Abstract
Objectives
To analyze the clinical characteristics and outcomes of admitted patients with the hospital- versus community-manifested COVID-19 and to evaluate the risk factors related to mortality in the first population.
Methods
This retrospective cohort included consecutive adult patients with COVID-19, hospitalized between March and September 2020. The demographic data, clinical characteristics, and outcomes were extracted from medical records. Patients with hospital-manifested COVID-19 (study group) and those with community-manifested COVID-19 (control group) were matched by the propensity score model. Logistic regression models were used to verify the risk factors for mortality in the study group.
Results
Among 7,710 hospitalized patients who had COVID-19, 7.2% developed symptoms while admitted for other reasons. Patients with hospital-manifested COVID-19 had a higher prevalence of cancer (19.2% vs 10.8%) and alcoholism (8.8% vs 2.8%) than patients with community-manifested COVID-19 and also had a higher rate of intensive care unit requirement (45.1% vs 35.2%), sepsis (23.8% vs 14.5%), and death (35.8% vs 22.5%) (P <0.05 for all). The factors independently associated with increased mortality in the study group were increasing age, male sex, number of comorbidities, and cancer.
Conclusion
Hospital-manifested COVID-19 was associated with increased mortality. Increasing age, male sex, number of comorbidities, and cancer were independent predictors of mortality among those with hospital-manifested COVID-19 disease.
Keywords: COVID-19, Hospital-manifested infection, Clinical characteristics, Outcomes, Risk factors, In-hospital mortality
Introduction
Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, a large part of the population avoided or delayed routine and nonemergency medical care for fear of in-hospital infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. However, several patients admitted due to other conditions manifested COVID-19 during their hospital stay.
Despite an increasing understanding of COVID-19, establishing whether an infection is hospital-acquired remains challenging. For SARS-CoV-2, hospital-acquired infections are frequently defined by clinical assessment of the time of admission and the first positive test or symptom onset [2]. As the incubation period is very variable and the exact moment of infection is unknown, studying patients who manifested COVID-19 during the hospital stay for other reasons is essential, and studies on the topic are scarce. In addition, data on the prevalence of this condition and its prognosis in Latin American patients is still lacking. Thus, the aims of this study were (i) to analyze the clinical characteristics and outcomes of in-hospital patients with hospital- versus community-manifested COVID-19, as well as (ii) to evaluate the risk factors related to mortality in the first population.
Methods
Study design and ethics
This study is part of a multicentric retrospective cohort, entitled the Brazilian COVID-19 Registry, which involved 34 Brazilian hospitals. The study protocol was approved by the National Commission for Research Ethics (CAAE: 30350820.5.1001.0008).
Study subjects
Consecutive adult patients (aged ≥18 years) with laboratory-confirmed COVID-19 [3] admitted to any of the participating hospitals from March 1 to September 30, 2020 were enrolled. The total duration of data collection was approximately seven months, although this duration varied for each hospital.
Hospital-manifested COVID-19 was defined as patients who were admitted for other reasons and had a positive test for COVID-19 at any moment of the hospital stay (study group). Community-manifested COVID-19 was defined as patients admitted due to COVID-19 (control group).
Data collection
Demographic data, clinical characteristics, and outcomes (for details, see Table S1) were collected from the medical records by trained researchers to the Research Electronic Data Capture (REDCap®) electronic platform [4,5], hosted at the Telehealth Center of the University Hospital of the Universidade Federal de Minas Gerais [6]. To ensure reliability and monitor data quality, the database was routinely audited.
The admission causes were stratified into categories based on both the Global Health Estimates [7] and the Global Burden of Diseases [8]. First, we created broad categories (e.g. malignant neoplasms and cardiovascular, infectious, and endocrine diseases) and subdivided these into specific diseases (Table S2). Then, each patient was assigned to one of these categories. Some patients were admitted for specific procedures (e.g. surgery, solid organ transplantation, or plasmapheresis), and others for propaedeutics or diagnostic evaluation due to several symptoms or syndromes (e.g. anasarca, fever of unknown origin, oliguria/anuria, and wasting syndrome). These categories were added as well (Table S2).
