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

Authors
 Min Hyung Kim  ;  Hyun Joo Shin  ;  Jaewoong Kim  ;  Sunhee Jo  ;  Eun-Kyung Kim  ;  Yoon Soo Park  ;  Taeyoung Kyong 
Citation
 JOURNAL OF CLINICAL MEDICINE, Vol.12(18) : 5852, 2023-09 
Journal Title
JOURNAL OF CLINICAL MEDICINE
Issue Date
2023-09
Keywords
COVID-19 ; artificial intelligence ; chest radiograph ; corticosteroid responsiveness
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.
Files in This Item:
T202305489.pdf Download
DOI
10.3390/ jcm12185852
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Hospital Medicine (입원의학과) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kyong, Tae Young(경태영) ORCID logo https://orcid.org/0000-0001-5846-7808
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
Park, Yoon Soo(박윤수)
Shin, Hyun Joo(신현주) ORCID logo https://orcid.org/0000-0002-7462-2609
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/196469
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