Evidence map›Paper›PMID 33610181›Full record

Observational studyBMC family practice2021

How do contraindications to non-opioid analgesics and opioids affect the likelihood that patients with back pain diagnoses in the primary care setting receive an opioid prescription? An observational cross-sectional study.

Michelle S Keller, Lyna Truong, Allison M Mays, Jack Needleman, Mary Sue V Heilemann, Teryl K Nuckols

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMC family practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 36% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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 3 institutions in 1 country.

Michelle S KellerDivision of General Internal Medicine, Department of Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA. Michelle.keller@cshs.org.ORCID 0000-0002-8157-7586
Lyna TruongDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Allison M MaysDivision of Geriatrics, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jack NeedlemanDepartment of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, CA, USA.
Mary Sue V HeilemannUCLA School of Nursing, Los Angeles, CA, USA.
Teryl K NuckolsDivision of General Internal Medicine, Department of Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA.
Cedars-Sinai Medical Center · USUCLA Health · USUniversity of Michigan · US

Funding

UCLA Clinical and Translational Science InstituteTL1TR000121 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M. · 2012 to 2015
$2.7M
NCATS NIH HHS TL1 TR000121
6 · The paper itself

Abstract

backgroundGiven the risks of opioids, clinicians are under growing pressure to treat pain with non-opioid medications. Yet non-opioid analgesics such as non-steroidal anti-inflammatory drugs (NSAIDs) have their own risks: patients with kidney disease or gastrointestinal diseases can experience serious adverse events. We examined the likelihood that patients with back pain diagnoses and contraindications to NSAIDs and opioids received an opioid prescription in primary care.

methodsWe identified office visits for back pain from 2012 to 2017 and sampled the first office visit per patient per year (N = 24,543 visits). We created indicators reflecting contraindications for NSAIDs (kidney, liver, cardiovascular/cerebrovascular, and gastrointestinal diseases; concurrent or chronic use of anticoagulants/antiplatelets, chronic corticosteroid use) and opioids (depression, anxiety, substance use (SUD) and bipolar disorders, and concurrent benzodiazepines) and estimated four logistic regression models, with the one model including all patient visits and models 2-4 stratifying for previous opioid use. We estimated the population attributable risk for each contraindication.

resultsIn our model with all patients-visits, patients received an opioid prescription at 4% of visits. The predicted probability (PP) of receiving an opioid was 4% without kidney disease vs. 7% with kidney disease; marginal effect (ME): 3%; 95%CI: 1-4%). For chronic or concurrent anticoagulant/antiplatelet prescriptions, the PPs were 4% vs. 6% (ME: 2%; 95%CI: 1-3%). For concurrent benzodiazepines, the PPs were 4% vs. 11% (ME: 7%, 95%CI: 5-9%) and for SUD, the PPs were 4% vs. 5% (ME: 1%, 95%CI: 0-3%). For the model including patients with previous long-term opioid use, the PPs for concurrent benzodiazepines were 25% vs. 24% (ME: -1%; 95%CI: - 18-16%). The population attributable risk (PAR) for NSAID and opioid contraindications was small. For kidney disease, the PAR was 0.16% (95%CI: 0.08-0.23%), 0.44% (95%CI: 0.30-0.58%) for anticoagulants and antiplatelets, 0.13% for substance use (95%CI: 0.03-0.22%) and 0.20% for concurrent benzodiazepine use (95%CI: 0.13-0.26%).

conclusionsPatients with diagnoses of kidney disease and concurrent use of anticoagulants/antiplatelet medications had a higher probability of receiving an opioid prescription at a primary care visit for low back pain, but these conditions do not explain a large proportion of the opioid prescriptions.

Indexed as

Analgesics, Non-NarcoticAnalgesics, OpioidBack PainBenzodiazepinesContraindicationsCross-Sectional StudiesHumansPrescriptionsPrimary Health CareProbabilityAnalgesics, Non-NarcoticAnalgesics, OpioidBenzodiazepinesBack painBenzodiazepinesOpioids

Identifiers

PMID33610181
PMCPMC7896404
OpenAlexW3131674327

What OpenQuestion holds

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LicenceCC BY
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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.