Statistical analysis
Patients with hospital-manifested COVID-19 (study group) were compared with matched controls (community-manifested) using the t- or Wilcoxon tests for continuous variables (according to data distribution) and chi-square or Fisher's exact tests for categorical variables. The descriptive analysis was presented by the median, interquartile range (IQR), numbers, and percentages.
For comparison purposes, the propensity score model was estimated by the logistic regression (including age, sex, number of comorbidities [hypertension, diabetes mellitus, obesity, coronary artery disease, heart failure, atrial fibrillation or flutter, cirrhosis, chronic obstructive pulmonary disease, cancer, and previous stroke], and hospital) to adjust for potential confounding variables and match the patients of the control group (1:1). The control group comprised those with the closest propensity score with patients with hospital-manifested COVID-19 (within 0.17 standard deviations of the logit of the propensity score on a scale from 0 to 1.00), using the MatchIt package in R software.
The demographic and clinical characteristics were assessed as risk factors for mortality among patients with hospital-manifested COVID-19 using the logistic regression analysis. In the first model, the variable “number of comorbidities” was included. In the second model, we considered the different comorbidities separately and did not include the variable “number of comorbidities”. In both models, we excluded the duplicates of the cause of hospitalization to prevent the comorbidity from being entered twice in the model.
All statistical analyses were performed using R software (version 4.0.2). The significance level for the two-tailed P-value was set at ≤0.05.
Results
From 7,710 hospitalized patients with COVID-19, 537 (7.2%) had hospital-manifested COVID-19, and 537 were selected as matched controls. The median age was 62 (interquartile range 47.2-74.0) years, and the majority of patients were female (53.3%). Patients with hospital-manifested COVID-19 had a higher prevalence of cancer (19.2% vs 10.8%, P<0.001), alcoholism (8.8% vs 2.8%, P<0.001), chronic kidney disease (3.5% vs 1.7%, P= 0.009), and cirrhosis (2.6% vs 0.9%, P= 0.037) than patients with community-manifested COVID-19 (for more details, see Table 1 ).
Table 1.
Demographic and clinical characteristics of the control group and study group.
| Characteristics | Control group | Study group | P-valueb |
|---|---|---|---|
| N = 537a | N = 537a | ||
| Age (years) | 62.0 (48.0, 75.0) | 62.0 (47.0, 74.0) | 0.257 |
| Male sex | 252 (46.9%) | 250 (46.6%) | 0.903 |
| Cardiovascular disease | |||
| Hypertension | 312 (58.1%) | 295 (54.9%) | 0.295 |
| Heart failure | 53 (9.9%) | 50 (9.3%) | 0.756 |
| Coronary artery disease | 49 (9.1%) | 55 (10.2%) | 0.536 |
| Atrial fibrillation/flutter | 30 (5.6%) | 31 (5.8%) | 0.895 |
| Stroke | 35 (6.5%) | 45 (8.4%) | 0.245 |
| Respiratory disease | |||
| Chronic obstructive pulmonary disease | 37 (6.9%) | 28 (5.2%) | 0.249 |
| Asthma | 30 (5.6%) | 15 (2.8%) | 0.022 |
| Metabolic disease | |||
| Diabetes mellitus | 181 (33.7%) | 148 (27.6%) | 0.029 |
| Obesity (body mass index>30kg/m2) | 90 (16.8%) | 48 (8.9%) | <0.001 |
| Other conditions | |||
| Chronic kidney disease | 47 (8.8%) | 74 (13.8%) | 0.009 |
| Cancer | 58 (10.8%) | 103 (19.2%) | <0.001 |
| Rheumatologic disease | 9 (1.7%) | 19 (3.5%) | 0.055 |
| Cirrhosis | 5 (0.9%) | 14 (2.6%) | 0.037 |
| Previous transplantation | 11 (2.0%) | 18 (3.4%) | 0.188 |
| HIV infection | 11 (2.0%) | 11 (2.0%) | >0.999 |
| Comorbidities (total number) | 0.684 | ||
| 0 | 125 (23.3%) | 133 (24.8%) | |
| 1 | 145 (27.0%) | 151 (28.1%) | |
| 2 | 154 (28.7%) | 139 (25.9%) | |
| 3 | 72 (13.4%) | 83 (15.5%) | |
| ≥4 | 41 (7.6%) | 31 (5.8%) | |
| Toxic habits | |||
| Alcoholism | 15 (2.8%) | 47 (8.8%) | <0.001 |
| Smoking | 109 (20.3%) | 129 (24.0%) | 0.142 |
n (%), median (interquartile range);
Pearson chi-square test, Wilcoxon rank sum test, Fisher's exact test.
