.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0, 2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 (0, 3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.104 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Antibiotic treatment \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">79 (44.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">38 (31.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">41 (74.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Days since antibiotic treatment until ICU admission \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (−2, 0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (−2, 0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (−2, 0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.549 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3249661.png" ] ] ] "notaPie" => array:2 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "Postsurgical diagnoses included scheduled surgeries, including hip arthroplasty and oncologic surgeries (one gastrointestinal [rectosigmoidoscopy] with lymph node dissection, one hemipelvectomy, one mastectomy, one lung biopsy, one supratentorial tumor, and one meningioma)." ] 1 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "b" "nota" => "Other diagnoses included dialytic urgency (n=7); diabetic ketoacidosis and hyperglycemic hyperosmolar state (n=4); upper gastrointestinal bleeding (n=2); intoxication due to calcium channel blockers (n=1); acute pancreatitis (n=1); acute demyelinating polyneuropathy (n=1); tumor lysis syndrome (n=1); and hemophagocytic syndrome (n=1)." ] ] ] "descripcion" => array:1 [ "en" => "Clinical characteristics of the included patients at ICU admission." ] ] 5 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "IQR: interquartile range; CRP: C-reactive protein; CAR: C-reactive protein/albumin ratio. 1Data available for 159/176 patients; 2Data available for 164/176 patients; 3Data available for 149/176 patients; 4Data available for 123/176 patients; 5Data available for 96/176 patients; 6Data available for 75/176 patients" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Variable \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Totaln=176median (IQR) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Survivors(n=121, 68.8%)median (IQR) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Non-survivors(n=55, 31.3%)median (IQR) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Albumin at admission (g/dL) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.80 (3.4, 4.10) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.8 (3.4, 4.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.5 (3.3, 4.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.031 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Albumin on Day 3, (g/dL)1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.6 (3.3, 3.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.7 (3.4, 3.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.4 (3.1, 3.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.003 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Change in albumin between admission and Day 3 (g/dL)1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.14 (−0.31, 0.04) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.11 (−0.29, 0.08) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">−0.25 (−0.40, −0.04) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.007 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CRP at admission (mg/L) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">24 (12, 96) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">24 (8, 86) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">96 (24, 108) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CRP on Day 3 (mg/L)1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">48 (12, 111) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">24 (8, 96) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">96 (46, 121) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Change in CRP between admission and Day 3 (mg/L)1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (12, 19) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (−18, 12) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 (0, 40) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.017 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR at admission \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.97 (2.99, 27.06) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.85 (2.04, 18.88) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21.88 (5.24–37.96) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR on Day 3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13.00 (3.65–30.69) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.61 (2.11, 26.67) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">29.2 (12.91, 39.63) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="5" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Fluid balance</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Accumulated fluid balance on Day 1 (mL)2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">520 (110–892) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">475 (−210–900) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">610 (250–892) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.070 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Accumulated fluid balance on Day 2 (mL)3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1285 (204–2113) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">800 (−30–1864) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1876 (119–2514) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Accumulated fluid balance on Day 3 (mL)4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1517 (450–2910) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1170 (200–2580) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2311 (1300–3479) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Accumulated fluid balance on Day 4 (mL)5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2345 (1093–3880) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2018 (360–3250) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3198 (2090–4331) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.007 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Accumulated fluid balance on Day 5 (mL)6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3041 (1724–4985) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2679 (1365–4214) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4364 (2845–5166) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.065 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3249659.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Albumin, CPR parameters and fluid balance of participants included in the study." ] ] 6 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "AUC: area under the curve; CAR: C-reactive protein/albumin ratio; NPV: negative predictive value; PPV: positive predictive value; CI: confidence interval." "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Variable \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Cut-off point (mg/g) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Youden index \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">AUC(95% CI) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Sensitivity (95% CI) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Specificity (95% CI) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">PPV(95% CI) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">NPV(95% CI) \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Patients without COVID-19</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">In-hospital mortality</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR at admission \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">28.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.391 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.716(0.581, 0.852) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">55.6(30.8, 78.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">83.6(73.0, 91.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">45.5(24.4, 67.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">88.4(78.4, 94.9) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR on Day 3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">31.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.570 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.811(0.696, 0.925) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">75.0(47.6, 92.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">82.0(70.0, 90.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">52.2(30.6, 73.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92.6(82.1, 97.9) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Vasopressor therapy</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR at admission \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">28.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.433 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.700(0.561, 0.839) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56.5(34.4–76.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">85.5(74.9–92.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">54.2(32.8–74.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">86.8(76.4–93.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR on Day 3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">33.3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.712 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.859(0.747, 0.970) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">80.0(56.3, 94.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">91.2(80.7, 97.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">76.2(52.8, 91.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92.9(82.7, 98.0) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mechanical ventilation</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR at admission \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.322 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.680(0.545, 0.814) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">94.7(74.0, 99.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">37.5(26.4, 49.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">29.0(18.2, 41.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">96.4(81.7, 99.9) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR on Day 3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">33.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.556 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.778(0.622, 0.934) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">71.4(41.9, 91.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">84.1(72.7, 92.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.50(27.2, 72.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">93.0(83.0, 98.1) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Hospital-acquired infection</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR at admission \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">27.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.390 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.738(0.562, 0.914) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">60.0(26.2, 87.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">79.0(68.5, 87.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">26.1(10.2, 48.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">94.1(85.6, 98.4) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR on Day 3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">40.9 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.410 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.710(0.508, 0.911) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">50.0(18.7, 81.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">91.0(81.5, 96.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">45.5(16.7, 76.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">92.4(83.2, 97.5) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Patients with COVID-19</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">In-hospital mortality</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR at admission \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">13.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.261 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.617(0.494, 0.740) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">59.5(42.1, 75.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">66.7(51.6, 79.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57.9 (40.8, 73.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">68.1(52.9, 80.9) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR on Day 3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.377 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.735(0.628, 0.843) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">83.3(67.2, 93.6) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">54.3(39.0, 69.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">58.8(44.2, 72.4) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">80.6(62.5, 92.5) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Vasopressor therapy</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR at admission \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">25.6 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.198 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.613(0.491, 0.734) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">36.8(31.8, 54.0) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">83.0(69.2, 92.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">63.6(40.7, 82.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">61.9(48.8, 73.9) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR on Day 3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.295 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.698(0.584, 0.811) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">78.4(61.8, 90.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">51.1(35.8, 66.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">56.6(42.2, 70.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">74.2(55.4, 88.1) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mechanical ventilation</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR at admission \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">18.7 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.236 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.603(0.479, 0.727) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">47.9(33.3, 62.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">75.7(58.8, 88.2) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">71.9(53.3, 86.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">53.8(39.5, 67.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR on Day 3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.337 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.684(0.567, 0.801) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">76.6(62.0, 87.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">57.1(39.4, 73.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">70.6(56.2, 82.5) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">64.5(45.4, 80.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"></td></tr><tr title="table-row"><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " colspan="8" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Hospital-acquired infection</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR at admission \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">23.4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.169 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.530(0.387, 0.674) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">45.5(24.4, 67.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">71.4(58.7, 82.1) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">35.7(18.6, 55.9) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">78.9(66.1, 88.6) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CAR on Day 3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7.5 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="char" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.161 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.566(0.426, 0.704) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">72.7(50.0, 89.3) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">43.3(30.6, 56.8) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">32.0(19.5, 46.7) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">81.3(63.6, 92.8) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3249660.png" ] ] ] ] "descripcion" => array:1 [ "en" => "CAR cut-off points and diagnostic performance at admission and on Day 3 for interest outcomes in critical care patients without and with COVID-19." ] ] 7 => array:5 [ "identificador" => "upi0005" "tipo" => "MULTIMEDIAECOMPONENTE" "mostrarFloat" => false "mostrarDisplay" => true "Ecomponente" => array:2 [ "fichero" => "mmc1.doc" "ficheroTamanyo" => 6187124 ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:37 [ 0 => array:3 [ "identificador" => "bib0190" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Evaluation of SOFA-based models for predicting mortality in the ICU: a systematic review" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "L. 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"vista" => "all" ] ] ] "idiomaDefecto" => "en" "url" => "/01227262/0000002300000003/v1_202308221623/S0122726223000319/v1_202308221623/en/main.assets" "Apartado" => array:4 [ "identificador" => "39561" "tipo" => "SECCION" "en" => array:2 [ "titulo" => "Originales" "idiomaDefecto" => true ] "idiomaDefecto" => "en" ] "PDF" => "https://static.elsevier.es/multimedia/01227262/0000002300000003/v1_202308221623/S0122726223000319/v1_202308221623/en/main.pdf?idApp=UINPBA00004N&text.app=https://www.elsevier.es/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0122726223000319?idApp=UINPBA00004N"]

