Two novel nomograms for predicting the risk of hospitalization or mortality due to COVID-19 by the naïve Bayesian classifier method

Abstract
Coronavirus disease 2019 (COVID-19) has become a global pandemic that has affected millions of people worldwide. The presence of multiple risk factors for COVID-19 makes it difficult to plan treatment and optimize the use of medical resources. The aim of this study is to determine potential risk factors for hospitalization or mortality in patients with COVID-19 via two novel naive Bayesian nomograms. The publicly available COVID-19 National data published by the Mexican Ministry of Health through the “Dirección General de Epidemiología” website was analyzed. Univariable logistic regression was utilized to identify potential risk factors that may affect hospitalization or mortality in patients with COVID-19. The naïve Bayesian classifier method was implemented to predict nomograms. The nomograms were verified by the area under the receiver operating characteristic curve (AUC), classification accuracy (CA), F1 score, precision, recall, and calibration plot. A total of 979,430 patients (45.3 ± 15.9 years old, and 51.1% male) tested positive for COVID-19 from January 1 to November 22, 2020. Among them, 22.3% of the patients required hospitalization and 99,964 patients (9.8%) died. The most important risk factors to predict the probability of hospitalization and mortality were pneumonia, age, chronic kidney failure, chronic obstructive respiratory disease, and diabetes. The performance measures demonstrated good discrimination and calibration (hospitalization: AUC = 0.896, CA = 0.880; mortality: AUC = 0.903, CA = 0.899). Two novel nomograms to estimate the risk of hospitalization and mortality were proposed, which could be used to facilitate individualized decision-making for patients newly diagnosed with COVID-19.
Keywords: COVID-19; hospitalization; mortality; nomogram; prediction; risk factor.