Current best evidence for clinical care (more info)
BACKGROUND: This study aimed to develop mortality-prediction models for patients with Coronavirus disease 2019 (COVID-19).
METHODS: The training cohort were consecutive patients with COVID-19 in the First People's Hospital of Jiangxia District in Wuhan from January 7, 2020 to February 11, 2020. We selected baseline clinical and laboratory data through the stepwise Akaike information criterion and ensemble XGBoost model to build mortality-prediction models. We then validated these models by randomly collecting COVID-19 patients in the Infection department of Union Hospital in Wuhan from January 1, 2020, to February 20, 2020.
RESULTS: 296 patients with COVID-19 were enrolled in the training cohort, 19 of whom died during hospitalization and 277 were discharged from the hospital. The clinical model developed with age, history of hypertension and coronary heart disease showed AUC of 0.88 (95% CI, 0.80-0.95); threshold, -2.6551; sensitivity, 92.31%; specificity, 77.44% and negative predictive value (NPV), 99.34%. The laboratory model developed with age, high-sensitivity C-reactive protein (hsCRP), peripheral capillary oxygen saturation (SpO2), neutrophil and lymphocyte count, D-dimer, aspartate aminotransferase (AST) and glomerular filtration rate (GFR) had a significantly stronger discriminatory power than the clinical model (p=0.0157), with AUC of 0.98 (95% CI, 0.92-0.99); threshold, -2.998; sensitivity, 100.00%; specificity, 92.82% and NPV, 100.00%. In the subsequent validation cohort (N=44), the AUCs (95% CI) were 0.83 (0.68, 0.93) and 0.88 (0.75, 0.96) for clinical model and laboratory model, respectively.
CONCLUSIONS: We developed two predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan and validated in patients from another center.
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I do not know whether the manuscript passed peer review with all the strict requisites. Small sample size.