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C-reactive protein to albumin ratio in critical care settings

Índice proteína C reactiva-albúmina en escenarios de cuidados intensivos

María Vargas-Hernandeza, Miguel Aguilar-Schotborgha,b,c,

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maguilars3@unicartagena.edu.co

Corresponding author.

, José Rojas-Suareza,b,d,e, Sandra Contreras-Arrietaa,f, Liseth García-Uribea, Wilfrido Coronell-Rodrigueza,b,d, Carmelo Dueñas-Castella,b,d

a Intensive Care and Obstetrics Research Group (GRICIO), Universidad de Cartagena, Cartagena de Indias, Colombia

b Gestión Salud Clinic, Cartagena de Indias, Colombia

c Internal Medicine Department, Pontifica Universidad Javeriana, Bogotá, Colombia

d Intensive Care Department, Universidad de Cartagena, Cartagena de Indias, Colombia

e GINUMED Research Group, Corporación Universitaria Rafael Nuñez, Cartagena de Indias, Colombia

f Anaesthesia Department, Universidad Militar Nueva Granada, Bogotá, Colombia

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          "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Comparison of receiver operating characteristic &#40;ROC&#41; curves of the C-reactive protein to albumin ratio &#40;CAR&#41; on Day 3 for in-hospital mortality by COVID-19 diagnosis&#46;</p>"
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Risk assessment for potential complications and in-hospital mortality is standard practice in critical care settings&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">1&#44;2</span></a> Many predictive models&#44; such as the Acute Physiology and Chronic Health Disease Classification System II &#40;APACHE II&#41;<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">3</span></a> and Sequential Organ Failure Assessment &#40;SOFA&#41;&#44;<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">4</span></a> require numerous clinical and laboratory parameters&#46; This situation may limit their applicability in low- to middle-income countries&#44; especially regarding time and resource requirements&#46;<a class="elsevierStyleCrossRefs" href="#bib0210"><span class="elsevierStyleSup">5&#44;6</span></a> For this&#44; other prognostic markers have emerged to simplify the risk assessment&#44; reflecting the dynamics of the inflammatory response&#46;<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">7</span></a> The C-reactive protein to albumin ratio &#40;CAR&#41;&#44; expressed as a fraction between the measurement units of its components &#40;mg&#47;L or mg&#47;dL&#41; over &#40;g&#47;L or g&#47;dL&#41;&#44;<a class="elsevierStyleCrossRefs" href="#bib0225"><span class="elsevierStyleSup">8&#44;9</span></a> has been used to assess hospital stay and mortality in critically ill septic and nonseptic patients&#46;<a class="elsevierStyleCrossRefs" href="#bib0235"><span class="elsevierStyleSup">10&#44;11</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Moreover&#44; serum C-reactive protein &#40;CRP&#41; and albumin are used separately to predict mortality in critically ill patients&#46;<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">12</span></a> CRP is an acute-phase protein produced in response to infection&#44; ischemia&#44; trauma&#44; and other inflammatory conditions and correlates with inflammation during the early course of illness&#46;<a class="elsevierStyleCrossRefs" href="#bib0250"><span class="elsevierStyleSup">13&#44;14</span></a> In contrast&#44; serum albumin is an acute-phase protein that is inversely correlated with the intensity of the inflammatory response&#46;<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">15</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">Recent retrospective studies involved CAR as a prognostic marker in COVID-19 patients<a class="elsevierStyleCrossRefs" href="#bib0265"><span class="elsevierStyleSup">16&#8211;18</span></a> and found differential CAR values in non-survivors with nonsevere COVID-19 compared to survivors with the same condition&#46;<a class="elsevierStyleCrossRefs" href="#bib0280"><span class="elsevierStyleSup">19&#44;20</span></a> However&#44; no studies have examined the differences in CAR cut-off points between patients with and without COVID-19&#46; Thus&#44; we aimed to assess differences in CAR to predict in-hospital death in a mixed critical care setting&#44; including patients with and without COVID-19&#44; and explore potential cut-off points for predicting adverse outcomes&#44; such as in-hospital death&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Materials and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Study design</span><p id="par0020" class="elsevierStylePara elsevierViewall">We conducted a prospective cohort study in two tertiary reference critical care facilities in Cartagena&#44; Colombia&#46; Adult patients &#40;age &#8805;18 years&#41; with and without COVID-19 admitted to the intensive care unit &#40;ICU&#41; in a critical condition from November 1&#44; 2020&#44; to May 31&#44; 2021&#44; were included&#46; We excluded patients with chronic hepatic illness&#44; malnutrition&#44; or pregnancy due to uncertain effects on albumin levels&#46; Patients who died within 24<span class="elsevierStyleHsp" style=""></span>h of ICU admission were also excluded from the final analysis&#46; Approval was obtained from the ethics committee of Universidad de Cartagena &#40;Act Number&#58; DI-FM-CERT-2906-2021&#41;&#46; The procedures used in this study adhere to the tenets of the Declaration of Helsinki&#46; Informed consent was obtained from all individual participants included in the study&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Sample size</span><p id="par0025" class="elsevierStylePara elsevierViewall">We estimated the frequency of survival in patients with low IFC &#40;cut-off point of &#60;88&#46;55&#41; according to the results of the study published by Palacios et al&#46;<a class="elsevierStyleCrossRef" href="#bib0290"><span class="elsevierStyleSup">21</span></a> Based on this&#44; we calculated a sample size of 156 patients &#40;level of confidence of 95&#37; and alpha error of 5&#37;&#41;&#46; We estimated a lost-to-follow-up rate of 10&#37;&#44; so we included 171 patients in the study&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Data collection</span><p id="par0030" class="elsevierStylePara elsevierViewall">Baseline characteristics&#44; laboratory findings&#44; and disease severity scores&#44; including APACHE II and SOFA scores&#44; were obtained upon admission to the ICU&#46; CRP &#40;mg&#47;dl&#41; and albumin concentrations &#40;g&#47;dl&#41; were obtained at admission and on Day 3&#46; Blood parameters were assessed by an Automated Analyser Mindray BC-240 &#40;Mindray Corporation&#44; Shenzhen&#44; China&#41;&#46; CRP was analyzed using an immunoturbidimetric method&#44; and albumin was assayed using a photoelectric colorimeter&#46; We considered a normal albumin concentration from 3&#46;5 to 5&#46;0<span class="elsevierStyleHsp" style=""></span>g&#47;dL and CRP from 0 to 5<span class="elsevierStyleHsp" style=""></span>mg&#47;L&#46; CAR was calculated by dividing CRP levels by albumin &#40;mg&#47;g&#41;&#46; Fluid balance data were obtained for the first five days after admission to the ICU&#46; The primary outcome was all-cause in-hospital death&#46; Secondary outcomes were required invasive mechanical ventilation &#40;IMV&#41;&#44; vasopressor therapy&#44; presence of hospital-acquired infections&#44; and length of stay&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Statistical analysis</span><p id="par0035" class="elsevierStylePara elsevierViewall">Categorical variables are presented with absolute and relative frequencies&#44; and quantitative variables are presented as medians and interquartile ranges&#46; Bivariate analysis was conducted using a dependent variable classifying in-hospital death as non-survivors using the chi-square test for categorical variables and the Wilcoxon test for quantitative variables&#46; We assessed CAR on days 1 and 3&#44; along with APACHE II and SOFA scores at admission&#44; to predict all-cause in-hospital death&#44; vasopressor requirement&#44; need for mechanical ventilation&#44; and