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Identification of risk factors for mortality associated with COVID-19
Yuetian Yu1, Cheng Zhu2, Luyu Yang3
1Department of Critical Care Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Insights
This study developed neural network models using genetic algorithms to predict Coronavirus Disease 2019 (COVID-19) patient outcomes. The models demonstrated superior performance compared to traditional logistic regression for risk stratification.
Area of Science:
- Computational biology
- Medical informatics
- Epidemiology
Background:
- Coronavirus Disease 2019 (COVID-19) poses a global health challenge.
- Effective risk stratification upon hospital admission is crucial for patient management and resource allocation.
- Existing tools for COVID-19 risk stratification lack sophistication.
Purpose of the Study:
- To develop advanced neural network models for predicting COVID-19 patient outcomes.
- To utilize genetic algorithms (GA) for optimal predictor selection in model development.
- To compare the performance of developed models against conventional logistic regression.
Main Methods:
- A cohort of 246 COVID-19 patients was analyzed retrospectively.
- Predictors were collected on hospital admission day; primary outcome was vital status at discharge.
- Genetic algorithms selected variables, and neural network models were built using cross-validation, then compared to logistic regression.
Main Results:
- The mortality rate was 17.1%. Non-survivors were older and exhibited elevated levels of high-sensitive troponin I, C-reactive protein, D-dimer, and alpha-hydroxybutyrate dehydrogenase, with lower lymphocyte counts.
- Two neural network models were developed: NNet1 (9 variables) and NNet2 (32 variables).
- NNet2 achieved the highest accuracy (AUC: 0.922), outperforming NNet1 (AUC: 0.806) and logistic regression models.
Conclusions:
- Several clinical and statistically significant risk factors for COVID-19 mortality were identified.
- Developed neural network models, particularly NNet2, show significantly improved predictive performance over conventional methods.
- These models offer a more sophisticated tool for COVID-19 risk stratification at hospital admission.
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.
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