metricas
covid
Buscar en
Clinics
Toda la web
Inicio Clinics The health facility as a risk factor for multidrug-resistant gram-negative bacte...
Journal Information
Vol. 77.
(January - December 2022)
Share
Share
Download PDF
More article options
Visits
1691
Vol. 77.
(January - December 2022)
Original articles
Full text access
The health facility as a risk factor for multidrug-resistant gram-negative bacteria in critically ill patients with COVID-19
Visits
1691
Viviane de Macedoa,b,c,
Corresponding author
vioakmac@gmail.com

Corresponding author.
, Gabriela de Souza dos Santosd, Rodolff Nunes da Silvae, Caio Nogara de Menezes Coutof, Camila Bastosb, Eloize Viecellib, Marina do Nascimento Mateusb, Maria Esther Grafg, Raquel Bernardelli Gonçalvesg, Márcia Aparecida da Silvag, Patricia Dal Bem Bernardinig, Roberta Serra Pereira Grandog, Viviane Pavanelo Boaventurag, Helki Simone Rodrigues Pereirag, Anna S. Levinc
a Hospital Infection Control and Epidemiology Center, Santa Casa de Curitiba, Curitiba, PR, Brazil
b Faculdade de Medicina, Universidade Positivo, Curitiba, PR, Brazil
c Department of Infectious Diseases, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
d Hospital Infection Control and Epidemiology Center, Rehabilitation Hospital, Curitiba, PR, Brazil
e Infection Control Program, Hospital de Clínicas, Curitiba, PR, Brazil
f Faculdade de Medicina, Pontifícia Universidade Católica do Paraná, Curitiba, PR, Brazil
g Hospital Infection Control and Epidemiology Center, Hospital do Trabalhador, Curitiba, PR, Brazil
Highlights

  • Critically ill patients with COVID-19 can have secondary infections.

  • Some of them are caused by Multidrug-Resistant (MDR) Gram-Negative Bacteria (GNB).

  • To know the risk factors related to MDR-GNB is essential to analyze if it is possible to prevent them.

  • Hospital of admission for patients with COVID-19 posed as high risk for MDR-GNB.

  • This is a modifiable risk factor.

This item has received
Article information
Abstract
Full Text
Bibliography
Download PDF
Statistics
Tables (1)
Table 1. Factors associated with acquiring multidrug-resistant gram-negative bacteria in patients with severe COVID-19 admitted to intensive care units of 4 hospitals (Curitiba, Brazil. March ‒ December 2020).
Abstract
Background

The relationship between Multidrug Resistant-Gram Negative Bacteria (MDR-GNB) infection and colonization in critically ill COVID-19 patients has been observed, however, it is still poorly understood. This study evaluated the risk factors for acquiring MDR-GNB in patients with severe COVID-19 in Intensive Care Units (ICU).

Methods

This is a nested case-control study in a cohort of 400 adult patients (≥ 18 years old) with COVID-19, hospitalized in the ICU of 4 hospitals in the city of Curitiba, Brazil. Cases were critical COVID-19 patients with one or more MDR GNB from any surveillance and/or clinical cultures were taken during their ICU stay. Controls were patients from the same units with negative cultures for MDR-GNB. Bivariate and multivariate analyses were done.

Results

Sixty-seven cases and 143 controls were included. Independent risk factors for MDR bacteria were: male gender (OR = 2.6; 95% CI 1.28‒5.33; p = 0.008); the hospital of admission (OR = 3.24; 95% CI 1.39‒7.57; p = 0.006); mechanical ventilation (OR = 25.7; 95% CI 7.26‒91; p < 0.0001); and desaturation on admission (OR = 2.6; 95% CI 1.27‒5.74; p = 0.009).

Conclusions

Male gender, desaturation, mechanical ventilation, and the hospital of admission were the independent factors associated with MDR-GNB in patients in the ICU with COVID-19. The only modifiable factor was the hospital of admission, where a newly opened hospital posed a higher risk. Therefore, coordinated actions toward a better quality of care for critically ill COVID-19 patients are essential.