hospital-acquired infection&#46; We used Youden&#39;s index to determine the optimal CAR cut-off point for each outcome for participants with and without COVID-19&#46; To assess the cause of differences between COVID-19 and non-COVID CAR results&#44; we compared albumin and CRP values between the two groups&#46; We compared diagnostic performance between groups using the Mann&#8211;Whitney <span class="elsevierStyleItalic">U</span> test for equality between receiver operating characteristic areas&#46; We also calculated sensitivity &#40;SE&#41;&#44; specificity &#40;SP&#41;&#44; positive predictive value &#40;PPV&#41;&#44; and negative predictive value &#40;NPV&#41; at cut-off points for CAR&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">To avoid possible bias in mortality prediction between COVID-19 patients with and without COVID-19&#44; we independently compared AUC and diagnostic accuracy as a sensitivity analysis&#46; A <span class="elsevierStyleItalic">p</span> value of less than 0&#46;05 was considered statistically significant&#46; All data analyses were performed using Stata 17&#46;0 software &#40;Stata Corp&#44; Texas&#44; USA&#41;&#46;</p></span></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Results</span><p id="par0045" class="elsevierStylePara elsevierViewall">The study cohort included 275 adults admitted to the ICU in the study period&#59; 99 were excluded&#46; The data of 176 patients were included for analysis&#59; 85 patients &#40;48&#46;3&#37;&#41; had COVID-19&#46; CAR was not available for 17 patients on the third day after admission due to early discharge or death &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0050" class="elsevierStylePara elsevierViewall">Baseline characteristics at ICU admission are presented in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#46; The median age was 62&#46;5 years &#40;interquartile range &#91;IQR&#93; 49&#44; 75&#46;5&#41;&#46; The most common diagnosis at admission was COVID-19 infection &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>85&#44; 48&#46;3&#37;&#41;&#46; The median APACHE II and SOFA scores were 10 &#40;IQR 7&#44; 16&#41; and 3 points &#40;IQR 2&#44; 4&#41;&#44; respectively&#46; All-cause in-hospital death was 31&#46;3&#37; &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>55&#41;&#46; Non-survivors were older &#40;72 years vs&#46; 58 years&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41; and had a higher frequency of chronic hypertension &#40;74&#46;5&#37; vs&#46; 52&#46;1&#37;&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;005&#41;&#46; At admission&#44; non-survivors had a higher frequency of COVID-19 infection &#40;67&#46;3&#37; vs&#46; 39&#46;7&#37;&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41; and a lower proportion of cardiovascular diagnosis &#40;1&#46;8&#37; vs&#46; 17&#46;4&#37;&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;004&#41;&#46; The APACHE II and SOFA scores were higher among non-survivors &#40;15 points vs&#46; 9 points&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#44; and 4 points vs&#46; 2 points&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#44; respectively&#41;&#46; Non-survivors had lower albumin levels &#40;3&#46;5<span class="elsevierStyleHsp" style=""></span>g&#47;dL vs&#46; 3&#46;8<span class="elsevierStyleHsp" style=""></span>g&#47;dL&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;0031&#41;&#44; higher CRP levels &#40;96<span class="elsevierStyleHsp" style=""></span>mg&#47;dL vs&#46; 24<span class="elsevierStyleHsp" style=""></span>mg&#47;dL&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#8804;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41;&#44; and higher CAR at admission &#40;21&#46;8 vs&#46; 5&#46;8&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#8804;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41; and on Day 3 &#40;29&#46;2 vs&#46; 6&#46;6&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#8804;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41; &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46; We assessed variation in albumin and CRP levels on Day 3 compared to that at admission to evaluate CAR dynamics&#46; CRP at admission was higher in COVID-19 patients &#40;48<span class="elsevierStyleHsp" style=""></span>mg&#47;dL vs&#46; 12<span class="elsevierStyleHsp" style=""></span>mg&#47;dL&#44; respectively&#41;&#46; We did not find significant changes between groups in CRP and albumin levels from Day 1 to 3&#44; even when comparing patients with and without COVID-19 &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0055" class="elsevierStylePara elsevierViewall">We described the vital signs and laboratory tests to calculate the APACHE II and SOFA scores at ICU admission in <a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Table 1</a>&#46; In <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#44; we present the analysis of CRP&#44; albumin&#44; and CAR based on COVID-19 diagnosis and survival status&#46; We found that non-survivors had a significant decline in albumin on Day 3 &#40;&#8722;0&#46;25<span class="elsevierStyleHsp" style=""></span>g&#47;dL vs&#46; &#8722;0&#46;11<span class="elsevierStyleHsp" style=""></span>g&#47;dL&#44; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;007&#41; and a significant increase in CRP levels &#40;40<span class="elsevierStyleHsp" style=""></span>mg&#47;dL vs&#46; 12<span class="elsevierStyleHsp" style=""></span>mg&#47;dL&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;017&#41; compared with survivors&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0060" class="elsevierStylePara elsevierViewall">Among participants without COVID-19&#44; on Day 3&#44; CRP levels were higher &#40;median 116&#46;5 IQR 70&#44; 157&#46;5 vs&#46; 23&#46;1 IQR 6&#44; 91&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41;&#44; and albumin levels were lower &#40;median 2&#46;9 IQR 2&#46;8&#44; 1&#46;4 vs&#46; 3&#46;6 IQR 3&#46;3&#44; 3&#46;9&#44; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41; in non-survivors compared to survivors&#46; In the COVID-19 group&#44; CRP on Day 3 was higher in non-survivors than survivors &#40;median 96 IQR 38&#46;7&#44; 117&#46;5 vs&#46; 24 IQR 12&#44; 96&#41;&#46; We did not find any differences in albumin between survivors and non-survivors in the COVID-19 group&#46;</p><p id="par0065" class="elsevierStylePara elsevierViewall">Fluid balances were available for 164 &#40;93&#37;&#41; patients in the first 24<span class="elsevierStyleHsp" style=""></span>h&#44; 149 &#40;84&#37;&#41; on Day 2&#44; 123 &#40;69&#37;&#41; on Day 3&#44; 96 &#40;54&#37;&#41; on Day 4&#44; and 75 &#40;42&#37;&#41; on Day 5&#46; The differences in fluid balance data availability were explained due to death or transfer to another hospital unit &#40;e&#46;g&#46;&#44; general ward&#41;&#46; The comparison of fluid balances between survivors and non-survivors is also shown in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#46; Nonsurvivors had higher rates of positive fluid balance throughout follow-up&#44; with differences on Days 2&#8211;4 &#40;Day 2&#58; 1876<span class="elsevierStyleHsp" style=""></span>mL vs&#46; 800<span class="elsevierStyleHsp" style=""></span>mL&#44; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; Day 3&#58; 2311<span class="elsevierStyleHsp" style=""></span>mL vs&#46; 1170<span class="elsevierStyleHsp" style=""></span>mL&#44; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#59; and Day 4&#58; 3198<span class="elsevierStyleHsp" style=""></span>mL vs&#46; 2018<span class="elsevierStyleHsp" style=""></span>mL&#44; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;007&#41;&#46; When assessing differences in fluid balance between participants with and without COVID-19&#44; we found the daily fluid balance to be higher&#44; and cumulative fluid balance increased over time &#40;on Days 2&#8211;4&#41; for non-survivors in the non-COVID group&#46; In the COVID-19 group&#44; differences in fluid balances were only observed on Day 2&#59; this difference was not maintained over time &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a>&#41;&#46;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0070" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Table 