Original article
Platelets level variability during the first year after liver transplantation in the risk prediction model for recipients mortality
Wojciech Jarmulskia, Alicja Wieczorkowskaa, Mariusz Trzaskaa, Ewa Hryniewieckab, Leszek Pączekb,c, Michał Ciszekb,
Corresponding author
m.ciszek@wum.edu.pl
Corresponding author at: Department of Immunology, Transplant Medicine and Internal Diseases, Medical University of Warsaw, 59 Nowogrodzka St, Warsaw 02-006, Poland
Corresponding author at: Department of Immunology, Transplant Medicine and Internal Diseases, Medical University of Warsaw, 59 Nowogrodzka St, Warsaw 02-006, Poland
a Polish-Japanese Academy of Information Technology, 86 Koszykowa St, Warsaw, Poland
b Department of Immunology, Transplant Medicine and Internal Diseases, Medical University of Warsaw, 59 Nowogrodzka St, Warsaw, Poland
c Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 5A Pawinskiego St, Warsaw, Poland
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IQR range reported. * denotes predictors with <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">1</span><span class="elsevierStyleSectionTitle" id="sect0035">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Several simple models based on lymphocytes and platelets count and basic biochemical parameters have been reported as predictors of liver inflammation, fibrosis, and survival in viral hepatitis, hepatectomy and liver transplantation <a class="elsevierStyleCrossRefs" href="#bib0105">[1–3]</a>. Currently, all popular scoring systems related to liver diseases are composed of static values exclusively. All known parameters reflecting liver function may change significantly in liver transplant (LTx) recipient over time due to various processes like acute graft rejection, cholestasis, infections, etc.</p><p id="par0010" class="elsevierStylePara elsevierViewall">We believe that by using only static measurements, we lose useful information on variability and trend of observations during the examined period. This information could be useful in improving survival modeling. Recently some researchers showed that variability of various vital and biochemical measurements has an impact on patients’ survival in many diseases <a class="elsevierStyleCrossRefs" href="#bib0120">[4–6]</a>, but it is still relatively uncommon to focus on these kind of parameters.</p><p id="par0015" class="elsevierStylePara elsevierViewall">The first aim of our study was to verify whether measurements of variability and/or trend of selected biochemical parameters during the first year after LTx improve modeling of long-term survival prediction of the recipients. The second aim was to propose a new model for survival prediction after LTx.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2</span><span class="elsevierStyleSectionTitle" id="sect0040">Methods</span><p id="par0020" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Patient selection</span>. The retrospective cohort study covers patients who received LTx between 2000 and 20015 and were treated at transplant department. The study protocol has been approved by the Ethics Committee and it conforms to previsions of the Declaration of Helsinki.</p><p id="par0025" class="elsevierStylePara elsevierViewall">We included patients who fulfill the design experiment criteria, i.e., survived a minimum one year after LTx and have available data from a minimum of five outpatients’ visits in the first year after transplant in the electronic database. Lab results obtained before 2010 were entered manually and after that were downloaded by automatic lab system to the patient's database. Blood for the tests was taken during patients ambulatory visits. Results of liver function tests’: alanine transaminase (ALT), aspartate transaminase (AST), bilirubin, and creatinine, hemoglobin (HGB), platelets (PLT), white blood cells (WBC) chosen for the analysis were available for each patient visit.</p><p id="par0030" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Static, variability and trend measures</span>. We have selected parameters representing variability, trend and a static value for observations of each biochemical measurement in the first year after LTx. Static values, which are most commonly used in the literature, are represented by the last observation in the period. The alternative measures possible for static value are mean and median, but we found no significant differences between them. Variability could be represented in various ways, such as variation, standard deviation, the difference between maximum and minimum values. Inspired by the study by Bangalore et al. <a class="elsevierStyleCrossRef" href="#bib0135">[7]</a>, we have chosen successive average variability as the primary variability measure, which is defined as the average absolute difference between successive values of observations. We have found that the choice of particular variability measure does not have any significant impact on models performance. Finally, the trend measure was represented by the tangent of the regression line over observations.