Dynamic evaluation of a COVID-19 death prediction model using Extreme Gradient Boosting Predictive Model

The COVID-19 pandemic has evolved dynamically with the emergence of new variants and an increase in vaccination coverage. Given the high fatality rate of severe COVID-19, disease severity prediction models must incorporate these temporal variations. In this light, the present study seeks to develop a model to predict COVID-19 mortality in hospitalized patients. The Extreme Gradient Boost model was used to predict COVID-19 mortality upon hospital admission, and the results were correlated with laboratory test results, vaccination status, comorbidities, and clinical signs and symptoms at the time of admission. Clinical data from electronic medical records, vaccination databases, and severe acute respiratory syndrome (SARS) reports were used. The XGBoost model performed best, with an area under the curve (AUC) of 96.4% at epidemiological week 53 of 2020. The most significant variables for the model were body temperature, blood pressure, respiratory rate, heart rate, urea, magnesium, sodium, and C reactive protein levels. Our study identified key clinical and laboratory variables for predicting COVID-19 mortality.