2</a> describes different reported outcomes&#46; Vasopressor therapy was required in 34&#46;7&#37; &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>61&#41; of patients&#44; 12&#46;5&#37; &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>22&#41; required more than one vasopressor&#44; 31&#46;2&#37; &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>55&#41; required IMV&#44; and 18&#46;2&#37; &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>32&#41; acquired hospital infections&#46; The median ICU length was 10 days &#40;IQR 4&#46;5&#44; 16&#41;&#46; Non-survivors required vasopressor therapy &#40;46&#37; vs&#46; 15&#37;&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41;&#44; inotropic therapy &#40;6&#37; vs&#46; 1&#37;&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;002&#41;&#44; and IMV &#40;47&#37; vs&#46; 20&#37;&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41; more frequently and had higher rates of hospital-acquired infections &#40;18&#37; vs&#46; 14&#37;&#59; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#41; than survivors&#46; The length of ICU stay among groups did not differ&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> shows the diagnostic performance of CAR at admission and on Day 3 for in-hospital death&#44; including the cut-off point analysis for patients with and without COVID-19&#46; In non-COVID-19 patients&#44; CAR cut-off points were 28&#46;5<span class="elsevierStyleHsp" style=""></span>mg&#47;g and 31&#46;6<span class="elsevierStyleHsp" style=""></span>mg&#47;g at admission and on Day 3&#44; respectively&#46; For COVID-19 patients&#44; CAR cut-off points were lower&#58; 13&#46;5<span class="elsevierStyleHsp" style=""></span>mg&#47;g at admission and 7&#46;4<span class="elsevierStyleHsp" style=""></span>mg&#47;g on Day 3&#46; Compared to CAR at admission&#44; CAR on Day 3 was better&#44; with a higher area under the curve &#40;AUC&#41; for all-cause in-hospital death in the non-COVID and COVID-19 groups &#40;0&#46;81 &#91;0&#46;69&#8211;0&#46;92&#93; vs&#46; 0&#46;71 &#91;0&#46;58&#8211;0&#46;85&#93;&#59; and 0&#46;73 &#91;0&#46;62&#8211;0&#46;84&#93; vs&#46; 0&#46;61&#91;0&#46;49&#8211;0&#46;740&#93;&#41;&#44; respectively&#46; CAR on Day 3 had higher sensitivity in both groups compared to CAR at admission &#40;non-COVID 75&#46;0&#37; vs&#46; 55&#46;6&#37;&#59; and COVID-1983&#46;3&#37; vs&#46; 59&#46;5&#37;&#41;&#46; When we compared the AUC of CAR Day 3 according to COVID-19 status&#44; even though a higher AUC was observed in the non-COVID group&#44; no significant differences were found compared to the AUC in the COVID-19 group &#40;AUC 0&#46;81 vs&#46; 0&#46;73&#44; respectively&#44; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;347&#59; <a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia><p id="par0080" class="elsevierStylePara elsevierViewall">When comparing the diagnostic performance of CAR on Day 3 with CAR at admission&#44; APACHE II&#44; and SOFA scores&#44; we found no differences between CAR on Day 3 and APACHE or SOFA scores in the COVID-19 and non-COVID groups &#40;<a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Figure 1</a>&#41;&#46; The AUC for CAR at admission and on Day 3 for vasopressor requirement&#44; mechanical ventilation&#44; and hospital-acquired infection in the COVID-19 and non-COVID groups are shown in <a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Figure 2</a>&#46;</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Discussion</span><p id="par0085" class="elsevierStylePara elsevierViewall">Our results showed that CAR is a prognostic factor in critical care settings independent of COVID-19 status&#46; We found CAR measured at admission and on Day 3 to predict mortality in critically ill patients&#44; both with and without COVID-19&#46; However&#44; the kinetics of the ratio and its components differed between groups&#44; with lower CAR cut-off points on Day 3 for predicting adverse outcomes&#44; especially in-hospital death&#44; for COVID-19 participants &#40;7&#46;4<span class="elsevierStyleHsp" style=""></span>mg&#47;g vs&#46; 31&#46;6<span class="elsevierStyleHsp" style=""></span>mg&#47;g&#44; respectively&#41;&#46; Additionally&#44; we found a percentage decrease in serum albumin &#40;delta of albumin&#41; and CAR changes associated with higher fluid balance&#44; a greater need for vasopressors and IMV&#44; and higher mortality in critically ill patients with and without COVID-19&#46;</p><p id="par0090" class="elsevierStylePara elsevierViewall">CAR is a recognized predictor of adverse clinical outcomes in various clinical settings&#46;<a class="elsevierStyleCrossRef" href="#bib0295"><span class="elsevierStyleSup">22</span></a> Before COVID-19&#44; Findikli et al&#46;<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">23</span></a> found CAR at admission to be an independent predictor of 60-day mortality in critically ill medical patients&#46; In our study&#44; CAR at admission&#44; and more significantly&#44; on Day 3&#44; predicted mortality&#46; This biphasic presentation could reflect the expected changes reported in COVID-19&#46;<a class="elsevierStyleCrossRef" href="#bib0305"><span class="elsevierStyleSup">24</span></a> Regardless&#44; in the non-COVID population&#44; Cuthbertson<a class="elsevierStyleCrossRef" href="#bib0310"><span class="elsevierStyleSup">25</span></a> introduced the concept of a dual metabolic response in critically ill patients in 1942&#44; which promoted fluid resuscitation in the first three days&#46; Twenty years ago&#44; Malbrain<a class="elsevierStyleCrossRef" href="#bib0315"><span class="elsevierStyleSup">26</span></a> referred to the CRP to albumin ratio as the capillary leak index &#40;CLI&#41;&#46; Thirteen years later&#44; he reported a correlation between CLI&#44; fluid balance with the presentation of organ dysfunction&#44; and poor prognosis&#44; introducing the concept of globally increased permeability syndrome &#40;GIPS&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0320"><span class="elsevierStyleSup">27</span></a></p><p id="par0095" class="elsevierStylePara elsevierViewall">In 2012&#44; Cordemans et al&#46;<a class="elsevierStyleCrossRef" href="#bib0325"><span class="elsevierStyleSup">28</span></a> reported a biphasic course of CLI&#44; with a maximum value on the third day&#46; In this retrospective study&#44; CLI was related to positive fluid balance&#44; high extravascular pulmonary water&#44; multiple organ failure&#44; and mortality&#46; Thus&#44; GIPS would represent a &#8220;third hit&#8221; of shock following acute injury and multiorgan dysfunction syndrome&#44; affected by the amounts of fluid administered and&#47;or the fluid balance on the third day&#44; variables associated with higher mortality in the literature&#46; However&#44; these findings need to be confirmed by additional studies&#46; Nevertheless&#44; our results suggest that CAR measurements should continue beyond admission&#44; especially on the third day&#44; as this parameter may be more accurate in predicting mortality&#46; One possible explanation for this finding is that the ratio correlated with inflammation dynamics&#44; irrespective of the etiology of the initial injury&#46; In addition&#44; the association of CAR and fluid balance on the third day with the highest mortality supports the importance of timely interventions to prevent fluid overload&#44; reducing the probability of GIPS and mortality&#46; Furthermore&#44; CAR could provide a standard of care for predicting response to initial resuscitation&#44; maintaining adequate fluid balance&#44; and avoiding GIPS&#44; multiorgan failure&#44; and death&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">CAR components &#40;CRP and albumin&#41; had differential performances among the observed groups&#46; For instance&#44; in our non-survivor group of patients without COVID-19&#44; their dynamics could reflect the level of inflammation related to a critical condition&#46; In this group&#44; albumin significantly decreased on the third day&#44; demarcating a difference between COVID-19 and non-COVID populations&#46; Previous