</p><p id="par0035" class="elsevierStylePara elsevierViewall">In order to reliably represent variability and trend measures, each patient in the scope of the analysis had to have a minimum number of observations. Theoretically, the trend can be measured with only two observations and variability – with 3. However, in practice including patients with such a small number of observations leads to unstable and unreliable models. Our choice of a minimum of five observations provided stable and reliable results in our modeling. Naturally, the higher the number of observations, the better, but increasing the required minimum also limits the number of patients who fulfill the criteria, so the smallest acceptable number was chosen.</p><p id="par0040" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Statistical analysis</span>. The statistical analysis was performed in R software, version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria). A significance value of 0.05 was considered in all statistic tests. Modeling patients’ survival was performed using Cox regression.</p><p id="par0045" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Validation methodology</span>. All models were tested for their performance on the unseen data using bootstrapping. Bootstrapping is a statistic method aimed at measuring estimating properties of an estimator using a strategy of random sampling with replacement. Performance was measured with the concordance index (C-index) <a class="elsevierStyleCrossRef" href="#bib0140">[8]</a>, which determines models discriminatory power. We used Uno's C formula which allows to reliably compare between models built on various subsets of patients <a class="elsevierStyleCrossRef" href="#bib0145">[9]</a>. To correct for results optimism and obtain confidence intervals for C-index, bootstrapping was repeated 100 times for each model.</p><p id="par0050" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Predictor selection</span>. For predictor selection, we used a forward stepwise selection method where added variables are characterized by the biggest increase of average C-index calculated in internal validation. The advantage of this method is receiving the final model with the low number of predictors which provide the biggest impact on the model's overall predictive power. Low number of variables in models also increases their applicability in medical practice and increases overall interpretability.</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3</span><span class="elsevierStyleSectionTitle" id="sect0045">Results</span><p id="par0055" class="elsevierStylePara elsevierViewall">The study included 450 patients, 266 (59%) men and 184 (41%) women, mean age at transplantation was 46 (standard deviation; SD<span class="elsevierStyleHsp" style=""></span>±<span class="elsevierStyleHsp" style=""></span>13.1) years. The median observation time was 4 years while the median time to censoring was 2 years and 4 months. Univariate analysis’ results are presented in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>, the predictive accuracy of single variables is visualized in <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>, and correlations between the predictors are visualized in <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>. There was no strong correlation between selected predictors. The best single predictor was the static value of AST with C-index 0.706 (0.5883–0.7494). Higher prediction score for static values was observed also in ALT 0.6102 (0.4843–0.6857) and bilirubin 0.6224 (0.5537–0.6695). Higher prediction score for variability was observed for creatinine 0.6023 (0.5409–0.6451), PLT 0.6350 (0.5491–0.7043), RBC 0.5689 (0.5065–0.6213) and WBC 0.6506 (0.5095–0.7124). Various sets of parameters (both static and variability and trends values) were tested to predict survival in the study group. Our best-fitted and proposed model for patients survival after LTx is presented and explained in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>, with C-index 0.8273 (interquartile range; IQR 0.7767–0.8649).</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="fig0005"></elsevierMultimedia><elsevierMultimedia ident="fig0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0060" class="elsevierStylePara elsevierViewall">Model formula:hazard<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>base hazard<span class="elsevierStyleHsp" style=""></span>*<span class="elsevierStyleHsp" style=""></span>exp(0.4609<span class="elsevierStyleHsp" style=""></span>*<span class="elsevierStyleHsp" style=""></span>AST Static<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>0.1934<span class="elsevierStyleHsp" style=""></span>*<span class="elsevierStyleHsp" style=""></span>PLT Variability<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.0780<span class="elsevierStyleHsp" style=""></span>*<span class="elsevierStyleHsp" style=""></span>WBC Trend<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>0.3459<span class="elsevierStyleHsp" style=""></span>*<span class="elsevierStyleHsp" style=""></span>PLT Trend)</p><p id="par0065" class="elsevierStylePara elsevierViewall">The model uses the following indicators for mortality prediction: the static value of AST, variability measure of PLT and trend measures of WBC and PLT. Model fit has been verified by the internal calibration presented in supplementary data files. Survival plot in <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a> shows that our model significantly stratifies patients based on their estimated risk.