Keywords:
COVID-19
Antimicrobial resistance
Gram-negative bacteria
Intensive Care Unit
Health Facility
Full Text
Introduction

Bacterial Antimicrobial Resistance (AMR) is one of the main threats to global health, limiting therapeutic options for treating infectious diseases. This leads to longer hospital stays, higher medical costs, and increased mortality.1 Before the COVID-19 pandemic, there were an estimated 4.95 million deaths associated with bacterial AMR in 2019, including 1.27 million deaths attributable to bacterial AMR in the world.2

Mainly during the first and second waves of the COVID-19 pandemic, there was excessive use of antimicrobials due to the severity of the patients and the lack of therapeutic options. Overuse of antibiotics has been related to antimicrobial resistance.3 Furthermore, hospitals were overcrowded and with overwhelming shortages in essential resources: healthcare workers, Personal Protective Equipment (PPE), ventilators, hospital beds, and infection control programs.4 Overcrowding has been associated with lower quality of care and a higher prevalence of infectious diseases among patients.5

Studies have shown that mainly critically ill patients with COVID-19 have secondary infections,6–10 a proportion of them caused by Multidrug-Resistant (MDR) Gram-Negative Bacteria (GNB). The incidence of MDR-GNB infections in critically ill COVID-19 patients in different regions of the world ranges between 3.4% to 46%.6–15 COVID-19 patients admitted to the ICU also seem to be more susceptible to colonization by MDR.16

Although MDR-GNB infections and colonizations have been linked to prolonged length of stay in the ICU, longer invasive mechanical ventilation, and steroid therapy,716 this relation is not fully understood and has been still little explored. Therefore, the aim of this study was to identify the risk factors for the isolation of MDR-GNB in critically ill COVID-19 patients.

Methods

This is a nested case-control study in a cohort of 400 adult patients (≥ 18 years old) with COVID-19 hospitalized in the Intensive Care Units (ICU) of 4 hospitals in the city of Curitiba, Brazil:8 1- Santa Casa (a philanthropic hospital with 249 beds, with 48 ICU beds, which is a referral hospital for chest pain, and renal and heart transplantation); 2- Trabalhador Hospital (a state institution with 222 beds, including 40 ICU beds, that is a trauma referral hospital); 3- Rehabilitation Hospital (a state institution with 82 beds, including 62 ICU beds, which was adapted for COVID-19 patients only); and 4- Institute of Medicine (a private hospital, which was reopened by the city as a field hospital for COVID-19 patients, with 100 beds, including 60 ICU beds). The patients were admitted between 1st March and 31st December 2020.

Cases were defined as severe COVID-19 patients with one or more MDR GNB from any surveillance and/or clinical cultures taken during their ICU stay.

Controls were severe COVID-19 patients in which no MDR GNB and/or MDR gram-positive bacteria were isolated (surveillance and/or clinical cultures) during their ICU stay. Patients who did not undergo surveillance cultures were excluded from the control group.

COVID-19 was defined as an infection confirmed with a positive Real-Time reverse transcriptase Polymerase Chain Reaction (RT-PCR) for SARS-CoV-2 from a nasopharyngeal swab, associated with suggestive signs, symptoms, and/or compatible radiological findings.

Patients with severe disease were defined as patients with Oxygen Saturation (SpO2) ≤94% in ambient air; or requiring supplemental oxygen; requiring mechanical ventilation; or requiring extracorporeal membrane oxygenation.8

The WHO Clinical Progression Scale17 was used to measure the COVID-19 severity of each patient on admission to the ICU: 0 (not infected); 1‒3 (ambulatory mild disease): 1- Asymptomatic; 2- Symptomatic, independent; 3- Symptomatic, assistance needed; 4‒5 (hospitalized with moderate disease): 4- No oxygen needed; 5- Oxygen by mask or nasal prongs; 6‒9 (hospitalized: severe disease) 6- Oxygen by non-invasive ventilation or high flow; 7- Intubation and mechanical ventilation, pO2/FiO2 ≥150 or SpO2/FiO2 ≥200; 8- Mechanical ventilation pO2/FiO2 < 150 or SpO2/FiO2 < 200, or vasopressors; 9- Mechanical ventilation pO2/FiO2 < 150 and vasopressors, dialysis, or ECMO; and 10 (dead).

The following GNBs were considered to be Multidrug Resistant (MDR):

  • Enterobacteriales resistant from first to third/fourth generation cephalosporins;

  • Carbapenem-Resistant Enterobacteriales (CRE);

  • Carbapenem-Resistant Acinetobacter spp. (CRA);

  • Carbapenem-Resistant Pseudomonas aeruginosa (CRP).

For bacterial identification, MICROSCAN WalkAway® (Beckman Coulter, Inc., Brea, CA, USA) automated system was used. Sensitivity to antimicrobial agents was also determined by MICROSCAN WalkAway® system according to the Brazilian Committee on Antimicrobial Susceptibility (BRCast) criteria. The phenotypic confirmation of ESBL production was done using the double-disk synergy test with cefotaxime, ceftazidime, and cefepime with and without clavulanic acid. Combined disk diffusion using phenylboronic acid as an inhibitor with cefoxitin was used for phenotypic confirmation of AmpC phenotype. Carbapenemase production was demonstrated by the modified Hodge test and by the Modified Carbapenem Inactivation Method (mCIM) and EDTA-mCIM (eCIM).

