Kun Wang1, Peiyuan Zuo2, Yuwei Liu3, Meng Zhang1, Xiaofang Zhao1, Songpu Xie1, Hao Zhang1, Xinglin Chen4, Chengyun Liu1,5. 1. Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 2. Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 3. Department of General Practice, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, China. 4. Department of Epidemiology and Biostatistics, EmpowerU, X & Y Solutions Inc, Boston, Massachusetts, USA. 5. First People's Hospital of Jiangxia District, Wuhan City & Union Jiangnan Hospital, HUST, Wuhan, China.
Abstract
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.
BACKGROUND: This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). METHODS: The training cohort included consecutive COVID-19patients 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-19patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. RESULTS: A total of 296 COVID-19patients 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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