</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4</span><span class="elsevierStyleSectionTitle" id="sect0050">Discussion</span><p id="par0070" class="elsevierStylePara elsevierViewall">In our analysis, we first prove that variability and trend of observations can improve their predictive power, and then we incorporate our findings into the model predicting the survival of patients after LTx. In this study, we concentrate on predicting long-term patients’ survival based on blood tests’ results obtained in the first year after LTx. The rationale for such experiment design is based on the fact that many acute processes, e.g., ischemia, bile ducts’ injury, rejection, and infections often take please in the first transplant year and could influence long term morbidity and survival. Such study design introduces bias by excluding patients who did not survive the first year after LTx, but as we are focusing on the impact of variability and trend of measurements on survival, we need to use a period instead of a time-point to derive the predictors. We wanted to know which parameters in the first year after transplantation influence survival in the cohort of our patients in the outpatient department. In Poland, all recipients are follow up indefinitely in the transplant centers, so it was important to us to find a predictor of long term survival to increase surveillance in patients at risk.</p><p id="par0075" class="elsevierStylePara elsevierViewall">Several studies have concentrated recently on the impact of the variability of common health parameters on morbidity and mortality in various diseases. Heart rate variability recorded in 10-min electrocardiogram could predict 18-month survival in cirrhotic patients independent of age, gender, use of beta-blockers, and the etiology of liver disease <a class="elsevierStyleCrossRef" href="#bib0120">[4]</a>. Glomerular filtration rate is a simple measurement of kidney function, but its variability measured during the first year since diagnosis of chronic kidney disease was the best predictor of cardiovascular outcome in a group of 2869 patients <a class="elsevierStyleCrossRef" href="#bib0125">[5]</a>. In a prospective cohort analysis including 4982 participants in the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) greater visit-to-visit variability of fasting blood glucose level was associated with increased mortality risk over a median follow-up of 5 years. Visit-to-visit variability was defined as the standard deviation or variability of glucose results from the three visits <a class="elsevierStyleCrossRef" href="#bib0130">[6]</a>.</p><p id="par0080" class="elsevierStylePara elsevierViewall">In our study the best single predictors of patients’ survival were the static values of AST (C-index 0.706), bilirubin (C-index 0.6224), ALT (C-index 0.6102), but on the top half of best-performing predictors over 75% were variables being measures of variability or trend. Based on these findings, we propose the model for predicting long-term mortality in patients after LTx. The final predictors in the model are the static value of AST, the variability of PLT and trend for PLT and WBC. Even though WBC trend variable is not statistically significant in the model (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.111), it was included by the automated feature selection procedure indicating that it has an impact on the model's overall predictive power.</p><p id="par0085" class="elsevierStylePara elsevierViewall">Models based on lymphocyte and platelets count and basic biochemical parameters have been reported as predictors of liver graft survival. In a study of 269 hepatocellular carcinoma transplant recipients low absolute lymphocyte count at one month after LTx was significantly related to lower overall survival over a mean follow-up of 35.9 months. The classical predictors like total tumor size<span class="elsevierStyleHsp" style=""></span>>8<span class="elsevierStyleHsp" style=""></span>cm, pretransplant albumin<span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>2.8<span class="elsevierStyleHsp" style=""></span>g/dL, Model End Stage Liver Disease (MELD) score<span class="elsevierStyleHsp" style=""></span>><span class="elsevierStyleHsp" style=""></span>15 (<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.017), and tumor beyond Milan criteria were the other significant factors of overall survival <a class="elsevierStyleCrossRef" href="#bib0150">[10]</a>.</p><p id="par0090" class="elsevierStylePara elsevierViewall">Postoperative PLT of <30<span class="elsevierStyleHsp" style=""></span>G/L predicted major postoperative complications and early perioperative mortality in a prospective study of 120 consecutive living-donor LTx recipients <a class="elsevierStyleCrossRef" href="#bib0155">[11]</a>. In a prospective study of 257 LTx patients platelet count <60<span class="elsevierStyleHsp" style=""></span>G/L in the first posttransplant week was an independent predictor of severe complications and early graft and patient survival. However, in both studies, this predictive values were lost in patients who survived more than 90 days <a class="elsevierStyleCrossRef" href="#bib0160">[12]</a>. Conversely, in a retrospective analysis of 61 liver transplant recipients high neutrophil-to-lymphocyte ratio (NLR) and platelet count until postoperative day 14 were predictors of the 1- and 5-year survival. In multivariate analysis, NLR<span class="elsevierStyleHsp" style=""></span>≥<span class="elsevierStyleHsp" style=""></span>50 and platelets <80<span class="elsevierStyleHsp" style=""></span>G/L were independently associated with 1-year mortality <a class="elsevierStyleCrossRef" href="#bib0165">[13]</a>.