studies linked hypoalbuminemia with poor outcomes in non-COVID-19<a class="elsevierStyleCrossRefs" href="#bib0305"><span class="elsevierStyleSup">24&#44;29</span></a> and COVID-19<a class="elsevierStyleCrossRef" href="#bib0335"><span class="elsevierStyleSup">30</span></a> populations&#46; Nevertheless&#44; hypoalbuminemia seems to be a marker of severe COVID-19 infection related to hyperinflammation&#46;<a class="elsevierStyleCrossRef" href="#bib0340"><span class="elsevierStyleSup">31</span></a> Based on our results&#44; albumin kinetics seem more critical to assess in patients without COVID-19&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">The comparison between CAR at admission and Day 3 with SOFA and APACHE II scores showed no significant differences in the observed AUC&#46; These results suggest that CAR performs as well as established severity scores to predict mortality&#46; Moreover&#44; CAR offers an additional advantage&#44; as it requires only two laboratory tests&#44; is easily obtained in the emergency room&#44; general ward&#44; or critical care settings and is easier to calculate&#46; Therefore&#44; CAR may be time-saving and&#44; if standardized&#44; its interpretation could be as valuable as other widely used measures&#46;</p><p id="par0110" class="elsevierStylePara elsevierViewall">When assessing COVID-19 status&#44; this condition is an independent predictor of mortality&#46; Previous studies have shown increased mortality in critically ill COVID-19 patients with a marked hyperinflammatory condition&#46; Considering the influence of COVID-19 infection in interpreting widely used tools in critical care&#44; we also assessed CAR differences between patients with and without COVID-19&#46; In our study&#44; CAR performance was similar between the two groups&#46; Several studies have evaluated CAR performance in the COVID-19 setting&#59; Bannaga<a class="elsevierStyleCrossRef" href="#bib0345"><span class="elsevierStyleSup">32</span></a> evaluated CRP and albumin independently&#44; not as a ratio&#44; and a recent meta-analysis showed that elevated CRP and hypoalbuminemia were independent predictors of mortality in COVID-19 patients&#46;<a class="elsevierStyleCrossRef" href="#bib0350"><span class="elsevierStyleSup">33</span></a> Karakoyun et al&#46;&#44; <a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">16</span></a> G&#252;ney et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0285"><span class="elsevierStyleSup">20</span></a> Kalabin et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0355"><span class="elsevierStyleSup">34</span></a> and Xue et al&#46;<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">35</span></a> used CAR to discriminate between severe and nonsevere COVID-19 patients and assessed the ratio to predict mortality&#46; In addition&#44; Kelesoglu et al&#46; used CAR to predict new-onset atrial fibrillation in COVID-19 patients&#46;<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">35</span></a> However&#44; none of these studies established a cut-off point&#44; and all were retrospective&#46; Finally&#44; Saylik et al&#46;<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">18</span></a> found a CAR cut-off point of 20&#46;75 to predict mortality in COVID-19 patients with hypertension but not without this comorbidity&#46; Our analysis correlating CAR levels to predict in-hospital death showed that compared to COVID-19 patients&#44; in patients without COVID-19&#44; a higher CAR cut-off level was needed to estimate the same probability of in-hospital death&#46; These differences probably reflect the disease characteristics of the included patients&#44; especially the leading cause of admission&#46;<a class="elsevierStyleCrossRefs" href="#bib0235"><span class="elsevierStyleSup">10&#44;36&#44;37</span></a> Previously&#44; Oh et al&#46;<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">11</span></a> proposed setting CAR reference values considering patient characteristics&#44; as infected patients with sepsis or septic shock have higher CAR values to predict mortality<a class="elsevierStyleCrossRefs" href="#bib0235"><span class="elsevierStyleSup">10&#44;36</span></a> compared to the cut-off values in postoperative patients&#46;<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">11</span></a> Thus&#44; more studies are needed to reproduce these findings&#44; including identifying differential cut-off points according to the population studied&#46;</p><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Strengths and limitations</span><p id="par0115" class="elsevierStylePara elsevierViewall">To the best of our knowledge&#44; this is the first prospective study describing CAR&#39;s performance in predicting mortality among patients with and without COVID-19&#46; Similarly&#44; we propose measuring CAR beyond admission&#44; as it may become more accurate to predict adverse outcomes in a broader population of critically ill patients&#46; Nevertheless&#44; the cut-off values for most studies were calculated on the first day of admission&#46; Thus&#44; more studies are needed to establish an optimal baseline and the most precise time to measure CAR to obtain the best performance&#46;</p><p id="par0120" class="elsevierStylePara elsevierViewall">This study is not without limitations&#46; The fluid balances obtained in our study were not accurate throughout the first five admission days&#46; This is because&#44; as a standard practice in our ICU&#44; urinary catheters are removed as soon as they are no longer needed&#44; resulting in inaccurate fluid balance measurements on the fourth and fifth days&#46; Finally&#44; we only measured CAR at ICU admission and on Day 3&#46; Therefore&#44; we could not describe the CAR kinetics from hospital admission or quantify it at other moments of hospitalization&#44; especially in patients not directly admitted to the ICU&#46; More studies are needed to clarify the optimal time for calculating the ratio&#46;</p></span></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Conclusion</span><p id="par0125" class="elsevierStylePara elsevierViewall">In conclusion&#44; CAR predicted in-hospital death and adverse outcomes in patients with and without COVID-19&#46; The diagnostic performance of the ratio was similar for both groups&#44; with lower cut-off points in the COVID-19 population&#46; The dynamic evolution of CAR and its components may be associated with a higher cumulative fluid balance and more significant morbidity and mortality&#46; More studies are needed to establish the optimal cut-off point for critically ill medical patients and the optimal time for calculating the ratio throughout the disease&#46;</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Funding</span><p id="par0130" class="elsevierStylePara elsevierViewall">This work was financed by the <span class="elsevierStyleGrantSponsor" id="gs1">Universidad de Cartagena&#44; Colombia</span> &#91;grant No&#46; <span class="elsevierStyleGrantNumber" refid="gs1">00528-2019</span>&#93;&#46; The sponsor had no role in study design&#44; collecting&#44; analyzing&#44; and interpreting data&#44; or report writing&#46;</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Authors&#8217; contributions</span><p id="par0135" class="elsevierStylePara elsevierViewall">All authors contributed to the conceptualization&#59; Mar&#237;a Vargas-Hernandez&#44; Miguel Aguilar-Schotborgh and Liseth Garc&#237;a-Uribe contributed to data collection&#59; Sandra Contreras-Arrieta and Jos&#233; Rojas-Suarez performed the statistical analysis&#59; Jos&#233; Rojas-Suarez&#44; Liseth Garc&#237;a-Uribe&#44; Wilfrido Coronell-Rodriguez&#44; and Carmelo Due&#241;as-Castell contributed to funding acquisition&#59; Mar&#237;a Vargas-Hernandez&#44; Miguel Aguilar-Schotborgh and Liseth Garc&#237;a-Uribe contributed to project administration&#46; All authors contributed to the writing&#44; review &#38; editing of the original draft&#46;</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Conflicts of interest</span><p id="par0140" class="elsevierStylePara elsevierViewall">None declared&#46;</p></span></span>"
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              "titulo" => "Conclusiones"
