Evidence map›Paper›PMID 31074823›Full record

Trial reportJAMA network open2019

Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.

Akbar K Waljee, Beth I Wallace, Shirley Cohen-Mekelburg, Yumu Liu, Boang Liu, Kay Sauder, Ryan W Stidham, Ji Zhu, Peter D R Higgins

Erratum issuedAbstract readClinical Trial, Phase IIIRandomized Controlled TrialValidation Study
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

65 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. 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 · 2025
    Guideline
  3. Pooled it
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  5. Pooled it
  6. Review
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  8. Article
  9. Article
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  12. Article
  13. Observational
  14. Article
  15. 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 · 2025
    Review
  16. Review
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  19. Article
  20. Review

5 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Akbar K WaljeeMichigan Integrated Center for Health Analytics and Medical Prediction, Ann Arbor.
Beth I WallaceMichigan Integrated Center for Health Analytics and Medical Prediction, Ann Arbor.
Shirley Cohen-MekelburgVA Center for Clinical Management Research, VA Ann Arbor Health Care System, Ann Arbor, Michigan.
Yumu LiuDepartment of Statistics, University of Michigan, Ann Arbor.
Boang LiuDepartment of Statistics, University of Michigan, Ann Arbor.
Kay SauderDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Michigan Medicine, Ann Arbor.
Ryan W StidhamDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Michigan Medicine, Ann Arbor.
Ji ZhuDepartment of Statistics, University of Michigan, Ann Arbor.
Peter D R HigginsDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Michigan Medicine, Ann Arbor.

Funding

University of Michigan Center for Gastrointestinal ResearchP30DK034933 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI OWYANG, CHUNG · 1986 to 2021
$22.5M
TRAINING OF ARTHRITIS RESEARCH SCIENTISTST32AR007080 · NIAMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jason Knight · 1986 to 2026
$6.3M
Improving Prediction of Medical Responsiveness and Clinical Outcomes in Crohn's DiseaseK23DK101687 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI STIDHAM, RYAN WILLIAM · 2015 to 2019
$945k
NIAMS NIH HHS T32 AR007080NIDDK NIH HHS K23 DK101687NIDDK NIH HHS P30 DK034933
6 · The paper itself

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.

Indexed as

Machine LearningAdultBiological TherapyC-Reactive ProteinCrohn DiseaseFemaleHumansMaleMiddle AgedPredictive Value of TestsRemission InductionSeverity of Illness IndexUstekinumabC-Reactive ProteinUstekinumab

Identifiers

PMID31074823
PMCPMC6512283

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Registered trials

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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.