Clinical Characteristics, Associated Factors, and Predicting COVID-19 Mortality Risk: A Retrospective Study in Wuhan, China

Yu, Caizheng; Lei, Qing; Li, Wenkai; Wang, Xiong; Liu, Wei; Fan, Xionglin; Li, Wengang

Introduction Coronavirus disease 2019 (COVID-19) has become a serious global pandemic. This study investigates the clinical characteristics and risk factors for COVID-19 mortality, and establishes a novel scoring system to predict mortality risk in COVID-19 patients. Methods A cohort of 1,663 hospitalized COVID-19 patients in Wuhan, China, of whom 212 died and 1,252 recovered, were included in the present study. Demographic, clinical, and laboratory data on admission were collected from electronic medical records between January 14, 2020, and February 28, 2020. Clinical outcomes were collected until March 26, 2020. Multivariable logistic regression was used to explore the association between potential risk factors and COVID-19 mortality. The receiver operating characteristic curve was used to predict COVID-19 mortality risk. All analyses were conducted in April 2020. Results Multivariable regression showed increased odds of COVID-19 mortality associated with older age (OR=2.15, 95% CI=1.35, 3.43), male sex (OR=1.97, 95% CI=1.29, 2.99), history of diabetes (OR=2.34, 95% CI=1.45, 3.76), lymphopenia (OR=1.59, 95% CI=1.03, 2.46), and increased procalcitonin (OR=3.91, 95% CI=2.22, 6.91, per SD increase) on admission. Spline regression analysis indicated that the correlation between procalcitonin levels and COVID-19 mortality was nonlinear (p=0.0004 for nonlinearity). The area under the receiver operating curve of the COVID-19 mortality risk was 0.765 (95% CI=0.725, 0.805). Conclusions The independent risk factors for COVID-19 mortality included older age, male sex, history of diabetes, lymphopenia, and increased procalcitonin, which could help clinicians to identify COVID-19 patients with poor prognosis at an earlier stage. The COVID-19 mortality risk score model may assist clinicians in reducing COVID-19-related mortality by implementing better strategies for more effective use of limited medical resources.