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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
DC Field | Value | Language |
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dc.contributor.author | 경태영 | - |
dc.contributor.author | 김은경 | - |
dc.contributor.author | 박윤수 | - |
dc.contributor.author | 신현주 | - |
dc.date.accessioned | 2023-11-07T07:36:49Z | - |
dc.date.available | 2023-11-07T07:36:49Z | - |
dc.date.issued | 2023-09 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/196469 | - |
dc.description.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. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | MDPI AG | - |
dc.relation.isPartOf | JOURNAL OF CLINICAL MEDICINE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Novel Risks of Unfavorable Corticosteroid Response in Patients with Mild-to-Moderate COVID-19 Identified Using Artificial Intelligence-Assisted Analysis of Chest Radiographs | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Hospital Medicine (입원의학과) | - |
dc.contributor.googleauthor | Min Hyung Kim | - |
dc.contributor.googleauthor | Hyun Joo Shin | - |
dc.contributor.googleauthor | Jaewoong Kim | - |
dc.contributor.googleauthor | Sunhee Jo | - |
dc.contributor.googleauthor | Eun-Kyung Kim | - |
dc.contributor.googleauthor | Yoon Soo Park | - |
dc.contributor.googleauthor | Taeyoung Kyong | - |
dc.identifier.doi | 10.3390/ jcm12185852 | - |
dc.contributor.localId | A05849 | - |
dc.contributor.localId | A00801 | - |
dc.contributor.localId | A01598 | - |
dc.contributor.localId | A02178 | - |
dc.relation.journalcode | J03556 | - |
dc.identifier.eissn | 2077-0383 | - |
dc.identifier.pmid | 37762792 | - |
dc.subject.keyword | COVID-19 | - |
dc.subject.keyword | artificial intelligence | - |
dc.subject.keyword | chest radiograph | - |
dc.subject.keyword | corticosteroid responsiveness | - |
dc.contributor.alternativeName | Kyong, Tae Young | - |
dc.contributor.affiliatedAuthor | 경태영 | - |
dc.contributor.affiliatedAuthor | 김은경 | - |
dc.contributor.affiliatedAuthor | 박윤수 | - |
dc.contributor.affiliatedAuthor | 신현주 | - |
dc.citation.volume | 12 | - |
dc.citation.number | 18 | - |
dc.citation.startPage | 5852 | - |
dc.identifier.bibliographicCitation | JOURNAL OF CLINICAL MEDICINE, Vol.12(18) : 5852, 2023-09 | - |
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