A Predictive Model for Hospitalization and Survival to COVID-19 in a Retrospective Population-based Study

Overview

Journal Sci Rep

Specialty Science

Date 2022 Oct 28

PMID 36307436

Authors

Alejandro Cisterna-Garcia

Antonio Guillen-Teruel

Marcos Caracena

Enrique Perez

Fernando Jimenez

Francisco J Francisco-Verdu

Gabriel Reina

Enrique Gonzalez-Billalabeitia

Jose Palma

Alvaro Sanchez-Ferrer

Juan A Botia

Affiliations

Soon will be listed here.

Abstract

The development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90-93%, ROC-AUC = 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71-73%, ROC-AUC = 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed ( https://alejandrocisterna.shinyapps.io/PROVIA ).

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