Trial reportJAMA network open2019
Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.
Trial report in JAMA network open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 65 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
65 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Risk Factors and Predictive Biomarkers for Postoperative Complications in Crohn's Disease Surgery: Systematic Review.International journal of molecular sciences · 2026Pooled it
- Saudi consensus guidance for the diagnosis and management of inflammatory bowel disease in children and adolescents.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association · 2025Guideline
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review.Journal of Crohn's & colitis · 2022Pooled it
- Relapse From Deep Remission After Therapeutic De-escalation in Inflammatory Bowel Disease: A Systematic Review and Meta-analysis.Journal of Crohn's & colitis · 2020Pooled it
- Applying small bowel endoscopy in inflammatory bowel disease management.Clinical endoscopy · 2026Review
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Using machine learning to predict clinical remission with exclusive enteral nutrition in pediatric Crohn disease.Journal of pediatric gastroenterology and nutrition · 2026Article
- Identifying key clinical and biochemical predictors of treatment outcomes in inflammatory bowel disease: a real-world evidence study.Scientific reports · 2025Article
- Artificial intelligence in inflammatory bowel disease: Current applications and future directions.World journal of gastroenterology · 2025Review
- Artificial Intelligence in Advancing Inflammatory Bowel Disease Management: Setting New Standards.Cancers · 2025Review
- A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study.JMIR medical informatics · 2025Article
- Persistent body-weight change on achalasia and peroral endoscopic myotomy: a multicenter cohort study.Journal of gastroenterology · 2025Observational
- Artificial intelligence in gastroenterology: Ethical and diagnostic challenges in clinical practice.World journal of gastroenterology · 2025Article
- How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2025Review
- Artificial intelligence in inflammatory bowel disease endoscopy - a review of current evidence and a critical perspective on future challenges.Therapeutic advances in gastroenterology · 2025Review
- Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review.Therapeutic advances in gastroenterology · 2025Review
- Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.American journal of translational research · 2025Review
- Machine learning-based prediction of response to Ustekinumab with Crohn's disease.Therapeutic advances in gastroenterology · 2025Article
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
5 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
Importance: Biological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy. Identification of likely responders could reduce costs and delays in remission. Objective: To identify patients with Crohn disease likely to be durable responders to ustekinumab before committing to long-term treatment. Design, Setting, and Participants: This cohort study analyzed data from 3 phase 3 randomized clinical trials (UNITI-1, UNITI-2, and IM-UNITI) conducted from 2011 to 2015. Participants (n = 401) were individuals with active (C-reactive protein [CRP] measurement of ≥5 mg/L at enrollment) Crohn disease who received ustekinumab therapy. Data analysis was performed from November 1, 2017, to June 1, 2018. Exposures: All included patients were exposed to 1 or more dose of ustekinumab for 8 weeks or more. Main Outcomes and Measures: Random forest methods were used in building 2 models for predicting Crohn disease remission, with a CRP level lower than 5 mg/dL as a proxy for biological remission, beyond week 42 of ustekinumab treatment. The first model used only baseline data, and the second used data through week 8. Results: In total, 401 participants, with a mean (SD) age of 36.3 (12.6) years and 170 male (42.4%), were included. The week-8 model had a mean area under the receiver operating characteristic curve (AUROC) of 0.78 (95% CI, 0.69-0.87). In the testing data set, 27 of 55 participants (49.1%) classified as likely to have treatment success achieved success with a CRP level lower than 5 mg/L after week 42, and 7 of 65 participants (10.8%) classified as likely to have treatment failure achieved this outcome. In the full cohort, 87 patients (21.7%) attained remission after week 42. A prediction model using the week-6 albumin to CRP ratio had an AUROC of 0.76 (95% CI, 0.71-0.82). Baseline ustekinumab serum levels did not improve the model's prediction performance. Conclusions and Relevance: In patients with active Crohn disease, demographic and laboratory data before week 8 of treatment appeared to allow the prompt identification of likely nonresponders to ustekinumab without the need for costly drug-level monitoring.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.