Broad-spectrum antibiotics were defined as any agent with activity against gram-positive bacteria such as Methicillin-Susceptible Staphylococcus Aureus (MSSA) and gram-negative bacteria such as Enterobacteriales and Pseudomonas. These included piperacillin-tazobactam, meropenem, cefepime, ceftazidime, amikacin, gentamicin, and fluoroquinolones.18

Data were extracted from electronic medical records using a data collection form. Demographic characteristics included age, sex, weight, and Body Mass Index (BMI). Clinical information included signs, symptoms, comorbidities, and treatment measures (antimicrobial therapy; steroid therapy; respiratory support; use of kidney replacement therapy; and length of use of invasive devices such as an orotracheal tube, central venous catheter, and indwelling urinary catheter). Laboratory assessment consisted of laboratory tests on admission to the ICU and at the moment of diagnosis of a secondary infection: C-reactive protein, total leukocyte count, number of lymphocytes, creatinine, glucose, D-dimer, troponin, and the ratio of Arterial Oxygen Partial pressure (PaO2) to Fraction of Inspired Oxygen (FiO2). Lung radiologic alterations were defined based on the medical report of the chest radiograph or computed tomography. Duration of the disease from the onset of symptoms, and length of stay in the hospital and in the ICU was also documented.

This study was approved by the research ethics committees of the study hospitals (Protocol n° 4.361.502, CAAE: 38239820.8.0000.5225).

Data analysis

The authors compared all critical COVID-19 patients with one or more MDR GNB from any surveillance and/or clinical cultures taken during their ICU admission (cases), with the controls.

The possible association of demographic and clinical variables with each dependent variable was initially tested in a bivariate analysis calculating the odds ratios and 95% Confidence Interval for each variable. Variables potentially associated with the development of each dependent variable in bivariable analysis (p < 0.20) were included in a multivariable logistic regression model in order to determine the adjusted odds ratios. Mechanical ventilation was chosen to represent invasive devices in multivariable analysis.

The variables C-reactive Protein (CRP), PaO2/FiO2 ratio, absolute lymphocyte count, and absolute leukocyte count were transformed from continuous variables to categorical variables. For CRP, the cutoff point used was the value of 108 mg/L, based on the study 19 which observed an increase in mortality among patients with values above this cut-off. For PaO2/FiO2 four categories of severity of acute respiratory syndrome were used, according to Villar et al.20 namely: >300 mmHg; 200‒299 mmHg; 100‒199 mmHg, and < 100 mmHg. For absolute lymphocyte count, the cutoff point used was the value of < 1.0 × 103 cells/µL, based on Wagner et al.,21 which observed that values below this cut-off were related to disease severity and clinical outcomes in COVID-19. For absolute leukocyte count, leukocytosis was defined as a leukocyte count >11.0 × 103 cells/µL.22

Statistical analyses were performed using EPI Info 7 Software (version 7.2.4, Centers for Disease Control and Prevention, Atlanta, EUA); p-values < 0.05 were considered to be statistically significant.

Results

From a cohort of 400 adult patients, 67 cases (15 with surveillance cultures positive for MDR GNB; 34 with clinical cultures positive for MDR-GNB; and 18 positives for both surveillance and clinical cultures) and 143 controls were included.

Among the demographic characteristics, clinical information, treatment measures, and complementary exams, the factors associated with MDR-GNB in the bivariate analysis were: gender; the hospital of admission; WHO Clinical Progression Scale; low leukocyte count; low lymphocyte count; high C-reactive protein; low PaO2/FiO2 ratio on ICU admission; pronation; and use of devices in the ICU (Table 1).

Table 1.

Factors associated with acquiring multidrug-resistant gram-negative bacteria in patients with severe COVID-19 admitted to intensive care units of 4 hospitals (Curitiba, Brazil. March ‒ December 2020).

