Scientific Reports · 2021 · 23 citations · 28 references
ComputingInfectious DiseasesClinical Decision SupportClinical EpidemiologyEpidemiologyMortality ModelingPredictive Health ModelingRisk PredictionLogistic RegressionMortality PredictionPredictive Mortality ModelingPredictive Outcome ModelingPrognostic Prediction ModelsSequential Backward Selection
Patients with coronavirus disease 2019 (COVID-19) can have increased risk of mortality shortly after intubation. The aim of this study is to develop a model using predictors of early mortality after intubation from COVID-19. A retrospective study of 1945 intubated patients with COVID-19 admitted to 12 Northwell hospitals in the greater New York City area was performed. Logistic regression model using backward selection was applied. This study evaluated predictors of 14-day mortality after intubation for COVID-19 patients. The predictors of mortality within 14 days after intubation included older age, history of chronic kidney disease, lower mean arterial pressure or increased dose of required vasopressors, higher urea nitrogen level, higher ferritin, higher oxygen index, and abnormal pH levels. We developed and externally validated an intubated COVID-19 predictive score (ICOP). The area under the receiver operating characteristic curve was 0.75 (95% CI 0.73-0.78) in the derivation cohort and 0.71 (95% CI 0.67-0.75) in the validation cohort; both were significantly greater than corresponding values for sequential organ failure assessment (SOFA) or CURB-65 scores. The externally validated predictive score may help clinicians estimate early mortality risk after intubation and provide guidance for deciding the most effective patient therapies.
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Clinical Characteristics of Coronavirus Disease 2019 in China
Wei‐jie Guan, Zhengyi Ni, Yu Hu et al. · New England Journal of Medicine · 2020 · 31.2K citations · Full text
Veterinary Medicine, Infectious Diseases, Internal Medicine +10