Evidence map›Paper›PMID 35379650›Full record

SynthesisBMJ (Clinical research ed.)2022

Efficacy of interventions to reduce long term opioid treatment for chronic non-cancer pain: systematic review and meta-analysis.

Nicholas Avery, Amy G McNeilage, Fiona Stanaway, Claire E Ashton-James, Fiona M Blyth, Rebecca Martin, Ali Gholamrezaei, Paul Glare

Open access · hybridAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in BMJ (Clinical research ed.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 4 pooled it
9.0field-weighted citation impact, top 2% of its field
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

35 citing papers in PubMed, 4 syntheses or guidelines pooled it, 48 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Nicholas AverySydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.ORCID 0000-0002-2060-312X
Amy G McNeilageSydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.ORCID 0000-0001-7112-042X
Fiona StanawaySchool of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.ORCID 0000-0003-2104-3010
Claire E Ashton-JamesSydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.ORCID 0000-0001-5547-4295
Fiona M BlythPain Management Research Institute, Kolling Institute, University of Sydney, Sydney, NSW, Australia.ORCID 0000-0003-1128-6756
Rebecca MartinMichael J Cousins Pain Management and Research Centre, Royal North Shore Hospital, Sydney, NSW, Australia.ORCID 0000-0001-9588-3974
Ali GholamrezaeiSydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.ORCID 0000-0001-8674-450X
The University of Sydney · AURoyal North Shore Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo review interventions to reduce long term opioid treatment in people with chronic non-cancer pain, considering efficacy on dose reduction and discontinuation, pain, function, quality of life, withdrawal symptoms, substance use, and adverse events.

designSystematic review and meta-analysis of randomised controlled trials and non-randomised studies of interventions. DATA SOURCES: Medline, Embase, PsycINFO, CINAHL, and the Cochrane Library searched from inception to July 2021. Reference lists and previous reviews were also searched and experts were contacted. ELIGIBILITY CRITERIA FOR STUDY SELECTION: Original research in English. Case reports and cross sectional studies were excluded. DATA EXTRACTION AND SYNTHESIS: Two authors independently selected studies, extracted data, and used the Cochrane risk-of-bias tools for randomised and non-randomised studies (RoB 2 and ROBINS-I). Authors grouped interventions into five categories (pain self-management, complementary and alternative medicine, pharmacological and biomedical devices and interventions, opioid replacement treatment, and deprescription methods), estimated pooled effects using random effects meta-analytical models, and appraised the certainty of evidence using GRADE (grading of recommendations, assessment, development, and evaluation).

resultsOf 166 studies meeting inclusion criteria, 130 (78%) were considered at critical risk of bias and were excluded from the evidence synthesis. Of the 36 included studies, few had comparable treatment arms and sample sizes were generally small. Consequently, the certainty of the evidence was low or very low for more than 90% (41/44) of GRADE outcomes, including for all non-opioid patient outcomes. Despite these limitations, evidence of moderate certainty indicated that interventions to support prescribers' adherence to guidelines increased the likelihood of patients discontinuing opioid treatment (adjusted odds ratio 1.5, 95% confidence interval 1.0 to 2.1), and that these prescriber interventions as well as pain self-management programmes reduced opioid dose more than controls (intervention

conclusionsEvidence on the reduction of long term opioid treatment for chronic pain continues to be constrained by poor study methodology. Of particular concern is the lack of evidence relating to possible harms. Agreed standards for designing and reporting studies on the reduction of opioid treatment are urgently needed. REVIEW REGISTRATION: PROSPERO CRD42020140943.

Indexed as

Analgesics, OpioidChronic PainCross-Sectional StudiesHumansPain ManagementQuality of LifeAnalgesics, Opioid

Identifiers

PMID35379650
PMCPMC8977989
OpenAlexW4224236297

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.