A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study

Abstract

The severe acute respiratory syndrome coronavirus (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) is highly transmissible and has been responsible for a pandemic associated with a high number of deaths. The clinical management of patients and the optimal use of resources are two important factors in reducing this mortality, especially in scenarios of high incidence. To this end, it is necessary to develop tools that allow early triage of patients with the minimal use of diagnostic tests and based on readily accessible data, such as electronic medical records. This work proposes the use of a machine learning model that allows the prediction of mortality and risk of hospitalization using simple demographic characteristics and comorbidities, using a COVID-19 dataset of 86867 patients. In addition, we developed a new method designed to deal with data imbalance problems. The model was able to predict with high accuracy (89-93%, ROC-AUC = 0.94) the patient's final status (expired/discharged) and with medium accuracy the risk of hospitalization (71-73%, ROC-AUC = 0.75). These models were obtained by assembling and using easily obtainable clinical characteristics (2 demographic characteristics and 19 predictors of comorbidities). The most relevant features of these models were the following patient characteristics: age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure.

Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran

“…The results of Khozeimeh et al's 58 study also showed that one of the most important features related to the mortality of patients with COVID-19 was heart disease. The results of studies of Ahmed et al 54 and Cisterna-García et al 56 similar to the results of our study, identified the features of lung disease, blood pressure, and acute respiratory distress syndrome as effective factors in the development of a predictive survival system.…”

“…data set. 53 Various studies [54][55][56][57][58][59][60][61] were conducted in predicting the survival and mortality of COVID-19 patients using ML techniques. Krajah's et al 59 study showed intubation was the most important features for the survival prediction.…”

Development of a prediction model for 30-day COVID-19 hospitalization and death in a national cohort of Veterans Health Administration patients – March 2022 - April 2023

“…Currently available COVID-19 risk prediction models have important limitations. First, most models were developed early in the pandemic (8)(9)(10)(11)(12)(13) and do not account for current circulating viral strains, population immunity from previous infection or vaccination, and treatments. This is particularly important as risk models are sensitive to population contextual factors under which they are developed.…”