Characteristics  Cases (n = 67)  Controls (n = 143)  Bivariate analysis Odds Ratio (95% CI)  p*  Multivariate analysis, Odds Ratio (95% CI)  p* 
Age, years mean (SD)  62.6 (13.6)  64.6 (14.6)    0.34     
Gender, n (%)             
Female  23 (34)  71 (50)  Reference       
Male  44 (66)  72 (50)  1.88 (1.03‒3.44)  0.03  2.61 (1.28‒5.33)  0.008 
Hospital of admission             
Santa Casa  12 (18)  51 (36)  Reference       
Trabalhador Hospital  13 (19)  35 (24)  1.50 (0.64‒3.86)  0.31  3.24 (1.39‒7.57)  0.0064 
Institute of Medicine  20 (30)  20 (14)  4.20 (1.75‒10.2)  0.0013     
Rehabilitation Hospital  22 (33)  37 (26)  2.50 (1.11‒5.74)  0.02     
Comorbidities, n (%)             
Hypertension  47 (70)  87 (61)  1.50 (0.81‒2.81)  0.19     
Coronary heart disease  20 (30)  37 (26)  1.20 (0.64‒2.31)  0.54     
Diabetes  20 (30)  49 (34)  0.81 (0.43‒1.52)  0.52     
Chronic kidney disease  5 (7)  11 (8)  0.96 (0.32‒2.90)  0.95     
Chronic obstructive lung disease  8 (12)  13 (9)  1.30 (0.53‒3.44)  0.52     
Cancer  2 (3)  4 (3)  1.06 (0.19‒5.90)  0.93     
Body Mass Index, n (%)             
18‒24.9  6 (14)  16 (19)         
25‒29.9  15 (34)  33 (40)  1.17 (0.38‒3.50)  0.77     
≥30  23 (52)  33 (40)  1.85 (0.63‒5.40)  0.26     
Signs and symptoms on hospital admission, n (%)             
Cough  41 (61)  99 (69)  0.70 (0.38‒1.28)  0.25     
Dyspnoea  58 (86)  115 (80)  1.56 (0.69‒3.54)  0.27     
Fever ≥ 37°C  35 (52)  73 (51)  1.04 (0.58‒1.87)  0.87     
Desaturation (SpO2 < 92%)  48 (72)  87 (61)  1.62 (0.86‒3.04)  0.12  2.70 (1.27‒574)  0.0009 
Fatigue  15 (22)  36 (25)  0.85 (0.43‒1.70)  0.66     
Diarrhea  2 (3)  12 (8)  0.33 (0.07‒1.54)  0.16     
Nausea or vomiting  3 (4)  13 (9)  0.47 (0.13‒1.71)  0.25     
Headache  3 (4)  15 (10)  0.43 (0.11‒1.44)  0.16     
Anosmia  8 (12)  18 (12)  0.94 (0.38‒2.28)  0.89     
Dysgeusia  5 (7)  15 (10)  0.68 (0.23‒1.97)  0.49     
Sore throat  1 (1)  7 (5)  0.29 (0.03‒2.44)  0.25     
Treatment and outcomes             
Pronation, n (%)  40 (65)  106 (38)  0.51 (0.27‒0.95)  0.035     
Renal replacement therapy, n (%)  21 (28)  43 (16)  1.06 (0.56‒1.98)  0.85     
MV, n (%)  64 (95)  74 (52)  9.50 (3.16‒25)  <0.0001  25.7 (7.26‒91)  <0.0001 
Mean days MV (SD)  11 (7)  8(5)    0.0005     
CVC, n (%)  64 (95)  74 (52)  9.50 (3.16‒25)  <0.0001     
Mean days of CVC (SD)  10 (5)  7 (5)    0.0001     
IUC, n (%)  64 (95)  82 (57)  15.80 (4.7‒52)  <0.0001     
Mean days of IUC (SD)  10 (5)  9 (5)    0.17     
Use of steroids, n (%)  50 (78)  103 (72)  1.14 (0.59‒2.21)  0.69     
Use of anticoagulants, n (%)  67 (100)  138 (96)  5.36 (0.29‒98)  0.25     
Therapeutic dose  3 (5)  16 (11)  0.37 (0.10‒1.32)  0.12     
Prophylactic dose  64 (95)  122 (85)  3.67 (1.05‒12.7)  0.04     
Use of antibiotics, n (%)  63 (94)  139 (97)  0.45 (0.10‒1.87)  0.27     
Broad-spectrum antibiotics, n (%)  22 (33)  32 (22)  1.69(0.89‒3.20)  0.10     
Ceftriaxone  55 (87)  124 (89)  0.7 (0.31‒1.50)  0.38     
Azithromycin  63 (94)  120 (86)  3.0 (1.00‒9.10)  0.05     
Cefepime  11 (17)  20 (14)  1.2 (0.54‒2.60)  0.64     
Piperacillin-tazobactam  10 (16)  8 (6)  2.9 (1.11‒7.80)  0.02     
Amikacin  7 (11)  9 (5)  1.7 (0.61‒4.80)  0.29     
Meropenem  2 (3)  2 (1)  2.1 (0.29‒15.7)  0.44     
Levofloxacin  4 (6)  1 (0.38)  9.01 (0.98‒82)  0.05     
Vancomycin  8 (13)  7 (5)  2.4 (0.84‒7.04)  0.09     
WHO scale on admission ‒ Mean (SD)  6.0 (1.25)  5.3 (1.10)    0.0001     
Days between onset of symptoms and ICU admission – Mean (SD)  6.9 (4.90)  7.2 (5.10)    0.88     
Days between hospital and ICU admission ‒ Mean (SD)  1.1 (2.70)  1.1 (2.40)    0.96     
Days between ICU admission and death ‒ Mean (SD)  18.5 (12.5)  8.8 (5.4)    <0.0001     
Death, n (%)  48 (72)  53 (37)  4.3 (2.28‒8.00)  <0.0001     
Laboratory test results on admission to the ICU ‒ Mean (SD)             
Leukocyte count (cells/mm310921 (4113)  12705 (6341)    0.03     
Lymphocyte count (cells/mm3957 (401)  1559 (730)    <0.0001     
C-reactive protein (mg/L)  170 (73)  106 (83)    <0.0001     
Creatinine (mg/dL)  1.49 (0.85)  1.63 (1.1)    0.35     
Blood glucose (mg/dL)  181 (70.4)  186 (89)    0.68     
PaO2/FIO2 ratio  152 (75)  232 (113)    <0.0001     
Thoracic CT alterations on admission to the ICU, n (%)             
Ground glass opacities  45 (92)  116 (92)  0.96 (0.28‒3.25)  0.96     
Consolidation  12 (24)  20 (16)  1.71 (0.76‒3.85)  0.18     
Atelectasis  8 (16)  26 (21)  0.75 (0.31‒1.79)  0.51     
Interlobular thickening  13 (26)  35 (28)  1.20 (0.56‒2.59)  0.62     
Pleural effusion  7 (14)  22 (17)  0.78(0.31‒1.98)  0.61     

