A Machine Learning Approach to Predict In-Hospital Mortality in COVID-19 Patients with Underlying Cardiovascular Disease using Artificial Neural Network

Background: Machine learning algorithms, such as artificial neural networks (ANN), provide more accurate predictions by discovering complex patterns within data. Since COVID-19 disease is prevalent, using advanced statistical tools can upgrade clinical decision making by identifying high risk patients at the time of admission. Objective: This study aims to predict in-hospital mortality in COVID-19 patients with underlying cardiovascular disease (CVD) using the ANN model. Methods: In the current retrospective cohort study, 880 COVID-19 patients with underlying CVD were enrolled from 26 health centers affiliated with Shiraz University of Medical Sciences and followed up from 10 June to 26 December 2020. The five-fold cross-validation method was utilized to build the optimal ANN model for predicting in-hospital death. Moreover, the predictive power of the ANN model was assessed with concordance indices and the area under the ROC curve (AUC). Results: The median (95% CI) survival time of hospitalization was 16.7 (15.2-18.2) days and the empirical death rate was calculated to be 17.5%. About 81.5% of intubated COVID-19 patients were dead and the majority of the patients were admitted to the hospital with triage level two (54%). According to the ANN model, intubation, blood urea nitrogen, C-reactive protein, lactate dehydrogenase, and serum calcium were the most important prognostic indicators associated with patients’ in-hospital mortality. In addition, the accuracy of the ANN model was obtained to be 83.4%, with a sensitivity and specificity of 72.7% and 85.6%, respectively (AUC=0.861). Conclusion: In this study, the ANN model demonstrated a good performance in the prediction of in-hospital mortality in COVID-19 patients with a history of CVD.