Identification of risk factors for mortality associated with COVID-19

Yuetian Yu; Cheng Zhu; Luyu Yang; Hui Dong; Ruilan Wang; Hongying Ni; Erzhen Chen; Zhongheng Zhang

doi:10.7717/peerj.9885

Identification of risk factors for mortality associated with COVID-19

PeerJ. 2020 Sep 1:8:e9885. doi: 10.7717/peerj.9885. eCollection 2020.

Authors

Yuetian Yu^#¹, Cheng Zhu^#², Luyu Yang³, Hui Dong³, Ruilan Wang⁴, Hongying Ni⁵, Erzhen Chen², Zhongheng Zhang⁶

Affiliations

¹ Department of Critical Care Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
² Department of Emergency Medicine, Rui Jin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
³ Department of Intensive Care Unit, Wuhan Third Hospital, Wuhan University, Wuhan, China.
⁴ Department of Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
⁵ Department of Critical Care Medicine, Jinhua Municipal Central Hospital, Jinhua, Zhejiang, China.
⁶ Department of Emergency Medicine, Sir Run Run Shaw hospital; Zhejiang University School of Medicine, Hangzhou, China.

^# Contributed equally.

Abstract

Objectives: Coronavirus Disease 2019 (COVID-19) has become a pandemic outbreak. Risk stratification at hospital admission is of vital importance for medical decision making and resource allocation. There is no sophisticated tool for this purpose. This study aimed to develop neural network models with predictors selected by genetic algorithms (GA).

Methods: This study was conducted in Wuhan Third Hospital from January 2020 to March 2020. Predictors were collected on day 1 of hospital admission. The primary outcome was the vital status at hospital discharge. Predictors were selected by using GA, and neural network models were built with the cross-validation method. The final neural network models were compared with conventional logistic regression models.

Results: A total of 246 patients with COVID-19 were included for analysis. The mortality rate was 17.1% (42/246). Non-survivors were significantly older (median (IQR): 69 (57, 77) vs. 55 (41, 63) years; p < 0.001), had higher high-sensitive troponin I (0.03 (0, 0.06) vs. 0 (0, 0.01) ng/L; p < 0.001), C-reactive protein (85.75 (57.39, 164.65) vs. 23.49 (10.1, 53.59) mg/L; p < 0.001), D-dimer (0.99 (0.44, 2.96) vs. 0.52 (0.26, 0.96) mg/L; p < 0.001), and α-hydroxybutyrate dehydrogenase (306.5 (268.75, 377.25) vs. 194.5 (160.75, 247.5); p < 0.001) and a lower level of lymphocyte count (0.74 (0.41, 0.96) vs. 0.98 (0.77, 1.26) × 10⁹/L; p < 0.001) than survivors. The GA identified a 9-variable (NNet1) and a 32-variable model (NNet2). The NNet1 model was parsimonious with a cost on accuracy; the NNet2 model had the maximum accuracy. NNet1 (AUC: 0.806; 95% CI [0.693-0.919]) and NNet2 (AUC: 0.922; 95% CI [0.859-0.985]) outperformed the linear regression models.

Conclusions: Our study included a cohort of COVID-19 patients. Several risk factors were identified considering both clinical and statistical significance. We further developed two neural network models, with the variables selected by using GA. The model performs much better than the conventional generalized linear models.

Keywords: COVID-19; Genetic algorithms; Risk factor.

Grants and funding

Hongying Ni received funding from the Jinhua Novel Coronavirus Pneumonia Emergency Response Research Project (2020XG-03). Zhongheng Zhang received National Natural Science Foundation of China (Grant No. 81901929). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.