Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning

Ayis Pyrros,Adam Eugene Flanders,Jorge Mario Rodríguez-Fernández,Andrew Chen,Patrick Cole,Daniel Wenzke,Eric Hart,Samuel Harford,Jeanne Horowitz,Paul Nikolaidis,Nadir Muzaffar,Viveka Boddipalli,Jai Nebhrajani,Nasir Siddiqui,Melinda Willis,Houshang Darabi,Oluwasanmi Koyejo,William Galanter

doi:10.1016/j.acra.2021.05.002

Abstract

Rationale and ObjectivesThe clinical prognosis of outpatients with coronavirus disease 2019 (COVID-19) remains difficult to predict, with outcomes including asymptomatic, hospitalization, intubation, and death. Here we determined the prognostic value of an outpatient chest radiograph, together with an ensemble of deep learning algorithms predicting comorbidities and airspace disease to identify patients at a higher risk of hospitalization from COVID-19 infection.Materials and MethodsThis retrospective study included outpatients with COVID-19 confirmed by reverse transcription-polymerase chain reaction testing who received an ambulatory chest radiography between March 17, 2020 and October 24, 2020. In this study, full admission was defined as hospitalization within 14 days of the COVID-19 test for > 2 days with supplemental oxygen. Univariate analysis and machine learning algorithms were used to evaluate the relationship between the deep learning model predictions and hospitalization for > 2 days.ResultsThe study included 413 patients, 222 men (54%), with a median age of 51 years (interquartile range, 39–62 years). Fifty-one patients (12.3%) required full admission. A boosted decision tree model produced the best prediction. Variables included patient age, frontal chest radiograph predictions of morbid obesity, congestive heart failure and cardiac arrhythmias, and radiographic opacity, with an internally validated area under the curve (AUC) of 0.837 (95% CI: 0.791–0.883) on a test cohort.ConclusionDeep learning analysis of single frontal chest radiographs was used to generate combined comorbidity and pneumonia scores that predict the need for supplemental oxygen and hospitalization for > 2 days in patients with COVID-19 infection with an AUC of 0.837 (95% confidence interval: 0.791–0.883). Comorbidity scoring may prove useful in other clinical scenarios.

Highlights

The coronavirus disease 2019 (COVID-19) pandemic placed unprecedented demand on healthcare systems
The purpose of this study was to develop a deep learning algorithm that could predict the likely presence of relevant comorbidities, in combination with an algorithm to quantify opacity, from frontal chest radiographs (CXRs), and thereby enable providers to more effectively risk-stratify patients presenting with COVID-19 infection
The area under the curve (AUC) for just the binomial electronic health record (EHR)-based ICD10 hierarchical condition category (HCC) codes was 0.5646

Summary

Introduction

The coronavirus disease 2019 (COVID-19) pandemic placed unprecedented demand on healthcare systems. Comorbid conditions like diabetes and cardiovascular disease are associated with more severe cases of COVID-19 [2]. Relevant comorbidities are sometimes unknown or unrecognized by the medical provider and patient, limiting the provider’s ability to perform a proper risk assessment [3]. The extraction of comorbidity data is based on contemporaneously provided patient history, manual record review, and/or electronic health record (EHR) queries [4], and the results are imperfect and often incomplete. The purpose of this study was to develop a deep learning algorithm that could predict the likely presence of relevant comorbidities, in combination with an algorithm to quantify opacity, from frontal chest radiographs (CXRs), and thereby enable providers to more effectively risk-stratify patients presenting with COVID-19 infection

Objectives

Methods

Results

Conclusion