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Development of a Risk Prediction Model for Adverse Skin Events Associated with TNF-α Inhibitors in Rheumatoid Arthritis Patients

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dc.contributor.authorKim, W-
dc.contributor.authorOh, SJ-
dc.contributor.authorKim, HJ-
dc.contributor.authorKim, JH-
dc.contributor.authorGil, JY-
dc.contributor.authorKu, YS-
dc.contributor.authorKim, JH-
dc.contributor.authorKim, HA-
dc.contributor.authorJung, JY-
dc.contributor.authorChoi, IA-
dc.contributor.authorKim, JH-
dc.contributor.authorKim, J-
dc.contributor.authorHan, JM-
dc.contributor.authorLee, KE-
dc.date.accessioned2024-09-27T00:19:55Z-
dc.date.available2024-09-27T00:19:55Z-
dc.date.issued2024-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/32830-
dc.description.abstractBackground: Rheumatoid arthritis (RA) is a chronic inflammatory disorder primarily targeting joints, significantly impacting patients’ quality of life. The introduction of tumor necrosis factor-alpha (TNF-α) inhibitors has markedly improved RA management by reducing inflammation. However, these medications are associated with adverse skin reactions, which can vary greatly among patients due to genetic differences. Objectives: This study aimed to identify risk factors associated with skin adverse events by TNF-α in RA patients. Methods: A cohort study was conducted, encompassing patients with RA who were prescribed TNF-α inhibitors. This study utilized machine learning algorithms to analyze genetic data and identify markers associated with skin-related adverse events. Various machine learning algorithms were employed to predict skin and subcutaneous tissue-related outcomes, leading to the development of a risk-scoring system. Multivariable logistic regression analysis identified independent risk factors for skin and subcutaneous tissue-related complications. Results: After adjusting for covariates, individuals with the TT genotype of rs12551103, A allele carriers of rs13265933, and C allele carriers of rs73210737 exhibited approximately 20-, 14-, and 10-fold higher incidences of skin adverse events, respectively, compared to those with the C allele, GG genotype, and TT genotype. The machine learning algorithms used for risk prediction showed excellent performance. The risk of skin adverse events among patients receiving TNF-α inhibitors varied based on the risk score: 0 points, 0.6%; 2 points, 3.6%; 3 points, 8.5%; 4 points, 18.9%; 5 points, 36.7%; 6 points, 59.2%; 8 points, 90.0%; 9 points, 95.7%; and 10 points, 98.2%. Conclusions: These findings, emerging from this preliminary study, lay the groundwork for personalized intervention strategies to prevent TNF-α inhibitor-associated skin adverse events. This approach has the potential to improve patient outcomes by minimizing the risk of adverse effects while optimizing therapeutic efficacy.-
dc.language.isoen-
dc.titleDevelopment of a Risk Prediction Model for Adverse Skin Events Associated with TNF-α Inhibitors in Rheumatoid Arthritis Patients-
dc.typeArticle-
dc.identifier.pmid39064094-
dc.identifier.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11278277-
dc.subject.keywordgenome-wide association study-
dc.subject.keywordrisk prediction-
dc.subject.keywordskin and subcutaneous tissue-related complications-
dc.subject.keywordTNF-α inhibitor-
dc.contributor.affiliatedAuthorKim, HA-
dc.contributor.affiliatedAuthorJung, JY-
dc.type.localJournal Papers-
dc.identifier.doi10.3390/jcm13144050-
dc.citation.titleJournal of clinical medicine-
dc.citation.volume13-
dc.citation.number14-
dc.citation.date2024-
dc.citation.startPage4050-
dc.citation.endPage4050-
dc.identifier.bibliographicCitationJournal of clinical medicine, 13(14). : 4050-4050, 2024-
dc.identifier.eissn2077-0383-
dc.relation.journalidJ020770383-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Rheumatology
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