Abstract
ObjectivesCoronavirus 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).MethodsThis 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.ResultsA 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.ConclusionsOur 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.
Highlights
The Coronavirus Disease 2019 (COVID-19) pandemic outbreak has become a global health emergency since its outbreak in Wuhan, China, and it is spreading rapidly across the world (Huang et al, 2020; Ren et al, 2020)
This study aimed to develop neural network models with predictors selected by genetic algorithms (GA)
We further developed two neural network models, with the variables selected by using GA
Summary
The Coronavirus Disease 2019 (COVID-19) pandemic outbreak has become a global health emergency since its outbreak in Wuhan, China, and it is spreading rapidly across the world (Huang et al, 2020; Ren et al, 2020). The World Health Organization declared it to be a pandemic outbreak due to its capability of human-tohuman transmission and rapid spread over the globe. The COVID-19-specific mortality rate has been reported to be from 2% to 20% (Sun, Chen & Viboud, 2020; Yang et al, 2020; Chen et al, 2020b), depending on the availability of medical resources and economic status. Risk stratification can help medical decision making and resource allocation, for example, high-risk patients can be transferred to the intensive care unit for close monitoring and organ support. Several studies have investigated the risk factors for mortality in COVID-19 (Liu et al, 2020; Wang et al, 2020), there has been no systematic effort to develop a prediction tool for risk stratification at an early stage
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