CI, Confidence Interval; CVC, Central Venous Catheter; ICU, Intensive Care Unit; IUC, Indwelling Urinary Catheter; MV, Mechanical Ventilation; OR, Odds Ratio; SD, Standard Deviation; SpO2, Oxygen Saturation; WHO, World Health Organization; CT, Computed Tomography.

In the multivariate analysis, the variables that significantly increased the risk of GNB-MDR were: gender, the hospital of admission; the use of mechanical ventilation; and desaturation on hospital admission. Male gender increased the risk 2.5 fold (OR = 2.6; 95% CI 1.28‒5.33; p = 0.008), while the hospital of admission increased the risk by 3 fold (OR = 3.24; 95% CI 1.39‒7.57; p = 0.006). Mechanical ventilation increased the risk of MDR by 26 fold (OR = 25.7; 95% CI 7.26‒91; p < 0.0001) while for desaturation the risk was 2.6 fold (OR = 2.6; 95% CI 1.27‒5.74; p = 0.009).

In surveillance cultures, CR-Acinetobacter baumannii was the most frequent bacteria (22 cases – 65%) followed by CR-Enterobacterales (10 cases – 29%) and CR-Pseudomonas (2 cases – 6%). In clinical cultures, there was also a predominance of CR-Acinetobacter baumannii. In respiratory samples, 15 were CRA; 11 were Enterobacteriales resistant to 3rd and 4th generation cephalosporins; 5 were CRE, and 2 were CRP. In blood cultures, 6 were CRA; 5 were CRE; 3 were Enterobacteriales resistant to 3rd and 4th generation cephalosporins, and 2 were CRP. Finally, in urine cultures, 2 of each were CRA; CRE; and Enterobacteriales resistant to 3rd and 4th generation cephalosporins; and 1 was CRP.

38 cases had healthcare-associated infections caused by MDR-GNB: 28 were lower tract respiratory (22 Ventilator-Associated Pneumonia [VAP], 2 non-ventilator associated pneumonia, and 4 tracheobronchitis); 7 central venous Catheter-related Bloodstream Infections [CLABSI], and 3 Catheter-Associated Urinary Tract Infection (CAUTI)].

Death was higher among the cases (72%) than controls (37%) (OR = 4.3; 95% CI 2.28‒8.0; p < 0.0001).

Discussion

In this nested case-control study in a cohort of 400 adult patients with COVID-19 admitted to the ICU of 4 hospitals, the authors found that the independent factors associated with MDR-GNB were the hospital of admission, the use of mechanical ventilation, desaturation, and male gender.

Among these four factors, the hospital of admission is the only modifiable variable. This highlights the fact that Infection Prevention and Control (IPC) measures play an essential role in health facilities, making safer places to care for COVID-19 patients, and avoiding colonization, cross-transmission, and infection by MDR bacteria. Even more so because in severe cases of COVID-19 bacterial infection occurs in approximately 13%, much lower than in influenza, which is > 34%.23 This suggests that differently from influenza COVID-19 does not lead to a special propensity towards secondary bacterial infections. Taken together, the present findings strengthen the need for health services to implement and to adhere to IPC measures.

