Revista brasileira de enfermagem · 2026
Analysis of factors associated with death in critically ill patients with COVID-19.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Cunha Maximina de Barros, Barbosa Dulce Aparecida, Fonseca Cassiane Dezoti da, Moitinho Matheus Santos, Kawagoe Julia Yaeko
Аннотация
OBJECTIVES: to assess the factors associated with death in critically ill COVID-19 patients.
METHODS: retrospective cohort of critically ill patients admitted to the Intensive Care Unit and infected with SARS-CoV-2, followed for 30 days, ≥ 18 years old, with a positive nasopharyngeal RT-PCR test for SARS-CoV-2. Descriptive analysis regarding the outcome, chi-square test, and backward stepwise multiple logistic regression model were used to describe predictor variables of sociodemographic and clinical characteristics.
RESULTS: 715 patients were analyzed, of whom 70.8% (n=507) died and 29.1% (n=209) survived. In the multivariate analysis, death outcome dependent variables, such as age range of 61-80 years, > 80 years, invasive mechanical ventilation, dialysis, corticosteroids, vancomycin, platelets after 24 hours, and other antibiotics, were identified as risk factors, and the ROC curve (AUC=0.904) predicted the proposed model.
CONCLUSIONS: the multivariate model showed good discriminatory capacity, identifying independent factors associated with death in critically ill COVID-19 patients.
METHODS: retrospective cohort of critically ill patients admitted to the Intensive Care Unit and infected with SARS-CoV-2, followed for 30 days, ≥ 18 years old, with a positive nasopharyngeal RT-PCR test for SARS-CoV-2. Descriptive analysis regarding the outcome, chi-square test, and backward stepwise multiple logistic regression model were used to describe predictor variables of sociodemographic and clinical characteristics.
RESULTS: 715 patients were analyzed, of whom 70.8% (n=507) died and 29.1% (n=209) survived. In the multivariate analysis, death outcome dependent variables, such as age range of 61-80 years, > 80 years, invasive mechanical ventilation, dialysis, corticosteroids, vancomycin, platelets after 24 hours, and other antibiotics, were identified as risk factors, and the ROC curve (AUC=0.904) predicted the proposed model.
CONCLUSIONS: the multivariate model showed good discriminatory capacity, identifying independent factors associated with death in critically ill COVID-19 patients.