Development and external validation of a prognostic tool for COVID-19 critical disease

• Type of Material: Article
• Authors: Daniel S. Chow, University of California Irvine; Justin Glavis-Bloom, University of California Irvine; Jennifer E. Soun, University of California Irvine; Brent Weinberg, Emory University; Theresa Barens Loveless, University of California Irvine; Xiaohui Xie, University of California Irvine; Simukayi Mutasa, Columbia University; Edwin Monuki, University of California Irvine; Jung In Park, University of California Irvine; Daniela Bota, University of California Irvine; Jie Wu, University of California Irvine; Leslie Thompson, University of California Irvine; Bernadette Boden-Albala, University of California Irvine; Saahir Khan, University of California Irvine; Alpesh N. Amin, University of California Irvine; Peter D. Chang, University of California Irvine
• Language: English
• Date: 2020-12-09
• Publisher: PUBLIC LIBRARY SCIENCE
• Publication Version: Final Published Version
• Copyright Statement: © 2020 Chow et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0242953
• Title of Journal or Parent Work: PLOS ONE
• Volume: 15
• Issue: 12
• Start Page: e0242953
• End Page: e0242953
• Grant/Funding Information: This study was funded by an internal award at the University of California, Irvine through the COVID-19 Basic, Translational, and Clinical Research Funding Opportunity. None of the authors received salary support from this award.
• Keywords: Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics
• Research Categories: Biology, Virology; Health Sciences, Health Care Management

Abstract

Background The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care. Methods This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia. Results Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years [range, 21–88 years]; 64% [36/55] male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/ 87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years [range, 27–88 years]; 55% [22/40] male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87–1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease. Conclusions and relevance We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.