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 included consecutive COVID-19 patients at the First People's Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020.
RESULTS: A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80-.95); threshold, -2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92-.99); threshold, -2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68-.93) and 0.88 (.75-.96) for the clinical model and laboratory model, respectively.
CONCLUSIONS: We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.
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Accurate prediction models of in-hospital mortality using baseline clinical and lab parameters would be invaluable for clinical management and triaging of cases, especially if hospital systems become overwhelmed with cases. As the authors indicate though, the models need to be validated for regions and countries outside of Wuhan.
I do not know whether the manuscript passed peer review with all the strict requisites. Small sample size.