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        4 => array:2 [
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          "titulo" => "Palabras clave"
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          "titulo" => "Introduction"
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        6 => array:3 [
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          "titulo" => "Materials and methods"
          "secciones" => array:4 [
            0 => array:2 [
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              "titulo" => "Study design"
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            1 => array:2 [
              "identificador" => "sec0020"
              "titulo" => "Sample size"
            ]
            2 => array:2 [
              "identificador" => "sec0025"
              "titulo" => "Data collection"
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            3 => array:2 [
              "identificador" => "sec0030"
              "titulo" => "Statistical analysis"
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          "titulo" => "Results"
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          "titulo" => "Discussion"
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              "titulo" => "Strengths and limitations"
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          "titulo" => "Conclusion"
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          "titulo" => "Funding"
        ]
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          "identificador" => "sec0060"
          "titulo" => "Authors&#8217; contributions"
        ]
        12 => array:2 [
          "identificador" => "sec0065"
          "titulo" => "Conflicts of interest"
        ]
        13 => array:2 [
          "identificador" => "xack684261"
          "titulo" => "Acknowledgements"
        ]
        14 => array:1 [
          "titulo" => "References"
        ]
      ]
    ]
    "pdfFichero" => "main.pdf"
    "tienePdf" => true
    "fechaRecibido" => "2023-03-01"
    "fechaAceptado" => "2023-05-12"
    "PalabrasClave" => array:2 [
      "en" => array:2 [
        0 => array:4 [
          "clase" => "keyword"
          "titulo" => "Keywords"
          "identificador" => "xpalclavsec1680597"
          "palabras" => array:5 [
            0 => "COVID-19"
            1 => "Critical care"
            2 => "Mortality"
            3 => "C-reactive protein"
            4 => "Albumin"
          ]
        ]
        1 => array:4 [
          "clase" => "abr"
          "titulo" => "Abbreviations"
          "identificador" => "xpalclavsec1680598"
          "palabras" => array:14 [
            0 => "AUC"
            1 => "APACHE II"
            2 => "CAR"
            3 => "CLI"
            4 => "CRP"
            5 => "GIPS"
            6 => "ICU"
            7 => "IMV"
            8 => "IQR"
            9 => "NPV"
            10 => "PPV"
            11 => "SE"
            12 => "SOFA"
            13 => "SP"
          ]
        ]
      ]
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        0 => array:4 [
          "clase" => "keyword"
          "titulo" => "Palabras clave"
          "identificador" => "xpalclavsec1680596"
          "palabras" => array:5 [
            0 => "COVID-19"
            1 => "Cuidado intensivo"
            2 => "Mortalidad"
            3 => "Prote&#237;na C reactiva"
            4 => "Alb&#250;mina"
          ]
        ]
      ]
    ]
    "tieneResumen" => true
    "resumen" => array:2 [
      "en" => array:3 [
        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Purpose</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Compare the performance of the C-reactive protein to albumin ratio &#40;CAR&#41; in predicting in-hospital death between patients with and without COVID-19&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Materials and methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">This prospective cohort study included critically ill patients with and without COVID-19&#46; CAR was obtained at admission and on Day 3&#44; along with fluid balance and other predictive scores&#46; The primary outcome was all-cause in-hospital death&#44; and the Youden index was used to determine CAR cut-off points&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">We included the data of 176 patients&#59; 85 had COVID-19&#46; All-cause in-hospital death was 31&#46;3&#37;&#46; The CAR cut-off points for patients without COVID-19 were 28&#46;5<span class="elsevierStyleHsp" style=""></span>mg&#47;g and 31&#46;6<span class="elsevierStyleHsp" style=""></span>mg&#47;g at admission and on Day 3&#44; respectively&#44; and 13&#46;5<span class="elsevierStyleHsp" style=""></span>mg&#47;g and 7&#46;4<span class="elsevierStyleHsp" style=""></span>mg&#47;g for COVID-19 patients&#46; Day 3 CAR was a better predictor of all-cause in-hospital death than CAR at admission in both groups&#46; Non-survivors in the COVID and non-COVID groups had higher positive fluid balance rates than survivors&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">The CAR predicted in-hospital death in both groups&#44; with different cut-off points&#46; CAR on Day 3 had better diagnostic performance than that at admission&#46; Cumulative fluid balance may influence CAR kinetics&#46; An optimal CAR cut-off point for critically ill patients&#44; optimal measuring time&#44; and impact of fluids therapy still need to be established&#46;</p></span>"
        "secciones" => array:4 [
          0 => array:2 [
            "identificador" => "abst0005"
            "titulo" => "Purpose"
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          1 => array:2 [
            "identificador" => "abst0010"
            "titulo" => "Materials and methods"
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          2 => array:2 [
            "identificador" => "abst0015"
            "titulo" => "Results"
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          3 => array:2 [
            "identificador" => "abst0020"
            "titulo" => "Conclusions"
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      "es" => array:3 [
        "titulo" => "Resumen"
        "resumen" => "<span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Objetivo</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Comparar el rendimiento del &#237;ndice prote&#237;na C reactiva-alb&#250;mina &#40;CAR&#41; en predicci&#243;n de mortalidad hospitalaria entre pacientes con y sin COVID-19&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Materiales y m&#233;todos</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Estudio de cohorte prospectivo&#44; que incluy&#243; pacientes cr&#237;ticamente enfermos con y sin COVID-19&#46; CAR fue obtenido al ingreso y el d&#237;a 3&#44; adem&#225;s del balance h&#237;drico y otras escalas predictoras&#46; El desenlace primario fue un compuesto de mortalidad hospitalaria por todas las causas&#44; y se utiliz&#243; el &#237;ndice de Youden para determinar los puntos de corte de CAR&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Se incluyeron 176 pacientes&#44; 85 con COVID-19&#46; La mortalidad hospitalaria fue del 31&#44;3&#37;&#46; El punto de corte de CAR para pacientes con COVID-19 fue de 28&#44;5 y 31&#44;6<span class="elsevierStyleHsp" style=""></span>mg&#47;g a la admisi&#243;n y el d&#237;a 3&#44; respectivamente&#44; y para pacientes sin COVID-19 fue de 13&#44;5 y 7&#44;4<span class="elsevierStyleHsp" style=""></span>mg&#47;g&#46; En ambos grupos&#44; el CAR el d&#237;a 3 fue mejor predictor de mortalidad hospitalaria que el tomado a la admisi&#243;n&#46; Los no-sobrevivientes en los grupos de COVID y no-COVID tuvieron mayores niveles de balance de h&#237;dricos comparados con los sobrevivientes&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">El CAR predijo la mortalidad hospitalaria en ambos grupos&#44; con puntos de corte diferentes&#46; CAR el d&#237;a 3 tuvo mejor rendimiento pron&#243;stico que el tomado a la admisi&#243;n&#46; El balance h&#237;drico acumulado pudo haber influido en la cin&#233;tica del CAR&#46; A&#250;n se necesita establecer un punto de corte &#243;ptimo del CAR en pacientes cr&#237;ticamente enfermos&#44; el tiempo de medici&#243;n &#243;ptimo y el impacto de la terapia h&#237;drica&#46;</p></span>"
