Hospital characteristics associated with COVID-19 mortality: data from the multicenter cohort Brazilian Registry

COVID-19 case fatality ratio and survival among hospitalized adults in Goiás, 2020: a cohort study

OBJECTIVE

To describe clinical-epidemiological and therapeutic aspects and to estimate case fatality ratio and risk factors for lower in-hospital survival due to COVID-19.

METHODS

This is a retrospective cohort study conducted in the state of Goiás, Brazil, in 2020, with data obtained from the Influenza Epidemiological Surveillance Information System and through a review of clinical records and hospital. Relative risk for in-hospital death was estimated and Poisson multiple regression and Cox regression analyses were performed. Survival functions were compared using the log-rank test and represented by Kaplan-Meier curves.

RESULTS

The sample consisted of 651 adults, whose median age was 59 years, 57.0% were admitted to public hospitals, 61.1% had severe acute respiratory syndrome on admission and 72.0% had at least one comorbidity, the most frequent being hypertension , diabetes and obesity. The overall case fatality ratio was 17.5% (95% confidence interval, 95%CI 14.7; 20.6), with no significant difference between public and private hospitals. The case fatality ratio was higher in the ≥60 years age group (prevalence ratio, PR 1.26; 95%CI 1.01; 1.58), in hypertensive patients (PR 1.41; 95%CI 1.14; 1 .74) and in those undergoing intensive care (PR 2.68; 95%CI 1.13; 6.32) and mechanical ventilation (PR 11.15; 95%CI 5.53; 22.46). The median time between hospital admission and death was 10 days (interquartile range, 6-18). Survival was lower in the ≥60 years age group (adjusted hazard ratio, HR 1.93; 95%CI 1.26; 2.95) and in those undergoing mechanical ventilation (HR 10.13; 95%CI 6.03; 17. 02).

CONCLUSION

Factors related to comorbidities and severity were independent predictors of shorter in-hospital survival among patients with COVID-19.

Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods

BACKGROUND

Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the temporal data shifts on the strength of association between different predictors and adverse outcomes. This study aims to evaluate factors related to patients and hospitals in the prediction of in-hospital mortality, need for invasive mechanical ventilation (IMV), and intensive care unit (ICU) transfer throughout the pandemic waves.

METHODS

This multicenter retrospective cohort included COVID-19 patients from 39 hospitals, from March/2020 to August/2022. The pandemic was divided into waves: 10/03/2020-14/11/2020 (first), 15/11/2020-25/12/2021 (second), 26/12/2021-03/08/2022 (third). Patient-related factors included clinical, demographic, and laboratory data, while hospital-related factors covered funding sources, accreditation, academic status, and socioeconomic characteristics. Shapley additive explanation (SHAP) values derived from the predictions of a light gradient-boosting machine (LightGBM) model were used to assess potential risk factors for death, IMV and ICU.

RESULTS

Overall, 16,958 adult patients were included (median age 59 years, 54.7% men). LightGBM achieved competitive effectiveness metrics across all periods. Temporal drifts were observed due to a decrease in various metrics, such as the recall for the positive class [ICU: 0.4211 (wave 1) to 0.1951 (wave 3); IMV: 0.2089 (wave 1) to 0.0438 (wave 3); death: 0.2711 (wave 1) to 0.1175 (wave 3)]. Peripheral arterial oxygen saturation to the fraction of inspired oxygen ratio (SatO2/FiO2) at admission had great predictive capacity for all outcomes, with an optimal cut-off value for death prediction of 227.78. Lymphopenia had its association strength increased over time for all outcomes, optimal threshold for death prediction of 643 × 109/L. Thrombocytopenia was the most important feature in wave 2 (ICU); overall, values below 143,000 × 109/L were more related to death.

CONCLUSION

Data drifts were observed in all scenarios, affecting potential predictive capabilities of explainable machine learning methods. Upon admission, SatO2/FiO2 values, platelet and lymphocyte count were significant predictors of adverse outcomes in COVID-19 patients. Overall, inflammatory response markers were more important than clinical characteristics. Limitations included sample representativeness and confounding factors. Integrating the drift's knowledge into models to improve effectiveness is a challenge, requiring continuous updates and monitoring of performance in real-world applications.

CLINICAL TRIAL NUMBER

Not applicable.

Assessment of mortality due to severe SARS-CoV-2 infection in public and private intensive care units in Brazil: a multicenter retrospective cohort study

BACKGROUND

This retrospective multicenter cohort study compared characteristics and outcomes of 5,790 critically ill patients with COVID-19 in Brazil's public and private intensive care units. Patients in public intensive care units exhibited greater disease severity, more frequent use of organ support, and higher mortality rates compared to those in private intensive care units. The risk of in-hospital death was more than twice as high in public intensive care units. ■ Public intensive care unit COVID-19 patients presented with more comorbidities and higher severity at admission. ■ Public intensive care units required more invasive organ support (e.g., mechanical ventilation, vasopressors, and renal replacement therapy) but less non-invasive ventilation and high-flow nasal cannula than private intensive care units. ■ In-hospital mortality was higher in public intensive care units, with an increased risk of death even after adjusting for patient characteristics and illness severity at intensive care unit admission.

OBJECTIVE

To compare the clinical characteristics, use of organ support, and outcomes of critically ill patients with COVID-19 admitted to public and private intensive care units.

METHODS

This multicenter retrospective cohort study included patients admitted to four intensive care units from March 1, 2020, to December 31, 2021. Patients with COVID-19 admitted to public and private intensive care units were compared. The primary outcome of interest, in-hospital mortality, was assessed using a hierarchical logistic regression (multilevel) model adjusted for study site and patient characteristics.