</p><p id="par0095" class="elsevierStylePara elsevierViewall">Experimental and clinical data suggest an important role for platelet-derived factors in the regeneration of the liver after partial hepatectomy. In humans, platelet transfusion seems to improve regeneration in living-donor transplant recipients and liver function in patients with cirrhosis <a class="elsevierStyleCrossRefs" href="#bib0170">[14,15]</a>. Platelets can influence liver regeneration by many mechanisms <a class="elsevierStyleCrossRef" href="#bib0180">[16]</a>, e.g., secretion of growth factors into the hepatic circulation <a class="elsevierStyleCrossRefs" href="#bib0185">[17,18]</a>, transfer of RNA from platelets to hepatocytes, and recruitment of inflammatory cells <a class="elsevierStyleCrossRefs" href="#bib0195">[19,20]</a>. All these studies focused only on PLT static value, whereas our analysis indicates that both PLT variability and trend measures have significant predictive power.</p><p id="par0100" class="elsevierStylePara elsevierViewall">Our study has limitations. Firstly, we are concentrating only on patients who survived at least one year after liver transplantation. In our work, we focused on the period immediately after the transplantation as we hypothesize that this period should provide the most valuable information and is critical in determining further treatment. Variability period cannot be too short in order to provide the minimum number of observations and stability of trend and variability measures. Secondly, to derive variability predictors for the first year after liver transplantation, we had to exclude patients who have very few observations in this period (less than 5), which limits the applicability of our model in some cases. On the other hand, many papers analyzing variability and trends and predictive factors in LTx recipients included much fewer patients than in our study <a class="elsevierStyleCrossRefs" href="#bib0120">[4,10,13,14]</a>. We are planning to verify our model using data retrospectively collected from medical records of all patients transplanted in our center. Further experiments are also planned to carry out similar analyzes covering a longer follow-up period after liver transplantation. One of the possible future extensions of the model is to incorporate the sliding window approach, i.e. to use one year period (or other lengths) not necessarily after liver transplantation to determine the remaining survival rate.</p><p id="par0105" class="elsevierStylePara elsevierViewall">In conclusion, adding variability and trend measures increases models’ predictive accuracy in modeling patients survival after LTx. Using this observation, we propose a high-accuracy survival model in which variability and trend of PLT measures in the first year after transplantation are strong predictors of long-term mortality.<span class="elsevierStyleDefList"><span class="elsevierStyleSectionTitle" id="sect1050">Abbreviations</span><span class="elsevierStyleDefTerm">LTx</span><span class="elsevierStyleDefDescription"><p id="par1080" class="elsevierStylePara elsevierViewall">liver transplant</p></span><span class="elsevierStyleDefTerm">AST</span><span class="elsevierStyleDefDescription"><p id="par1085" class="elsevierStylePara elsevierViewall">aspartate transaminase</p></span><span class="elsevierStyleDefTerm">ALT</span><span class="elsevierStyleDefDescription"><p id="par1090" class="elsevierStylePara elsevierViewall">alanine transaminase</p></span><span class="elsevierStyleDefTerm">PLT</span><span class="elsevierStyleDefDescription"><p id="par1095" class="elsevierStylePara elsevierViewall">blood platelets</p></span><span class="elsevierStyleDefTerm">RBC</span><span class="elsevierStyleDefDescription"><p id="par1100" class="elsevierStylePara elsevierViewall">red blood cells</p></span><span class="elsevierStyleDefTerm">WBC</span><span class="elsevierStyleDefDescription"><p id="par1105" class="elsevierStylePara elsevierViewall">white blood cells</p></span><span class="elsevierStyleDefTerm">IQR</span><span class="elsevierStyleDefDescription"><p id="par1110" class="elsevierStylePara elsevierViewall">interquartile range</p></span></span></p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Funding</span><p id="par0110" class="elsevierStylePara elsevierViewall">The research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Conflict of interest</span><p id="par0115" class="elsevierStylePara elsevierViewall">None.