Evidence map›Paper›PMID 37986977›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Personalizing treatment selection in Crohn's disease: a meta-analysis of individual participant data from fifteen randomized controlled trials.

Vivek A Rudrapatna, Vignesh G Ravindranath, Douglas V Arneson, Arman Mosenia, Atul J Butte, Shan Wang

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Vivek A RudrapatnaDivision of Gastroenterology, Department of Medicine, University of California, San Francisco, CA.ORCID 0000-0003-1789-3004
Vignesh G RavindranathBakar Computational Health Sciences Institute, University of California, San Francisco, CA.
Douglas V ArnesonBakar Computational Health Sciences Institute, University of California, San Francisco, CA.ORCID 0000-0002-9464-2586
Arman MoseniaSchool of Medicine, University of California, San Francisco, CA.ORCID 0000-0002-2645-2246
Atul J ButteBakar Computational Health Sciences Institute, University of California, San Francisco, CA.ORCID 0000-0002-7433-2740
Shan WangDepartment of Mathematics and Statistics, University of San Francisco, San Francisco, CA.ORCID 0000-0002-5442-1009
University of California, San Francisco · USUniversity of San Francisco · US

Funding

Clinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1M
TRAINING PROGRAM IN DISGESTIVE DISEASES AND NUTRITIONT32DK007007 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI AVERIL I MA · 1986 to 2026
$6.4M
A data science framework for transforming electronic health records into real-world evidenceK99LM014099 · NLM · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI RUDRAPATNA, VIVEK ASHOK · 2023 to 2024
$178k
NCATS NIH HHS UL1 TR001872NIDDK NIH HHS T32 DK007007NLM NIH HHS K99 LM014099
6 · The paper itself

Abstract

backgroundMeta-analyses have found anti-TNF drugs to be the best treatment, on average, for Crohn's disease. We performed a subgroup analysis to determine if it is possible to achieve more efficacious outcomes by individualizing treatment selection.

methodsWe obtained participant-level data from 15 trials of FDA-approved treatments (N=5703). We used sequential regression and simulation to model week six disease activity as a function of drug class, demographics, and disease-related features. We performed hypothesis testing to define subgroups based on rank-ordered preferences for treatments. We queried health records from University of California Health (UCH) to estimate the impacts these models could have on practice. We computed the sample size needed to prospectively test a prediction of our models.

results45% of the participants (N=2561) showed greater efficacy with at least one drug class (anti-TNF, anti-IL-12/23, anti-integrin) over another. They were classifiable into 6 subgroups, two showing greatest efficacy with anti-TNFs (36%, N=2064). Women over 50 showed superior responses with anti-IL-12/23s. Although they represented only 2% of the trial-based cohort, 25% of Crohn's patients at UCH are women over 50 (N=5,647), consistent with potential selection bias in trials. Moreover, 75% of biologic-exposed women over 50 did not receive an anti-IL12/23 first-line, supporting the potential value of these models. A future trial with 250 patients per arm will have 97% power to confirm the superiority of anti-IL-12/23s over anti-TNFs in these patients. A treatment recommendation tool is available at https://crohnsrx.org.

conclusionsPersonalizing treatment can improve outcomes in Crohn's disease. Future work is needed to confirm these findings, and improve representativeness in Crohn's trials.

Indexed as

clinical decision supportcomparative effectivenesscomparative efficacyCrohn’s diseaseheterogeneity of treatment effectindividual participant data meta-analysispersonalized medicineprecision medicinerandomized controlled trialssubgroup analysis

Identifiers

PMID37986977
PMCPMC10659518
OpenAlexW4388635085

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.