The most common causes of hospitalization (Table 2 ) in the study group were cardiovascular diseases (20.0%), propaedeutics evaluation (19.5%), gastrointestinal diseases (10.0%), and procedures (10.0%). Regarding the clinical outcomes, the patients in this group had a higher mortality rate (35.8% vs 22.5%, P<0.001), intensive care unit admission rate (45.1% vs 35.2%, P<0.001), frequency of sepsis (23.8% vs 14.5%, P= 0.010), and bleeding (4.1% vs 1.5%, P= 0.010) than the control group (Table 3 ). On the other hand, there were no statistically significant differences in the need for invasive mechanical ventilation and dialysis, as well as the incidence of venous thromboembolism and acute heart failure, between groups (Table 3).
Table 2.
Cause of admission for the study group (N = 537).
| Characteristics | N (%) |
|---|---|
| Cardiovascular diseases | 104 (20.0%) |
| Propaedeutics/diagnostic evaluation | 101 (19.5%) |
| Gastrointestinal diseases | 52 (10.0%) |
| Procedures | 52 (10.0%) |
| Genitourinary diseases | 42 (8.1%) |
| Malignant neoplasms | 31 (6.0%) |
| Injuries | 30 (5.8%) |
| Musculoskeletal diseases | 26 (5.0%) |
| Skin and soft tissue diseases | 19 (3.7%) |
| Neurological conditions | 17 (3.3%) |
| Diabetes mellitus | 16 (3.1%) |
| Respiratory diseases | 15 (2.9%) |
| Mental and substance use disorders | 7 (1.3%) |
| Infectious and parasitic diseases | 5 (1.0%) |
| Endocrine, blood, immune disorders | 2 (0.4%) |
Median (interquartile range).
Table 3.
Clinical outcomes of control and study groups.
| Characteristics | Control group | Study group | P-valueb |
|---|---|---|---|
| N = 537a | N = 537a | ||
| Hospital length of stay | 8.0 (5.0, 15.0) | 12.0 (5.0, 21.0) | <0.001 |
| ICU | 189 (35.2%) | 242 (45.1%) | <0.001 |
| Time of ICU admission | 1.0 (0.0, 3.0) | 1.0 (0.0, 5.0) | 0.026 |
| Days in the ICU | 9.0 (4.0, 19.0) | 7.0 (3.0, 15.0) | 0.100 |
| Mechanical ventilation | 145 (28.0%) | 166 (32.7%) | 0.103 |
| Dialysis | 68 (12.7%) | 85 (15.8%) | 0.138 |
| Sepsis | 78 (14.5%) | 128 (23.8%) | <0.001 |
| Disseminated intravascular coagulation | 2 (0.4%) | 4 (0.7%) | 0.687 |
| Acute cardiac insufficiency | 17 (3.2%) | 24 (4.5%) | 0.265 |
| Acute myocardial infarction | 5 (0.9%) | 10 (1.9%) | 0.194 |
| Myocarditis | 2 (0.4%) | 1 (0.2%) | >0.999 |
| Hemorrhage | 8 (1.5%) | 22 (4.1%) | 0.010 |
| Vascular thrombosis | 19 (3.5%) | 26 (4.8%) | 0.286 |
| Venous thromboembolism | 18 (3.4%) | 24 (4.5%) | 0.345 |
| Arterial thrombosis | 1 (0.2%) | 2 (0.4%) | >0.999 |
| In-hospital mortality | 121 (22.5%) | 192 (35.8%) | <0.001 |
n (%); Median (interquartile range);
Pearson chi-squared test; Wilcoxon rank sum test; Fisher's exact test. ICU, intensive care unit.
