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Development of a Risk Prediction Model for Adverse Skin Events Associated with TNF-α Inhibitors in Rheumatoid Arthritis Patients
DC Field | Value | Language |
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dc.contributor.author | Kim, W | - |
dc.contributor.author | Oh, SJ | - |
dc.contributor.author | Kim, HJ | - |
dc.contributor.author | Kim, JH | - |
dc.contributor.author | Gil, JY | - |
dc.contributor.author | Ku, YS | - |
dc.contributor.author | Kim, JH | - |
dc.contributor.author | Kim, HA | - |
dc.contributor.author | Jung, JY | - |
dc.contributor.author | Choi, IA | - |
dc.contributor.author | Kim, JH | - |
dc.contributor.author | Kim, J | - |
dc.contributor.author | Han, JM | - |
dc.contributor.author | Lee, KE | - |
dc.date.accessioned | 2024-09-27T00:19:55Z | - |
dc.date.available | 2024-09-27T00:19:55Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://repository.ajou.ac.kr/handle/201003/32830 | - |
dc.description.abstract | Background: 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.iso | en | - |
dc.title | Development of a Risk Prediction Model for Adverse Skin Events Associated with TNF-α Inhibitors in Rheumatoid Arthritis Patients | - |
dc.type | Article | - |
dc.identifier.pmid | 39064094 | - |
dc.identifier.url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11278277 | - |
dc.subject.keyword | genome-wide association study | - |
dc.subject.keyword | risk prediction | - |
dc.subject.keyword | skin and subcutaneous tissue-related complications | - |
dc.subject.keyword | TNF-α inhibitor | - |
dc.contributor.affiliatedAuthor | Kim, HA | - |
dc.contributor.affiliatedAuthor | Jung, JY | - |
dc.type.local | Journal Papers | - |
dc.identifier.doi | 10.3390/jcm13144050 | - |
dc.citation.title | Journal of clinical medicine | - |
dc.citation.volume | 13 | - |
dc.citation.number | 14 | - |
dc.citation.date | 2024 | - |
dc.citation.startPage | 4050 | - |
dc.citation.endPage | 4050 | - |
dc.identifier.bibliographicCitation | Journal of clinical medicine, 13(14). : 4050-4050, 2024 | - |
dc.identifier.eissn | 2077-0383 | - |
dc.relation.journalid | J020770383 | - |
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