ArticleScientific reports2024
Machine learning using clinical data at baseline predicts the medium-term efficacy of ustekinumab in patients with ulcerative colitis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic Prediction Models for Ulcerative Colitis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Recent Advances in Molecular Targeted Therapy for Inflammatory Bowel Disease.Internal medicine (Tokyo, Japan) · 2026Review
- Advances in Biologic Therapies for Allergic Diseases: Current Trends, Emerging Agents, and Future Perspectives.Journal of clinical medicine · 2025Review
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- Machine Learning of Serum Cytokine and Chemokine Profiles Can Classify Inflammatory Bowel Disease Beyond Clinical Diagnosis.Gastro hep advances · 2025Article
- Machine Learning for Predicting Biologic Agent Efficacy in Ulcerative Colitis: An Analysis for Generalizability and Combination with Computational Models.Diagnostics (Basel, Switzerland) · 2024Article
- Machine learning in predicting treatment response and remission in inflammatory bowel disease: a systematic review.Annals of gastroenterologyArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the therapeutic response to biologics before administration is a key clinical challenge in ulcerative colitis (UC). We previously reported a model for predicting the efficacy of vedolizumab (VDZ) for UC using a machine-learning approach. Ustekinumab (UST) is now available for treating UC, but no model for predicting its efficacy has been developed. When applied to patients with UC treated with UST, our VDZ prediction model showed positive predictive value (PPV) of 56.3% and negative predictive value (NPV) of 62.5%. Given this limited predictive ability, we aimed to develop a UST-specific prediction model with clinical features at baseline including background factors, clinical and endoscopic activity, and blood test results, as we did for the VDZ prediction model. The top 10 features (Alb, monocytes, height, MCV, TP, Lichtiger index, white blood cell count, MCHC, partial Mayo score, and CRP) associated with steroid-free clinical remission at 6 months after starting UST were selected using random forest. The predictive ability of a model using these predictors was evaluated by fivefold cross-validation. Validation of the prediction model with an external cohort showed PPV of 68.8% and NPV of 71.4%. Our study suggested the importance of establishing a drug-specific prediction model.
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