RESULTS

A total of 5,790 patients with COVID-19 were admitted to the participating intensive care units, with 3,321 (57.3%) admitted to private hospitals and 2,469 (42.6%) admitted to public hospitals. Patients in public intensive care units were less likely to be male and had higher median SAPS III scores, Charlson Comorbidity Index values, and SOFA scores. They also required mechanical ventilation (53.1% versus 40.0%, p<0.001), vasopressors (43.1% versus 33.9%, p<0.001), and renal replacement therapy (20.3% versus. 14.5%, p<0.001) more frequently than those in private intensive care units. In contrast, patients in private intensive care units were more frequently managed with non-invasive ventilation (38.0% versus 66.8%; p<0.001) and high-flow nasal cannulas (18.3% versus 48.1%; p<0.001). The in-hospital mortality rate was significantly higher in public intensive care units (40.3%) compared to private intensive care units (16.4%) (adjusted OR=2.96; 95%CI=1.94-4.51; p<0.001).

CONCLUSION

We observed significant differences in resource utilization and mortality rates between patients with COVID-19 admitted to public and private intensive care units. Patients with COVID-19 in public care units face a higher risk of in-hospital mortality compared to those in private care units.

Clinical characteristics and outcomes of patients with COVID-19 admitted to the intensive care unit during the first and second waves of the pandemic in Brazil: a single-center retrospective cohort study

OBJECTIVE

To describe and compare the clinical characteristics and outcomes of patients admitted to intensive care units during the first and second waves of the COVID-19 pandemic.

METHODS

In this retrospective single-center cohort study, data were retrieved from the Epimed Monitor System; all adult patients admitted to the intensive care unit between March 4, 2020, and October 1, 2021, were included in the study. We compared the clinical characteristics and outcomes of patients admitted to the intensive care unit of a quaternary private hospital in São Paulo, Brazil, during the first (May 1, 2020, to August 31, 2020) and second (March 1, 2021, to June 30, 2021) waves of the COVID-19 pandemic.

RESULTS

In total, 1,427 patients with COVID-19 were admitted to the intensive care unit during the first (421 patients) and second (1,006 patients) waves. Compared with the first wave group [median (IQR)], the second wave group was younger [57 (46-70) versus 67 (52-80) years; p<0.001], had a lower SAPS 3 Score [45 (42-52) versus 49 (43-57); p<0.001], lower SOFA Score on intensive care unit admission [3 (1-6) versus 4 (2-6); p=0.018], lower Charlson Comorbidity Index [0 (0-1) versus 1 (0-2); p<0.001], and were less frequently frail (10.4% versus 18.1%; p<0.001). The second wave group used more noninvasive ventilation (81.3% versus 53.4%; p<0.001) and high-flow nasal cannula (63.2% versus 23.0%; p<0.001) during their intensive care unit stay. The intensive care unit (11.3% versus 10.5%; p=0.696) and in-hospital mortality (12.3% versus 12.1%; p=0.998) rates did not differ between both waves.

CONCLUSION

In the first and second waves, patients with severe COVID-19 exhibited similar mortality rates and need for invasive organ support, despite the second wave group being younger and less severely ill at the time of intensive care unit admission.

Myocardial Injury and Prognosis in Hospitalized COVID-19 Patients in Brazil: Results From The Brazilian COVID-19 Registry

BACKGROUND

Cardiovascular complications of COVID-19 are important aspects of the disease's pathogenesis and prognosis. Evidence on the prognostic role of troponin and myocardial injury in Latin American hospitalized COVID-19 patients is still scarce.

OBJECTIVES

To evaluate myocardial injury as independent predictor of in-hospital mortality and invasive mechanical ventilation support in hospitalized patients, from the Brazilian COVID-19 Registry.

METHODS

This cohort study is a substudy of the Brazilian COVID-19 Registry, conducted in 31 Brazilian hospitals of 17 cities, March-September 2020. Primary outcomes included in-hospital mortality and invasive mechanical ventilation support. Models for the primary outcomes were estimated by Poisson regression with robust variance, with statistical significance of p<0.05.

RESULTS

Of 2,925 patients (median age of 60 years [48-71], 57.1% men), 27.3% presented myocardial injury. The proportion of patients with comorbidities was higher among patients with cardiac injury (median 2 [1-2] vs. 1 [0-2]). Patients with myocardial injury had higher median levels of brain natriuretic peptide, lactate dehydrogenase, creatine phosphokinase, N-terminal pro-brain natriuretic peptide, and C-reactive protein than patients without myocardial injury. As independent predictors, C-reactive protein and platelet counts were related to the risk of death, and neutrophils and platelet counts were related to the risk of invasive mechanical ventilation support. Patients with high troponin levels presented a higher risk of death (RR 2.03, 95% CI 1.60-2.58) and invasive mechanical ventilation support (RR 1.87, 95% CI 1.57-2.23), when compared to those with normal troponin levels.

CONCLUSION

Cardiac injury was an independent predictor of in-hospital mortality and the need for invasive mechanical ventilation support in hospitalized COVID-19 patients.

JUE Insight: Is hospital quality predictive of pandemic deaths? Evidence from US counties

In the large literature on the spatial-level correlates of COVID-19, the association between quality of hospital care and outcomes has received little attention to date. To examine whether county-level mortality is correlated with measures of hospital performance, we assess daily cumulative deaths and pre-crisis measures of hospital quality, accounting for state fixed-effects and potential confounders. As a measure of quality, we use the pre-pandemic adjusted five-year penalty rates for excess 30-day readmissions following pneumonia admissions for the hospitals accessible to county residents based on ambulance travel patterns. Our adjustment corrects for socio-economic status and down-weighs observations based on small samples. We find that a one-standard-deviation increase in the quality of local hospitals is associated with a 2% lower death rate (relative to the mean of 20 deaths per 10,000 people) one and a half years after the first recorded death.