Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19

Authors

Neha Paranjape, Department of Infectious Disease, Wellstar Medical Group, Marietta, Georgia, United States of America.
Lauren L. Staples, Kennesaw State University
Christina Y. Stradwick, Analytics and Data Science Institute, Kennesaw State University, Marietta, Georgia, United States of America.
Herman Gene Ray, Analytics and Data Science Institute, Kennesaw State University, Marietta, Georgia, United States of America.
Ian J. Saldanha, Center for Evidence Synthesis in Health, Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, United States of America.

Department

School of Data Science and Analytics

Document Type

Article

Publication Date

1-1-2021

Abstract

BACKGROUND: Identifying factors that can predict severe disease in patients needing hospitalization for COVID-19 is crucial for early recognition of patients at greatest risk. OBJECTIVE: (1) Identify factors predicting intensive care unit (ICU) transfer and (2) develop a simple calculator for clinicians managing patients hospitalized with COVID-19. METHODS: A total of 2,685 patients with laboratory-confirmed COVID-19 admitted to a large metropolitan health system in Georgia, USA between March and July 2020 were included in the study. Seventy-five percent of patients were included in the training dataset (admitted March 1 to July 10). Through multivariable logistic regression, we developed a prediction model (probability score) for ICU transfer. Then, we validated the model by estimating its performance accuracy (area under the curve [AUC]) using data from the remaining 25% of patients (admitted July 11 to July 31). RESULTS: We included 2,014 and 671 patients in the training and validation datasets, respectively. Diabetes mellitus, coronary artery disease, chronic kidney disease, serum C-reactive protein, and serum lactate dehydrogenase were identified as significant risk factors for ICU transfer, and a prediction model was developed. The AUC was 0.752 for the training dataset and 0.769 for the validation dataset. We developed a free, web-based calculator to facilitate use of the prediction model (https://icucovid19.shinyapps.io/ICUCOVID19/). CONCLUSION: Our validated, simple, and accessible prediction model and web-based calculator for ICU transfer may be useful in assisting healthcare providers in identifying hospitalized patients with COVID-19 who are at high risk for clinical deterioration. Triage of such patients for early aggressive treatment can impact clinical outcomes for this potentially deadly disease.

Journal Title

PloS one

Volume

16

Issue

3

First Page

e0248891

Digital Object Identifier (DOI)

10.1371/journal.pone.0248891