Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning.
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.
['Pyrros|Ayis|A|', 'Flanders|Adam Eugene|AE|', 'Rodríguez-Fernández|Jorge Mario|JM|', 'Chen|Andrew|A|', 'Cole|Patrick|P|', 'Wenzke|Daniel|D|', 'Hart|Eric|E|', 'Harford|Samuel|S|', 'Horowitz|Jeanne|J|', 'Nikolaidis|Paul|P|', 'Muzaffar|Nadir|N|', 'Boddipalli|Viveka|V|', 'Nebhrajani|Jai|J|', 'Siddiqui|Nasir|N|', 'Willis|Melinda|M|', 'Darabi|Houshang|H|', 'Koyejo|Oluwasanmi|O|', 'Galanter|William|W|']
D000328:Adult D000086382:COVID-19 D000077321:Deep Learning D005260:Female D006760:Hospitalization D006801:Humans D008297:Male D008875:Middle Aged D010100:Oxygen D013902:Radiography, Thoracic D012189:Retrospective Studies
COVID-19 deep learning chest radiography convolutional neural networks multi-task learning