Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs

Abstract

The prediction of corticosteroid responses in coronavirus disease 2019 (COVID-19) patients is crucial in clinical practice, and exploring the role of artificial intelligence (AI)-assisted analysis of chest radiographs (CXR) is warranted. This retrospective case–control study involving mild-to-moderate COVID-19 patients treated with corticosteroids was conducted from 4 September 2021, to 30 August 2022. The primary endpoint of the study was corticosteroid responsiveness, defined as the advancement of two or more of the eight-categories-ordinal scale. Serial abnormality scores for consolidation and pleural effusion on CXR were obtained using a commercial AI-based software based on days from the onset of symptoms. Amongst the 258 participants included in the analysis, 147 (57%) were male. Multivariable logistic regression analysis revealed that high pleural effusion score at 6–9 days from onset of symptoms (adjusted odds ratio of (aOR): 1.022, 95% confidence interval (CI): 1.003–1.042, p = 0.020) and consolidation scores up to 9 days from onset of symptoms (0–2 days: aOR: 1.025, 95% CI: 1.006–1.045, p = 0.010; 3–5 days: aOR: 1.03 95% CI: 1.011–1.051, p = 0.002; 6–9 days: aOR; 1.052, 95% CI: 1.015–1.089, p = 0.005) were associated with an unfavorable corticosteroid response. AI-generated scores could help intervene in the use of corticosteroids in COVID-19 patients who would not benefit from them.

Keywords

• artificial intelligence
• chest radiograph
• corticosteroid responsiveness
• COVID-19