The characteristics of the work environment affect the quality of care both directly and indirectly. In the present study, the hospital with the highest risk for MDR-GNB had been closed and was reopened as a field hospital for COVID-19 patients. The structure of the hospital, as well as the ICU, was adapted to receive COVID-19 patients. In addition, there was a mixture of experienced and inexperienced healthcare workers due to a scarcity of specialized personnel. The processes of work were being organized while the patients were being hospitalized, as well as the training of medical and multidisciplinary teams on infection prevention and control: hand hygiene, the use of personal protective equipment, equipment disinfection, and environmental cleaning. Furthermore, the overcrowding of patients may also have affected the implementation of isolation measures for patients colonized with MDR, leading to potential outbreaks.16 All these factors made the ideal environment for the increase of MDR bacteria.1516

In fact, in the present study Acinetobacter baumannii was the most frequent MDR-GNB. This bacterium has the capacity to survive for prolonged periods on inanimate objects and even in hand sanitizers.24 Longer ICU stays, and frequent and prolonged use of invasive devices (mechanical ventilation, central venous catheter) favor colonization of patients by MDR bacteria. Furthermore, prolonged use of gloves without adequate hand hygiene plus the use of the same gown, discarded only after an entire work shift 25 may favor environmental colonization, especially by Acinetobacter.78101214 Many hospitals in different parts of the world also had difficulty in effectively applying infection control strategies. For example, in Parana, the southern Brazilian state in which this study was carried out, Carbapenem-Resistant A. baumannii (CRAB) increased from 7.9% in 2019 to 12.4% in 2020. The incidence of CRAB per 1000 patient days increased significantly after April 2020 and correlated strongly with the incidence of COVID-19.26

Many studies have shown the correlation between MDR and mechanical ventilation and/or the use of invasive devices in patients with COVID-19.2728 Baiou A. et al.15 observed the only risk factor independently associated with MDR infections was mechanical ventilation. A pre-pandemic meta-analysis2930 also showed that ventilation (within the previous 6 months)30 was significantly associated with the increase of MDR bacteria. Invasive procedures affecting the respiratory tract and the prolonged use of an endotracheal tube can damage the respiratory mucosa. Furthermore, the prolonged use of mechanical ventilation may make sputum dry and sticky which can also increase the risk of lung infection and injury. Interference with normal respiratory barrier and physiological functions increases the likelihood of MDR‐GNB infection and increases the incidence of ventilator‐associated pneumonia.31

In the present study, patients with desaturation on hospital admission had a higher risk for MDR bacteria. Patients with severe COVID-19 have a dysfunctional immune response, which triggers a cytokine storm that mediates widespread lung inflammation that in itself mediates damage to the lung through excessive secretion of proteases and reactive oxygen species, in addition to the direct damage caused by the virus. Together, these result in diffuse alveolar damage, including desquamation of alveolar cells, hyaline membrane formation, and pulmonary edema. This limits the efficiency of gas exchange in the lung, causing difficulty in breathing and low blood oxygen levels. The lung also becomes more vulnerable to secondary infections, among them MDR-GNB, mainly when adequate environmental cleaning and hand hygiene are lacking.32

Finally, it is not clear why MDR-GNB is more prevalent in men.313334 Some explanations may be physical function, lifestyle (higher smoking rate, for example), weaker innate and adaptive immune system, and male sex hormones. Testosterone has an immunosuppressive behavior, while females have more immunity-related genes on the X chromosome, and estrogen may help them combat and prevent different diseases.3536 Finally, men have more comorbidities when compared to women, and have been shown to have a poor medical follow-up.37 All these factors may lead to prolonged hospitalization, which may favor colonization by MDR bacteria.

The strength of this study is its multicenter design, although only hospitals in one city were included, and the relatively large number of cases and controls. However, many patients could not be included as controls due to the absence of surveillance cultures to ascertain that they were not cases

Conclusions

In conclusion, in the present study, male gender, desaturation, mechanical ventilation, and the hospital of admission were the independent factors associated with MDR-GNB in patients in ICU with COVID-19. The only modifiable factor was the hospital of admission, as the newly opened hospital posed a higher risk. Therefore, efforts are needed to improve the quality of care to patients with severe COVID-19 in order to avoid or reduce colonization and infection by MDR-GNB bacteria. These findings also suggest that better public health preparedness for emergencies in Brazil is necessary. Good quality of health care should be the focus of robust actions in order to mitigate the transmission of MDR-GNB bacteria.

