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524 results on '"Han, Ji-Min"'

15. Efficacy and safety of enavogliflozin versus dapagliflozin added to metformin plus gemigliptin treatment in patients with type 2 diabetes: a double-blind, randomized, comparator-active study: ENHANCE-D study

Author

Kim, Kyung-Soo, Han, Kyung Ah, Kim, Tae Nyun, Park, Cheol-Young, Park, Jung Hwan, Kim, Sang Yong, Kim, Yong Hyun, Song, Kee Ho, Kang, Eun Seok, Kim, Chul Sik, Koh, Gwanpyo, Kang, Jun Goo, Kim, Mi Kyung, Han, Ji Min, Kim, Nan Hee, Mok, Ji Oh, Lee, Jae Hyuk, Lim, Soo, Kim, Sang Soo, Kim, Tae Ho, Won, Kyu Chang, Lee, Ki Young, Cho, Jae Hyoung, Han, Ju Young, Kim, So Hun, Nah, Jae Jin, Song, Hwa Rang, Lee, Si Eun, and Kim, Sungrae

Published
2023
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20. Development of a Risk Prediction Model for Adverse Skin Events Associated with TNF-α Inhibitors in Rheumatoid Arthritis Patients.

Author

Kim, Woorim, Oh, Soo-Jin, Kim, Hyun-Jeong, Kim, Jun-Hyeob, Gil, Jin-Yeon, Ku, Young-Sook, Kim, Joo-Hee, Kim, Hyoun-Ah, Jung, Ju-Yang, Choi, In-Ah, Kim, Ji-Hyoun, Kim, Jinhyun, Han, Ji-Min, and Lee, Kyung-Eun

Subjects
MACHINE learning, DISEASE risk factors, TUMOR necrosis factors, GENOME-wide association studies, LOGISTIC regression analysis, RHEUMATOID arthritis
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. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Risk factors for hyperglycemia in COVID‐19 patients treated with remdesivir.

Author

Kim, Woorim, Lee, Go Woon, Rhee, Nuga, Min, Kyung Hyun, Kim, Jun Hyeob, Gil, Jin Yeon, Kim, Song Yi, Han, Ji Min, and Lee, Kyung Eun

Subjects
COVID-19, REMDESIVIR, RECEIVER operating characteristic curves, MACHINE learning, DISEASE risk factors, LOGISTIC regression analysis
Abstract

The primary objective of this study was to investigate the factors contributing to hyperglycemic adverse events (AEs) associated with the administration of remdesivir in hospitalized patients diagnosed with coronavirus disease 2019 (COVID‐19). Furthermore, the study aimed to develop a risk score model employing various machine learning approaches. A total of 1262 patients were enrolled in this investigation. The relationship between covariates and hyperglycemic AEs was assessed through logistic regression analysis. Diverse machine learning algorithms were employed for the purpose of forecasting hyperglycemia‐related complications. After adjusting for covariates, individuals with a body mass index ≥23 kg/m2, those using proton pump inhibitors, cholinergic medications, or individuals with cardiovascular diseases exhibited approximately 2.41‐, 2.73‐, 2.65‐, and 1.97‐fold higher risks of experiencing hyperglycemic AEs (95% CI 1.271–4.577, 1.223–6.081, 1.168–5.989, and 1.119–3.472, respectively). Multivariate logistic regression, elastic net, and random forest models displayed area under the receiver operating characteristic curve values of 0.65, 0.66, and 0.60, respectively (95% CI 0.572–0.719, 0.640–0.671, and 0.583–0.611, respectively). This study comprehensively explored factors associated with hyperglycemic complications arising from remdesivir administration and, concurrently, leveraged a range of machine learning methodologies to construct a risk scoring model, thereby facilitating the tailoring of individualized remdesivir treatment regimens for patients with COVID‐19. [ABSTRACT FROM AUTHOR]

Published
2024
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