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:9 [ 0 => array:3 [ "identificador" => "xres1358385" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction and objectives" ] 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" => "xpalclavsec1249331" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 3 => array:2 [ "identificador" => "sec0010" "titulo" => "Methods" ] 4 => array:2 [ "identificador" => "sec0015" "titulo" => "Results" ] 5 => array:2 [ "identificador" => "sec0020" "titulo" => "Discussion" ] 6 => array:2 [ "identificador" => "sec0025" "titulo" => "Funding" ] 7 => array:2 [ "identificador" => "sec0030" "titulo" => "Conflict of interest" ] 8 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2020-01-13" "fechaAceptado" => "2020-03-05" "PalabrasClave" => array:1 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1249331" "palabras" => array:5 [ 0 => "Blood platelets" 1 => "Data analysis" 2 => "Liver transplantation" 3 => "Prognosis" 4 => "Risk assessment" ] ] ] ] "tieneResumen" => true "resumen" => array:1 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction and objectives</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Many scoring systems in liver diseases use static values of liver function parameters. These parameters may change significantly in liver transplant (LTx) recipients over time due to various processes. The study was aimed at building a new model for survival prediction after LTx based on variability of selected parameters.</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Materials and methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">The study included 450 LTx recipients who survived a minimum one year after transplantation. We analyzed liver enzymes and hematology parameters static values and their variability during the first year after transplantation. Modeling patients’ survival was performed using Cox regression. Various sets of parameters (both static and variability and trends values) were tested to predict survival in our study group. Models’ performance was measured using the concordance index.</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The single predictors of the patients survival were the static values of AST with C-index 0.706 (0.5883–0.7494), ALT 0.6102 (0.4843–0.6857) and bilirubin 0.6224 (0.5537–0.6695). High prediction scores were observed for variability in creatinine 0.6023 (0.5409–0.6451), PLT 0.6350 (0.5491–0.7043), RBC 0.5689 (0.5065–0.6213) and WBC 0.6506 (0.5095–0.7124). Our best-fitted and proposed model for patients survival after LTx has C-index 0.8273 (IQR 0.7767–0.8649). The model uses the following indicators for mortality prediction: the static value of AST, variability measure of PLT and trend measures of WBC and PLT.</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusions</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">Adding variability and trend measures increases predictive accuracy in modeling patients survival after LTx. We propose a high-accuracy survival model in which variability and trend of PLT measures in the first year after transplantation are strong predictors of long-term mortality.</p></span>" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction and objectives" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusions" ] ] ] ] "multimedia" => array:5 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1715 "Ancho" => 3010 "Tamanyo" => 382166 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Concordance of each predictor in univariate analysis. IQR range reported. * denotes predictors with <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span><<span class="elsevierStyleHsp" style=""></span>0.05.</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 2137 "Ancho" => 2333 "Tamanyo" => 514870 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Survival plot for the proposed model for patients survival after liver transplantation.</p>" ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 823 "Ancho" => 1500 "Tamanyo" => 86840 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">Comparison of models predictive performance. IQR reported.</p>" ] ] 3 => 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:2 [ "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 " colspan="2" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Variable</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">Hazard ratio (95% CI<a class="elsevierStyleCrossRef" href="#tblfn0035"><span class="elsevierStyleSup">g</span></a>) \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"><span class="elsevierStyleItalic">P</span> value \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">C-index (IQR<a class="elsevierStyleCrossRef" href="#tblfn0040"><span class="elsevierStyleSup">h</span></a>) \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 " 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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Age \t\t\t\t\t\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">0.86 (0.66–1.12) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.2754 \t\t\t\t\t\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">0.5387 (0.5039–0.5803) \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 " 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="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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.75 (0.44–1.30) \t\t\t\t\t\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">0.3077 \t\t\t\t\t\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">0.2193 (0.1845–0.2767) \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 " rowspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">ALT<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a></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">Static \t\t\t\t\t\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">1.61 (1.39–1.87) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.6102 (0.4843–0.6857) \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">Variability \t\t\t\t\t\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">1.32 (1.18–1.48) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.5285 (0.4682–0.5714) \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">Trend \t\t\t\t\t\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">1.32 (1.08–1.62) \t\t\t\t\t\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">0.0074 \t\t\t\t\t\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">0.5599 (0.4045–0.6322) \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 " rowspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">AST<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">b</span></a></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">Static \t\t\t\t\t\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">1.58 (1.40–1.79) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.7060 (0.5883–0.7494) \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">Variability \t\t\t\t\t\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">1.52 (1.35–1.72) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.6213 (0.5487–0.6624) \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">Trend \t\t\t\t\t\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">1.59 (1.35–1.87) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.6379 (0.5267–0.7284) \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 " rowspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Bilirubin</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">Static \t\t\t\t\t\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">1.46 (1.31–1.62) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.6224 (0.5537–0.6695) \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">Variability \t\t\t\t\t\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">1.37 (1.24–1.52) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.5921 (0.5235–0.6463) \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">Trend \t\t\t\t\t\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">1.74 (1.46–2.08) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.0000 \t\t\t\t\t\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">0.6077 (0.5619–0.6549) \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 " rowspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Creatinine</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">Static \t\t\t\t\t\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">0.86 (0.63–1.18) \t\t\t\t\t\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">0.3554 \t\t\t\t\t\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">0.4617 (0.3810–0.5052) \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">Variability \t\t\t\t\t\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">1.20 (1.04–1.38) \t\t\t\t\t\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">0.0103 \t\t\t\t\t\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">0.6023 (0.5409–0.6451) \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">Trend \t\t\t\t\t\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">1.06 (0.81–1.38) \t\t\t\t\t\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">0.6960 \t\t\t\t\t\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">0.4598 (0.4172–0.5293) \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 " rowspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HGB<a class="elsevierStyleCrossRef" href="#tblfn0030"><span class="elsevierStyleSup">f</span></a></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">Static \t\t\t\t\t\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">0.58 (0.45–0.74) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.4417 (0.3812–0.5027) \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">Variability \t\t\t\t\t\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">1.53 (1.29–1.81) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.5812 (0.4989–0.6245) \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">Trend \t\t\t\t\t\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">0.51 (0.40–0.66) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.4587 (0.4026–0.5501) \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 " rowspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">PLT<a class="elsevierStyleCrossRef" href="#tblfn0025"><span class="elsevierStyleSup">e</span></a></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">Static \t\t\t\t\t\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">1.21 (0.97–1.51) \t\t\t\t\t\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">0.0868 \t\t\t\t\t\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">0.5436 (0.4429–0.6156) \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">Variability \t\t\t\t\t\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">1.22 (1.10–1.35) \t\t\t\t\t\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">0.0002 \t\t\t\t\t\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">0.6350 (0.5491–0.7043) \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">Trend \t\t\t\t\t\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">1.32 (1.00–1.75) \t\t\t\t\t\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">0.0513 \t\t\t\t\t\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">0.5804 (0.4888–0.6441) \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 " rowspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">RBC<a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">c</span></a></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">Static \t\t\t\t\t\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">0.56 (0.43–0.74) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.4884 (0.4086–0.5767) \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">Variability \t\t\t\t\t\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">1.37 (1.17–1.59) \t\t\t\t\t\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">0.0001 \t\t\t\t\t\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">0.5689 (0.5065–0.6213) \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">Trend \t\t\t\t\t\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">0.54 (0.44–0.68) \t\t\t\t\t\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"><0.0000 \t\t\t\t\t\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">0.5156 (0.4361–0.6029) \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 " rowspan="3" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">WBC<a class="elsevierStyleCrossRef" href="#tblfn0020"><span class="elsevierStyleSup">d</span></a></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">Static \t\t\t\t\t\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">0.17 (0.01–2.59) \t\t\t\t\t\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">0.2042 \t\t\t\t\t\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">0.5087 (0.4221–0.5668) \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">Variability \t\t\t\t\t\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">1.06 (0.96–1.16) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.2674 \t\t\t\t\t\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">0.6506 (0.5095–0.7124) \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">Trend \t\t\t\t\t\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">0.95 (0.86–1.04) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.2690 \t\t\t\t\t\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">0.3694 (0.3342–0.4999) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2333778.png" ] ] ] "notaPie" => array:8 [ 0 => array:3 [ "identificador" => "tblfn0005" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Alanine aminotransaminase.</p>" ] 1 => array:3 [ "identificador" => "tblfn0010" "etiqueta" => "b" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">Aspartate aminotransaminase.</p>" ] 2 => array:3 [ "identificador" => "tblfn0015" "etiqueta" => "c" "nota" => "<p class="elsevierStyleNotepara" id="npar0015">Red blood cells.</p>" ] 3 => array:3 [ "identificador" => "tblfn0020" "etiqueta" => "d" "nota" => "<p class="elsevierStyleNotepara" id="npar0020">White blood cells.</p>" ] 4 => array:3 [ "identificador" => "tblfn0025" "etiqueta" => "e" "nota" => "<p class="elsevierStyleNotepara" id="npar0025">Blood platelets count.</p>" ] 5 => array:3 [ "identificador" => "tblfn0030" "etiqueta" => "f" "nota" => "<p class="elsevierStyleNotepara" id="npar0030">Hemoglobin,.</p>" ] 6 => array:3 [ "identificador" => "tblfn0035" "etiqueta" => "g" "nota" => "<p class="elsevierStyleNotepara" id="npar0035">Confidence interval.</p>" ] 7 => array:3 [ "identificador" => "tblfn0040" "etiqueta" => "h" "nota" => "<p class="elsevierStyleNotepara" id="npar0040">Interquartile range.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Results of univariate analysis of variables as predictors of liver transplant recipients’ survival after transplantation.</p>" ] ] 4 => 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 [ "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="middle" 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="middle" scope="col" style="border-bottom: 2px solid black">Coefficient \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="middle" scope="col" style="border-bottom: 2px solid black">Hazard ratio \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="middle" scope="col" style="border-bottom: 2px solid black">SE<a class="elsevierStyleCrossRef" href="#tblfn0060"><span class="elsevierStyleSup">d</span></a> \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="middle" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">P</span> 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" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="middle">AST<a class="elsevierStyleCrossRef" href="#tblfn0045"><span class="elsevierStyleSup">a</span></a> Static \t\t\t\t\t\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="middle">0.4609 \t\t\t\t\t\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="middle">1.5856 \t\t\t\t\t\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="middle">0.0613 \t\t\t\t\t\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="middle"><0.0000 \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 " align="left" valign="middle">PLT<a class="elsevierStyleCrossRef" href="#tblfn0050"><span class="elsevierStyleSup">b</span></a> Variability \t\t\t\t\t\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="middle">0.1934 \t\t\t\t\t\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="middle">1.2134 \t\t\t\t\t\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="middle">0.0595 \t\t\t\t\t\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="middle">0.0012 \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 " align="left" valign="middle">WBC<a class="elsevierStyleCrossRef" href="#tblfn0055"><span class="elsevierStyleSup">c</span></a> Trend \t\t\t\t\t\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="middle">−0.0780 \t\t\t\t\t\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="middle">0.9250 \t\t\t\t\t\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="middle">0.0489 \t\t\t\t\t\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="middle">0.1110 \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 " align="left" valign="middle">PLT<a class="elsevierStyleCrossRef" href="#tblfn0050"><span class="elsevierStyleSup">b</span></a> Trend \t\t\t\t\t\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="middle">0.3459 \t\t\t\t\t\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="middle">1.4132 \t\t\t\t\t\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="middle">0.1432 \t\t\t\t\t\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="middle">0.0157 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2333779.png" ] ] ] "notaPie" => array:4 [ 0 => array:3 [ "identificador" => "tblfn0045" "etiqueta" => "a" "nota" => "<p class="elsevierStyleNotepara" id="npar0045">Aspartate aminotransaminase.</p>" ] 1 => array:3 [ "identificador" => "tblfn0050" "etiqueta" => "b" "nota" => "<p class="elsevierStyleNotepara" id="npar0050">Blood platelets count.</p>" ] 2 => array:3 [ "identificador" => "tblfn0055" "etiqueta" => "c" "nota" => "<p class="elsevierStyleNotepara" id="npar0055">White blood cells.</p>" ] 3 => array:3 [ "identificador" => "tblfn0060" "etiqueta" => "d" "nota" => "<p class="elsevierStyleNotepara" id="npar0060">Standard error.</p>" ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Proposed model for patients’ survival after liver transplantation. Model formula: hazard<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>base hazard * exp(0.4609 * AST Static<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>0.1934 * PLT Variability<span class="elsevierStyleHsp" style=""></span>−<span class="elsevierStyleHsp" style=""></span>0.0780 * WBC Trend<span class="elsevierStyleHsp" style=""></span>+<span class="elsevierStyleHsp" style=""></span>0.3459 * PLT Trend).</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:20 [ 0 => array:3 [ "identificador" => "bib0105" "etiqueta" => "[1]" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Aspartate transaminase to platelet ratio index and gamma-glutamyl transpeptidase-to-platelet ratio outweigh fibrosis index based on four factors and red cell distribution width-platelet ratio in diagnosing liver fibrosis and inflammation in chronic hepatitis B" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "X. 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| Year/Month | Html | Total | |
|---|---|---|---|
| 2024 November | 1 | 0 | 1 |
| 2024 October | 10 | 4 | 14 |
| 2024 September | 15 | 8 | 23 |
| 2024 August | 19 | 13 | 32 |
| 2024 July | 41 | 6 | 47 |
| 2024 June | 15 | 3 | 18 |
| 2024 May | 33 | 8 | 41 |
| 2024 April | 21 | 8 | 29 |
| 2024 March | 26 | 8 | 34 |
| 2024 February | 45 | 18 | 63 |
| 2024 January | 17 | 15 | 32 |
| 2023 December | 14 | 16 | 30 |
| 2023 November | 14 | 23 | 37 |
| 2023 October | 22 | 29 | 51 |
| 2023 September | 19 | 11 | 30 |
| 2023 August | 17 | 19 | 36 |
| 2023 July | 12 | 13 | 25 |
| 2023 June | 22 | 10 | 32 |
| 2023 May | 49 | 19 | 68 |
| 2023 April | 46 | 8 | 54 |
| 2023 March | 35 | 11 | 46 |
| 2023 February | 10 | 12 | 22 |
| 2023 January | 14 | 10 | 24 |
| 2022 December | 18 | 14 | 32 |
| 2022 November | 28 | 19 | 47 |
| 2022 October | 13 | 20 | 33 |
| 2022 September | 17 | 25 | 42 |
| 2022 August | 11 | 20 | 31 |
| 2022 July | 13 | 19 | 32 |
| 2022 June | 9 | 24 | 33 |
| 2022 May | 15 | 13 | 28 |
| 2022 April | 7 | 11 | 18 |
| 2022 March | 23 | 7 | 30 |
| 2022 February | 25 | 4 | 29 |
| 2022 January | 57 | 6 | 63 |
| 2021 December | 36 | 13 | 49 |
| 2021 November | 16 | 8 | 24 |
| 2021 October | 32 | 16 | 48 |
| 2021 September | 25 | 11 | 36 |
| 2021 August | 10 | 6 | 16 |
| 2021 July | 24 | 10 | 34 |
| 2021 June | 22 | 10 | 32 |
| 2021 May | 18 | 7 | 25 |
| 2021 April | 41 | 16 | 57 |
| 2021 March | 35 | 11 | 46 |
| 2021 February | 8 | 5 | 13 |
| 2021 January | 12 | 10 | 22 |
| 2020 December | 11 | 11 | 22 |
| 2020 November | 21 | 10 | 31 |
| 2020 October | 21 | 6 | 27 |
| 2020 September | 36 | 11 | 47 |
| 2020 August | 63 | 15 | 78 |
| 2020 July | 42 | 23 | 65 |
| 2020 June | 5 | 10 | 15 |
| 2020 May | 8 | 10 | 18 |
| 2020 April | 2 | 6 | 8 |
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