In the first multivariable logistic regression analysis for in-hospital mortality (Table 4 ), the following variables were shown to be independent predictors: older age (odds ratio [OR] 1.04, 95% confidence interval [CI] 1.02-1.05) and a higher number of comorbidities (OR 1.34, 95% CI 1.1-1.64). In the second analysis (Table 5 ), the factors independently associated with mortality were increasing age (OR 1.04, 95% CI 1.02-1.05), male sex (OR 1.51, 95% CI 1.01-2.27), and underlying cancer (OR 2.34, 95% CI 1.33-4.15).
Table 4.
Risk factors for in-hospital mortality of the patients with hospital-manifested COVID-19 (study group, first multivariate logistic regression model).
| Characteristics | Odds ratio (95% confidence interval) | P-value |
|---|---|---|
| Male sex | 1.43 (0.97-2.13) | 0.0735 |
| Age (years) | 1.04 (1.02-1.05) | <0.0001 |
| Number of comorbidities | 1.34 (1.1-1.64) | 0.0035 |
| Causes of hospital admission | ||
| Malignant neoplasms | 1.79 (0.71-4.44) | 0.2126 |
| Musculoskeletal diseases | 1.83 (0.73-4.72) | 0.2030 |
| Digestive diseases | 0.79 (0.37-1.66) | 0.5394 |
| Cardiovascular diseases | 1.14 (0.61-2.13) | 0.6871 |
| Procedures | 0.79 (0.33-1.82) | 0.5874 |
| Injuries | 2.04 (0.8-5.18) | 0.1320 |
| Respiratory diseases | 1.09 (0.32-3.57) | 0.8825 |
| Neurological conditions | 1.05 (0.34-3.16) | 0.9361 |
| Skin and soft tissue diseases | 1.38 (0.42-4.18) | 0.5740 |
| Genitourinary diseases | 1.01 (0.44-2.26) | 0.9893 |
| Diabetes mellitus | 0.54 (0.14-1.75) | 0.3296 |
| Mental and substance use disorders | 0.48 (0.02-3.73) | 0.5422 |
| Infectious and parasitic disease | 0.75 (0.04-5.83) | 0.8055 |
Table 5.
Risk factors for in-hospital mortality of the patients with hospital-manifested COVID-19 (study group, second multivariate logistic regression model).
