Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19

PLOS Digit Health. 2022 Aug 1;1(8):e0000057. doi: 10.1371/journal.pdig.0000057. eCollection 2022 Aug.

Abstract

We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model's performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019 at a single institution, modeling select comorbidities using the value-based Medicare Advantage HCC Risk Adjustment Model. Sex, age, HCC codes, and risk adjustment factor (RAF) score were used. The model was validated on frontal CXRs from 413 ambulatory patients with COVID-19 (internal cohort) and on initial frontal CXRs from 487 COVID-19 hospitalized patients (external cohort). The discriminatory ability of the model was assessed using receiver operating characteristic (ROC) curves compared to the HCC data from electronic health records, and predicted age and RAF score were compared using correlation coefficient and absolute mean error. The model predictions were used as covariables in logistic regression models to evaluate the prediction of mortality in the external cohort. Predicted comorbidities from frontal CXRs, including diabetes with chronic complications, obesity, congestive heart failure, arrhythmias, vascular disease, and chronic obstructive pulmonary disease, had a total area under ROC curve (AUC) of 0.85 (95% CI: 0.85-0.86). The ROC AUC of predicted mortality for the model was 0.84 (95% CI,0.79-0.88) for the combined cohorts. This model using only frontal CXRs predicted select comorbidities and RAF score in both internal ambulatory and external hospitalized COVID-19 cohorts and was discriminatory of mortality, supporting its potential use in clinical decision making.

Grants and funding

AP, NS and SK were funded by the Medical Imaging Data Resource Center, which is supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under contracts 75N92020C00008 and 75N92020C00021. JR-F and WG received funding from the University of Illinois at Chicago Center for Clinical and Translational Science (CCTS) award ULTR002003. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.