Model Prediction for In-Hospital Mortality in Patients with COVID-19: A Case-Control Study in Isfahan, Iran.

Abstract

ABSTRACTThe COVID-19 pandemic has now imposed an enormous global burden as well as a large mortality in a short time period. Although there is no promising treatment, identification of early predictors of in-hospital mortality would be critically important in reducing its worldwide mortality. We aimed to suggest a prediction model for in-hospital mortality of COVID-19. In this case–control study, we recruited 513 confirmed patients with COVID-19 from February 18 to March 26, 2020 from Isfahan COVID-19 registry. Based on extracted laboratory, clinical, and demographic data, we created an in-hospital mortality predictive model using gradient boosting. We also determined the diagnostic performance of the proposed model including sensitivity, specificity, and area under the curve (AUC) as well as their 95% CIs. Of 513 patients, there were 60 (11.7%) in-hospital deaths during the study period. The diagnostic values of the suggested model based on the gradient boosting method with oversampling techniques using all of the original data were specificity of 98.5% (95% CI: 96.8–99.4), sensitivity of 100% (95% CI: 94–100), negative predictive value of 100% (95% CI: 99.2–100), positive predictive value of 89.6% (95% CI: 79.7–95.7), and an AUC of 98.6%. The suggested model may be useful in making decision to patient’s hospitalization where the probability of mortality may be more obvious based on the final variable. However, moderate gaps in our knowledge of the predictors of in-hospital mortality suggest further studies aiming at predicting models for in-hospital mortality in patients with COVID-19.