| Characteristics | Odds ratio (95% confidence interval) | P-value |
|---|---|---|
| Male sex | 1.55 (1.01-2.27) | 0.0465 |
| Age (years) | 1.04 (1.02-1.05) | <0.0001 |
| Manifest | ||
| Malignant neoplasm | 1.94 (0.76-4.89) | 0.159 |
| Musculoskeletal diseases | 2.03 (0.8-5.3) | 0.1378 |
| Digestive diseases | 0.76 (0.35-1.62) | 0.4782 |
| Cardiovascular diseases | 1.18 (0.58-2.41) | 0.6412 |
| Procedures | 0.7 (0.28-1.66) | 0.4255 |
| Injuries | 2.03 (0.79-5.24) | 0.1405 |
| Respiratory diseases | 1.05 (0.3-3.45) | 0.9415 |
| Neurological conditions | 1.16 (0.36-3.57) | 0.8004 |
| Skin and soft tissue disease | 1.37 (0.41-4.22) | 0.5939 |
| Genitourinary diseases | 1.11 (0.48-2.51) | 0.8068 |
| Diabetes mellitus | 0.54 (0.13-1.81) | 0.3395 |
| Mental and substance use disorders | 0.6 (0.03-4.44) | 0.6598 |
| Infectious and parasitic disease | 0.87 (0.04-6.95) | 0.9085 |
| Comorbidities | ||
| Hypertension | 1.47 (0.88-2.44) | 0.1384 |
| Coronary artery disease | 1.15 (0.46-2.88) | 0.7615 |
| Heart failure | 0.88 (0.35-2.13) | 0.7688 |
| Atrial fibrillation/flutter | 1.34 (0.45-4.08) | 0.6011 |
| Stroke | 1.11 (0.45-2.77) | 0.8136 |
| Chronic obstructive pulmonary disease | 1.23 (0.52-2.95) | 0.6406 |
| Diabetes mellitus | 1.34 (0.85-2.11) | 0.2119 |
| Obesity (body mass index>30 kg/m2) | 1.32 (0.65-2.63) | 0.4354 |
| Cirrhosis | 1.05 (0.3-3.4) | 0.9397 |
| Cancer | 2.34 (1.33-4.15) | 0.0034 |
Discussion
In this study from a large cohort of Brazilian patients, 7.2% had hospital-manifested COVID-19. The patients with hospital-manifested symptoms had a higher prevalence of cancer, alcoholism, chronic kidney disease, and cirrhosis than the controls. Cardiovascular diseases, propaedeutics, gastrointestinal diseases, and procedures were the most common causes of hospitalization in that group. Furthermore, patients with hospital-manifested COVID-19 were associated with a higher rate of admission to the intensive care unit, frequency of sepsis, bleeding, and in-hospital mortality. Increasing age, male sex, number of comorbidities, and cancer were independent predictors of mortality among patients with hospital-manifested COVID-19.
This study is innovative in assessing the most frequent causes of hospital admission, prognosis, and risk factors for mortality in patients with hospital-manifested COVID-19 in Latin America. However, the cut-off to define hospital-acquired COVID-19 is still a matter of debate. Hospital-acquired COVID-19 is frequently defined by comparing the time of admission and the first positive laboratory test or the symptom onset [2]. National Health Service, for example, classifies hospital-acquired infections as ‘indeterminate’, ‘probable’, or ‘definite’ for symptom onset or a positive test after 3-7 days, 8-14 days, and >14 days after admission, respectively [9]. If the delay is much greater than the incubation period, the infection is likely hospital-acquired [1,2]. Thus, the number of patients with hospital-acquired SARS-CoV-2 infection will depend on the definition used, with imprecision driven by the unobservable nature of the infection and the wide variation in the incubation periods [2]. In the absence of universal testing of all patients at admission, we opted to use the cautious definition of hospital-manifested COVID-19.
In fact, it is quite probable that we have had anecdotal information of patients (from different studies) who were infected before hospital admission and those patients were misclassified as hospital-acquired COVID-19. A recent study considered nosocomial those patients who developed symptoms and had a positive SARS-CoV-2 polymerase chain reaction test results more than 7 days after admission. In this study, only 5.7% of patients tested positive for COVID-19 after 14 days from hospital presentation [10]. On the other hand, in low-income countries, such as Brazil, there is no standardized protocol for testing inpatients for COVID-19. This heterogeneity has made it even more challenging to identify positive COVID-19 cases because many institutions only test hospitalized patients who developed symptoms or have been in close contact with a confirmed COVID-19 case (e.g., in the same ward) [11]. This may explain the prevalence (7.2%) of patients with hospital-manifested COVID-19 in our cohort.
