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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Rationale and Objectives: The 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 Methods: This 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. Results: The 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. Conclusion: Deep 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.

Original languageEnglish (US)
Pages (from-to)1151-1158
Number of pages8
JournalAcademic radiology
Volume28
Issue number8
DOIs
StatePublished - Aug 2021

Keywords

  • COVID-19
  • chest radiography
  • convolutional neural networks
  • deep learning
  • multi-task learning

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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