References
[1]
WHO. Antimicrobial resistance. 2022. https://www.who.int/newsroom/fact-sheets/detail/antimicrobial-resistance (accessed Jun 16, 2022).
[2]
Antimicrobial Resistance Collaborators.
Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis.
[3]
TM Rawson, LSP Moore, N Zhu, N Ranganathan, K Skolimowska, M Gilchrist, et al.
Bacterial and fungal coinfection in individuals with coronavirus: a rapid review to support COVID-19. Antimicrobial Prescribing.
Clin Infect Dis, 71 (2020), pp. 2459-2468
[4]
E Badalov, L Blackler, AE Scharf, K Matsoukas, S Chawla, LP Voigt, et al.
COVID-19 double jeopardy: the overwhelming impact of the social determinants of health.
Int J Equity Health, 21 (2022), pp. 76
[5]
M Virtanen, K Terho, T Oksanen, T Kurvinen, J Pentti, M Routamaa, et al.
Patients with infectious diseases, overcrowding, and health in hospital staff.
Arch Intern Med, 171 (2011), pp. 1296-1298
[6]
M Bongiovanni, G Barilaro, U Zanini, G. Giuliani.
Impact of the COVID-19 pandemic on multidrug-resistant hospital-acquired bacterial infections.
J Hosp Infect, 123 (2022), pp. 191-192
[7]
J Li, J Wang, Y Yang, P Cai, J Cao, X Cai, et al.
Etiology and antimicrobial resistance of secondary bacterial infections in patients hospitalized with COVID-19 in Wuhan, China: A retrospective analysis.
Antimicrob Resist Infect Control, 9 (2020), pp. 153
[8]
V de Macedo, GS Santos, RN Silva, CNM Couto, C Bastos, E Viecelli, et al.
Healthcare-associated infections: a threat to the survival of patients with COVID-19 in intensive care units.
J Hosp Infect, 126 (2022), pp. 109-115
[9]
D Pasero, AP Cossu, P. Terragni.
Multi-drug resistance bacterial infections in critically ill patients admitted with COVID-19.
Microorganisms, 9 (2021), pp. 1773
[10]
RLD Costa, CDC Lamas, LFN Simvoulidis, CA Espanha, LPM Moreira, RAB Bonancim, et al.
Secondary infections in a cohort of patients with COVID-19 admitted to an intensive care unit: impact of gram-negative bacterial resistance.
Rev Inst Med Trop Sao Paulo, 64 (2022), pp. e6
[11]
G Grasselli, M Greco, A Zanella, G Albano, M Antonelli, G Bellani, et al.
Risk factors associated with mortality among patients with COVID-19 in intensive care units in Lombardy, Italy.
JAMA Intern Med, 180 (2020), pp. 1345-1355
[12]
R Cultrera, A Barozzi, M Libanore, E Marangoni, R Pora, B. Quarta, et al.
Co-infections in critically ill patients with or without COVID-19: a comparison of clinical microbial culture findings.
Int J Environ Res Public Health, 18 (2021), pp. 4358
[13]
G Grasselli, V Scaravilli, D Mangioni, L Scudeller, L Alagna, M Bartoletti, et al.
Hospital-acquired infections in critically ill patients with COVID-19.
[14]
M Ripa, L Galli, A Poli, C Oltolini, V Spagnuolo, A Mastrangelo, et al.
Secondary infections in patients hospitalized with COVID-19: incidence and predictive factors.
Clin Microbiol Infect, 27 (2021), pp. 451-457
[15]
A Baiou, AA Elbuzidi, D Bakdach, A Zaqout, KM Alarbi, AA Bintaher, et al.
Clinical characteristics and risk factors for the isolation of multi-drug-resistant Gram-negative bacteria from critically ill patients with COVID-19.
J Hosp Infect, 110 (2021), pp. 165-171
[16]
P Fernández, L Moreno, G Yagüe, E Andreu, R Jara, M. Segovia.
Colonization by multidrug-resistant microorganisms in ICU patients during the COVID-19 pandemic.
Med Intensiva (Engl Ed), 45 (2021), pp. 313-315
[17]
WHO Working Group on the Clinical Characterisation and Management of COVID-19 infection. A minimal common outcome measure set for COVID-19 clinical research.
Lancet Infect Dis, 20 (2020), pp. e192-e197
[18]
H Hagiya, R Kokado, A Ueda, H Okuno, D Morii, S Hamaguchi, et al.
Association of adverse drug events with broad-spectrum antibiotic use in hospitalized patients: a single-center study.
Intern Med, 58 (2019), pp. 2621-2625
[19]
NR Smilowitz, D Kunichoff, M Garshick, B Shah, M Pillinger, JS Hochman, et al.
C-reactive protein and clinical outcomes in patients with COVID-19.
Eur Heart J, 42 (2021), pp. 2270-2279
[20]
J Villar, L Pérez-Méndez, J Blanco, JM Añón, L Blanch, J Belda, et al.