The exact explanation for the number of patients infected in these hospital settings is still unknown and probably multifactorial, which could be related to the large numbers of inpatients, limited facilities for isolation of cases, reduced capacity for rapid and reliable diagnostic testing, and inadequate use of personal protective equipment, in addition to no suspicion of COVID-19, in patients with atypical symptoms or those who are asymptomatic [12].
Our results showed that patients with hospital-manifested COVID-19 had a greater prevalence of cancer, alcoholism, chronic kidney disease, and cirrhosis than controls. Elkrief et al. [13] described a high rate of hospital-acquired COVID-19 in patients with cancer. Our study also identified that patients with cancer are at a particularly increased risk for mortality from COVID-19, possibly due to the use of immunosuppressants and other associated factors, such as undernutrition and prolonged time of hospitalization.
The main causes of hospitalization unrelated to COVID-19 observed in our study, including cardiovascular diseases, propaedeutics, procedures, and gastrointestinal diseases, are possibly related to the fact that the majority of the centers are general hospitals, with the exception of one specialized hospital in trauma. However, there are no studies with a similar hospital profile to ours to enable a direct comparison of the frequency of the causes of hospitalization.
As aforementioned, the few available evidence refer to hospital-acquired SARS-CoV-2 infection using different criteria. Although the results are useful to understand the problem in several settings, they cannot be used to directly compare with our data. Shiwani et al. [14], for example, demonstrated that despite the baseline characteristics of hospital-acquired and community-acquired COVID-19 in the United Kingdom displaying significant differences, the rates of mortality were similar between these patients. Khan et al. [15] identified that among 173 patients, 19 (11.0%) had nosocomial infection, and in line with this, Shiwani et al. [14] did not observe differences in 30-day mortality rates (e.g., patients admitted with suspected COVID-19 [21.1%] vs patients with incidental COVID-19 [17.6%] vs patients with nosocomial COVID-19 [21.6%], P = 0.755). In contrast, in the current analysis, patients with hospital-manifested COVID-19 had a higher mortality rate than controls (35.8% vs 22.5%, P <0.001). Our findings are in accordance with the initial hypothesis that patients who manifest COVID-19 while admitted for other conditions would have a higher risk for severe outcomes and subsequent mortality because of the delayed medical assistance in a pandemic scenery and the presence of underlying comorbidities, which makes these patients more vulnerable to COVID-19.
Overall, age and the presence of underlying diseases are established predictors of severity and mortality in patients with community-manifested COVID-19. In a previous analysis, our group reported a higher in-hospital mortality risk in older adults, those with a higher number of comorbidities, those with lower oxygen saturation-fraction of inspired oxygen ratio, and those with higher levels of C-reactive protein and platelet count, in addition to higher heart rate and blood urea nitrogen [16].
These factors have also been shown to be associated with those defined as hospital-acquired COVID-19. Carter et al. [17] described a higher mortality rate in older patients with frailty, renal failure, and increased C-reactive protein levels. In addition, Kuderer et al. [18] observed that the independent factors associated with increased mortality were increasing age, male sex, smoking, number of comorbidities, and active cancer. Elkrief et al. [13] observed older age and higher cancer stage to be independent risk factors for mortality in this population. All these findings are in agreement with ours, which indicated that advancing age, male sex, number of comorbidities, and cancer as risk factors for mortality in the study group.
To the best of our knowledge, there is a lack of studies comparing community-manifested versus hospital-manifested in Latin America. A large breadth of our understanding of COVID-19 comes from studies performed in Europe or North America, despite Brazil having been an important epicenter of the pandemic. It is known that scientific information is a critical source for directing medical practice, as well as guiding the national government policy, but the differences in the demographics between the populations and the health systems’ characteristics largely influenced the results [14]. Thus, this study is an important part of such data in Latin America. It also has other strengths, such as its multicenter design, sample size, and matched analysis.
