Evidence map›Paper›PMID 40486924›Full record

ArticlePain reports2025

Predictors of successful initiation of buprenorphine in enriched enrollment randomized withdrawal clinical trials in both opioid experienced and naïve participants: a participant-level meta-analysis.

Andrew Conroy, Warren B Bilker, Christopher J Miller, Charles E Argoff, Russell Bell, Jennifer Haythornthwaite, Jennifer S Gewandter, Ian Gilron, Katherine N Theken, John T Farrar

Abstract read
In one paragraph

Article in Pain reports, 2025. 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.

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

10 authors.

Andrew ConroyDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Warren B BilkerDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Christopher J MillerDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Charles E ArgoffDepartment of Neurology, Albany Medical Center, Albany, NY, USA.
Russell BellDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA.
Jennifer HaythornthwaiteDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jennifer S GewandterDepartment of Anesthesiology and Perioperative Medicine, Dentistry, Neurosurgery, University of Rochester, Rochester, NY, USA.
Ian GilronDepartment of Anesthesiology and Perioperative Medicine, Queens University School of Medicine, Kingston, ON, Canada.
Katherine N ThekenDepartment of Oral and Maxillofacial Surgery, University of Pennsylvania, Philadelphia, PA, USA.
John T FarrarDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.ORCID https://orcid.org/0000-0001-8656-5157

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: No prediction models exist for the success for buprenorphine initiation in opioid-naïve patients or in transition from other opioids in patients treated for chronic pain. Objectives: To create a prediction model for the successful use of buprenorphine to treat chronic pain. Methods: Stepwise Akaike information criterion prediction modeling procedures were applied to a harmonized participant-level data set of 10 enriched enrollment randomized withdrawal clinical trials of buprenorphine submitted to the Food and Drug Administration. Available baseline factors and nine patient-reported outcomes were considered to predict success with the titration (10 studies) and maintenance of benefit after randomization (5 studies). Patient-reported outcomes were modeled separately given inconsistent use across studies. Results: No prediction model reached an area under the receiver operator curve ≥0.70, the threshold for clinical usefulness. Successful initiation or transition of buprenorphine was accomplished in 3541 of 6052 (58.7%) participants, and 614 of 877 (70.0%) completed the 12-week maintenance phase with no difference between opioid-experienced and opioid-naïve participants. Only a medical history of obesity and baseline pain were retained in the overall titration model and only baseline pain in the maintenance model. Only brief pain inventory and subject opioid withdrawal scores were retained in the titration subsets containing those measures. Conclusion: No clinically useful prediction models of clinical benefit were identified, but a few covariates may be of interest in future studies of the initiation of buprenorphine in opioid-naïve patients or of transition from other opioids to buprenorphine. The lack of a predictor supports considering a trial of buprenorphine in clinically relevant scenarios for patients without known opioid use disorder, including careful monitoring and an a priori plan to deal with any problems that may occur.

Indexed as

BuprenorphineChronic painFood and drug administration dataHarmonized dataIndividual patient dataPrediction modelsSecondary analysis

Identifiers

PMID40486924
PMCPMC12144644

What Socratic holds

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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 Socratic graph.