Spanish initiative for epidemiology, stratification, and therapies for ARDS (SIESTA) network. A universal definition of ARDS: the PaO2/FiO2 ratio under a standard ventilatory setting ‒ a prospective, multicenter validation study.
Intensive Care Med, 39 (2013), pp. 583-592
[21]
J Wagner, A DuPont, S Larson, B Cash, A. Farooq.
Absolute lymphocyte count is a prognostic marker in COVID-19: a retrospective cohort review.
Int J Lab Hematol, 42 (2020), pp. 761-765
[22]
K Zhao, R Li, X Wu, Y Zhao, T Wang, Z Zheng, et al.
Clinical features in 52 patients with COVID-19 who have increased leukocyte count: a retrospective analysis.
Eur J Clin Microbiol Infect Dis, 39 (2020), pp. 2279-2287
[23]
DS Chertow, MJ. Memoli.
Bacterial coinfection in influenza: a grand rounds review.
JAMA, 309 (2013), pp. 275-282
[24]
M Asif, IA Alvi, SU. Rehman.
Insight into Acinetobacter baumannii: pathogenesis, global resistance, mechanisms of resistance, treatment options, and alternative modalities.
Infect Drug Resist, 11 (2018), pp. 1249-1260
[25]
M Polly, BL de Almeida, RP Lennon, MF Cortês, SF Costa, T. Guimarães.
Impact of the COVID-19 pandemic on the incidence of multidrug-resistant bacterial infections in an acute care hospital in Brazil.
Am J Infect Control, 50 (2022), pp. 32-38
[26]
VM de Carvalho Hessel Dias, F Tuon, P de Jesus Capelo, JP Telles, CMCB Fortaleza, Baena C. Pellegrino.
Trend analysis of carbapenem-resistant Gram-negative bacteria and antimicrobial consumption in the post-COVID-19 era: an extra challenge for healthcare institutions.
J Hosp Infect, 120 (2022), pp. 43-47
[27]
A Mazzariol, A Benini, I Unali, R Nocini, M Smania, A Bertoncelli, et al.
Dynamics of SARS-CoV2 infection and multi-drug resistant bacteria superinfection in patients with assisted mechanical ventilation.
Front Cell Infect Microbiol, 12 (2021),
[28]
AN Jamnani, M Montazeri, M Mirzakhani, M Moosazadeh, M. Haghighi.
Evaluation of bacterial coinfection and antibiotic resistance in patients with COVID-19 under mechanical ventilation.
SN Compr Clin Med, 4 (2022), pp. 19
[29]
G Chen, K Xu, F Sun, Y Sun, Z Kong, B. Fang.
Risk factors of multidrug-resistant bacteria in lower respiratory tract infections: a systematic review and meta-analysis.
Can J Infect Dis Med Microbiol, 2020 (2020),
[30]
WM Zhu, Z Yuan, HY. Zhou.
Risk factors for carbapenem-resistant Klebsiella pneumoniae infection relative to two types of control patients: a systematic review and meta-analysis.
Antimicrob Resist Infect Control, 9 (2020), pp. 23
[31]
H Ang, X. Sun.
Risk factors for multidrug-resistant Gram-negative bacteria infection in intensive care units: a meta-analysis.
Int J Nurs Pract, 24 (2018), pp. e12644
[32]
MZ Tay, CM Poh, L Rénia, PA MacAry, LFP. Ng.
The trinity of COVID-19: immunity, inflammation and intervention.
Nat Rev Immunol, 20 (2020), pp. 363-374
[33]
S Perez, GK Innes, MS Walters, J Mehr, J Arias, R Greeley, et al.
Increase in hospital-acquired carbapenem-resistant Acinetobacter baumannii infection and colonization in an acute care hospital during a surge in COVID-19 admissions ‒ New Jersey, February-July 2020.
MMWR Morb Mortal Wkly Rep, 69 (2020), pp. 1827-1831
[34]
A Karruli, F Boccia, M Gagliardi, F Patauner, MP Ursi, P Sommese, et al.
Multidrug-resistant infections and outcome of critically ill patients with coronavirus disease 2019: a single center experience.
Microb Drug Resist, 27 (2021), pp. 1167-1175
[35]
S Jaillon, K Berthenet, C. Garlanda.
Sexual dimorphism in innate immunity.
Clin Rev Allergy Immunol, 56 (2019), pp. 308-321
[36]
N Chen, M Zhou, X Dong, J Qu, F Gong, Y Han, et al.
Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study.
[37]
R Ejaz, MT Ashraf, S Qadeer, M Irfan, A Azam, S Butt, et al.
Gender-based incidence, recovery period, and mortality rate of COVID-19 among the population of district Attock, Pakistan.
Braz J Biol, 83 (2021),
Copyright © 2022. HCFMUSP
Download PDF
Article options
es en pt

¿Es usted profesional sanitario apto para prescribir o dispensar medicamentos?

Are you a health professional able to prescribe or dispense drugs?

Você é um profissional de saúde habilitado a prescrever ou dispensar medicamentos