Regarding the study limitations, the primary cause for hospitalization might have been a COVID-19 manifestation that was not acknowledged at admission in some instances (or atypical symptoms attributed especially to the presence of chronic diseases [19]). COVID-19 also may cause decompensation of baseline medical conditions contributing to hospitalization, even when it is not considered the primary cause [19]. Future studies with larger sample sizes should include the aforementioned National Health Service surveillance criteria for hospital-acquired infections [9].
Conclusion
Hospital-manifested COVID-19 was associated with worse outcomes than community-acquired COVID-19. Older age, male sex, a higher number of comorbidities, and cancer are risk factors for in-hospital mortality in patients with hospital-manifested COVID-19. Our findings reinforce the importance of early diagnoses, show the necessity of maximizing the prevention of intra-hospital infection, and emphasize the clinical care for those with hospital-manifested COVID-19.
Funding
This study was supported, in part, by Minas Gerais State Agency for Research and Development (Fundação de Amparo à Pesquisa do Estado de Minas Gerais - FAPEMIG [grant number APQ-01154-21]), National Institute of Science and Technology for Health Technology Assessment (Instituto de Avaliação de Tecnologias em Saúde – IATS)/National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq [grant number 465518/2014-1]), CAPES Foundation (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior [grant number 88887.507149/2020-00]), and Ministry of Education (Ministério da Educação - MEC [grant number 27.849*8). PDP is supported by the CAPES Foundation (grant number 88887.629451/2021000).
Ethics: informed consent
Individual written informed consent was waived due to the severity of the situation and the use of deidentified data, based on medical chart review only.
Author contributions
Substantial contributions to the conception or design of the work: PDP, MCP, and MSM. Substantial contributions to the acquisition, analysis, or interpretation of data for the work: PDP, VMRG, MCAN, MCPBL, MCP, TCAF, and MSM. Drafted the work: PDP, VMRG, MCAN, MVRSS, TCAF, and MSM. Revised the manuscript critically for important intellectual content: all authors. Final approval of the version to be published: all authors.
Consent for publication
All authors gave consent for publication of the work.
Availability of data and materials
Data is available upon reasonable request.
Declaration of competing interest
The authors have no competing interests to declare.
Acknowledgments
The authors would like to thank the hospitals that took part of this collaboration and supported this project: Hospital Bruno Born; Hospital Cristo Redentor; Hospital das Clínicas da Faculdade de Medicina de Botucatu; Hospital das Clínicas da Universidade Federal de Minas Gerais (UFMG); Hospital das Clínicas da Universidade Federal de Pernambuco; Hospital de Clínicas de Porto Alegre; Hospital Santo Antônio; Hospital Eduardo de Menezes; Hospital João XXIII; Hospital Júlia Kubitschek; Hospital Mãe de Deus; Hospital Márcio Cunha; Hospital Mater Dei Betim-Contagem; Hospital Mater Dei Contorno; Hospital Mater Dei Santo Agostinho; Hospital Metropolitano Dr. Célio de Castro; Hospital Metropolitano Odilon Behrens; Hospital Moinhos de Vento; Hospital Nossa Senhora da Conceição; Hospital Regional Antônio Dias; Hospital Regional de Barbacena Dr. José Américo; Hospital Regional do Oeste; Hospital Risoleta Tolentino Neves; Hospital Santa Cruz; Hospital Santa Rosália; Hospital São João de Deus; Hospital São Lucas da PUCRS; Hospital Semper; Hospital SOS Cárdio; Hospital Tacchini; Hospital Unimed BH; Hospital Universitário Canoas; Hospital Universitário Ciências Médicas; and Hospital Universitário de Santa Maria. The authors also thank all the clinical staff at those hospitals who cared for the patients and all undergraduate students who helped with data collection.
Footnotes
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ijid.2023.02.012.
Appendix. Supplementary materials
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Data Availability Statement
Data is available upon reasonable request.