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            "titulo" => "Objetivo"
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            "titulo" => "Materiales y m&#233;todos"
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            "identificador" => "abst0035"
            "titulo" => "Resultados"
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            "identificador" => "abst0040"
            "titulo" => "Conclusiones"
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            "apendice" => "<p id="par0155" class="elsevierStylePara elsevierViewall">The following are the supplementary data to this article&#58;<elsevierMultimedia ident="upi0005"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix A"
            "titulo" => "Supplementary data"
            "identificador" => "sec0075"
          ]
        ]
      ]
    ]
    "multimedia" => array:8 [
      0 => array:7 [
        "identificador" => "fig0005"
        "etiqueta" => "Figure 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr1.jpeg"
            "Alto" => 2158
            "Ancho" => 3333
            "Tamanyo" => 230477
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Flow chart of participants included in the study&#46;</p>"
        ]
      ]
      1 => array:7 [
        "identificador" => "fig0010"
        "etiqueta" => "Figure 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
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            "Tamanyo" => 496742
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        "descripcion" => array:1 [
          "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">C-reactive protein&#44; albumin&#44; and CRP&#47;albumin ratio on days 1 and 3 in critically ill participants with and without COVID-19&#46; Note&#58; ns&#58; not significant&#59; &#42;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#59; &#42;&#42;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;01&#59; &#42;&#42;&#42;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;001&#46;</p>"
        ]
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        "identificador" => "fig0015"
        "etiqueta" => "Figure 3"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr3.jpeg"
            "Alto" => 2020
            "Ancho" => 3341
            "Tamanyo" => 303014
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Day-by-day comparison of daily fluid intake between survivors and non-survivors&#46; Fluid balance and cumulative fluid for the first five days after intensive care unit &#40;ICU&#41; admission of patients without &#40;a&#8211;c&#41; and with &#40;d&#8211;f&#41; COVID-19&#46; Note&#58; Solid lines represent non-survivors and dotted lines represent survivors&#59; &#42;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#46;</p>"
        ]
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        "identificador" => "fig0020"
        "etiqueta" => "Figure 4"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr4.jpeg"
            "Alto" => 3454
            "Ancho" => 2175
            "Tamanyo" => 436691
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Comparison of receiver operating characteristic &#40;ROC&#41; curves of the C-reactive protein to albumin ratio &#40;CAR&#41; on Day 3 for in-hospital mortality by COVID-19 diagnosis&#46;</p>"
        ]
      ]
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            "identificador" => "at1"
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          "leyenda" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">BMI&#58; body mass index&#59; COPD&#58; chronic obstructive pulmonary disease&#59; CKD&#58; chronic kidney disease&#59; APACHE II&#58; Acute Physiology and Chronic Health Disease Classification System II&#59; SOFA&#58; Sequential Organ Failure Assessment&#59; IQR&#58; interquartile range&#46;</p>"
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Variable&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Total<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>176 &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
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                  \t\t\t\t\ttable-head\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span> value&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">62&#46;5 &#40;49&#44; 75&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">58 &#40;44&#44; 71&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">72 &#40;60&#44; 80&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleItalic">Sex</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;728&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Male&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">99 &#40;56&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">54 &#40;44&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">23 &#40;41&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Female&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">77 &#40;43&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">67 &#40;55&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">32 &#40;58&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
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                  \t\t\t\t"><span class="elsevierStyleItalic">BMI &#40;kg&#47;m</span><span class="elsevierStyleSup"><span class="elsevierStyleItalic">2</span></span><span class="elsevierStyleItalic">&#41;&#44; median &#40;IQR&#41;</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">26&#46;5 &#40;24&#46;5&#44; 28&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">26&#46;6 &#40;24&#46;7&#44; 29&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">25&#46;7 &#40;24&#46;1&#44; 27&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;216&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " colspan="5" align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleItalic">Comorbidities</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Diabetes&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">47 &#40;26&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">29 &#40;24&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">18 &#40;32&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;223&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Chronic arterial hypertension&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">63 &#40;52&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">41 &#40;74&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;005&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Ischemic cardiopathy&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">13 &#40;7&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">8 &#40;6&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">5 &#40;9&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;560&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Heart failure&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">14 &#40;7&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">12 &#40;9&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2 &#40;3&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;153&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>COPD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">7 &#40;4&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">3 &#40;2&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">4 &#40;7&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;131&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CKD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">32 &#40;18&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">20 &#40;16&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">12 &#40;21&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;399&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Stroke&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">10 &#40;5&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">7 &#40;5&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">3 &#40;5&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;930&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Cancer&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">13 &#40;7&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">11 &#40;9&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2 &#40;3&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;200&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Obesity&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">35 &#40;19&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">27 &#40;22&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">8 &#40;14&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;231&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="5" align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="5" align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleItalic">Admission diagnosis</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>COVID-19&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">85 &#40;48&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">48 &#40;39&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">37 &#40;67&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Cardiovascular&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">22 &#40;12&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">21 &#40;17&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">1 &#40;1&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">0&#46;004&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Non-COVID sepsis&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Accumulated fluid balance on Day 3 &#40;mL&#41;<span class="elsevierStyleSup">4</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">1517 &#40;450&#8211;2910&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Accumulated fluid balance on Day 4 &#40;mL&#41;<span class="elsevierStyleSup">5</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">2345 &#40;1093&#8211;3880&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2018 &#40;360&#8211;3250&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Accumulated fluid balance on Day 5 &#40;mL&#41;<span class="elsevierStyleSup">6</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">3041 &#40;1724&#8211;4985&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2679 &#40;1365&#8211;4214&#41;&nbsp;\t\t\t\t\t\t\n
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          "leyenda" => "<p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">AUC&#58; area under the curve&#59; CAR&#58; C-reactive protein&#47;albumin ratio&#59; NPV&#58; negative predictive value&#59; PPV&#58; positive predictive value&#59; CI&#58; confidence interval&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
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                  \t\t\t\t\ttable-head\n
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                  \t\t\t\t">92&#46;6&#40;82&#46;1&#44; 97&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR at admission&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR on Day 3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">80&#46;0&#40;56&#46;3&#44; 94&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">91&#46;2&#40;80&#46;7&#44; 97&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">76&#46;2&#40;52&#46;8&#44; 91&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">92&#46;9&#40;82&#46;7&#44; 98&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " colspan="8" align="left" valign="\n
                  \t\t\t\t\ttop\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR at admission&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">94&#46;7&#40;74&#46;0&#44; 99&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">37&#46;5&#40;26&#46;4&#44; 49&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">29&#46;0&#40;18&#46;2&#44; 41&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">96&#46;4&#40;81&#46;7&#44; 99&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR on Day 3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">33&#46;6&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">71&#46;4&#40;41&#46;9&#44; 91&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">84&#46;1&#40;72&#46;7&#44; 92&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">93&#46;0&#40;83&#46;0&#44; 98&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
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                  \t\t\t\t  " colspan="8" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Hospital-acquired infection</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR at admission&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">94&#46;1&#40;85&#46;6&#44; 98&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR on Day 3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " colspan="8" align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleBold">Patients with COVID-19</span></td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " colspan="8" align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleItalic">In-hospital mortality</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR at admission&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">66&#46;7&#40;51&#46;6&#44; 79&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">68&#46;1&#40;52&#46;9&#44; 80&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR on Day 3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">7&#46;4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;377&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;735&#40;0&#46;628&#44; 0&#46;843&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">83&#46;3&#40;67&#46;2&#44; 93&#46;6&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">54&#46;3&#40;39&#46;0&#44; 69&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">58&#46;8&#40;44&#46;2&#44; 72&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">80&#46;6&#40;62&#46;5&#44; 92&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:1.0px"></span></td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " colspan="8" align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleItalic">Vasopressor therapy</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR at admission&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;613&#40;0&#46;491&#44; 0&#46;734&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">36&#46;8&#40;31&#46;8&#44; 54&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">83&#46;0&#40;69&#46;2&#44; 92&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">63&#46;6&#40;40&#46;7&#44; 82&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">61&#46;9&#40;48&#46;8&#44; 73&#46;9&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR on Day 3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;295&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;698&#40;0&#46;584&#44; 0&#46;811&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">78&#46;4&#40;61&#46;8&#44; 90&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">51&#46;1&#40;35&#46;8&#44; 66&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">56&#46;6&#40;42&#46;2&#44; 70&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">74&#46;2&#40;55&#46;4&#44; 88&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="8" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Mechanical ventilation</span></td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR at admission&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;603&#40;0&#46;479&#44; 0&#46;727&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">47&#46;9&#40;33&#46;3&#44; 62&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">75&#46;7&#40;58&#46;8&#44; 88&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">71&#46;9&#40;53&#46;3&#44; 86&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">53&#46;8&#40;39&#46;5&#44; 67&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>CAR on Day 3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">7&#46;4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t\ttop\n
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Article information

ISSN: 01227262
Original language: English

DOI:10.1016/j.acci.2023.05.002

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2024 August	6	7	13
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