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

Neha Paranjape, Lauren L Staples, Christina Y Stradwick, Herman Gene Ray and Ian J Saldanha

PLOS ONE, 2021, vol. 16, issue 3, 1-10

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.

Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248891 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 48891&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0248891

DOI: 10.1371/journal.pone.0248891

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().