Evidence map›Paper›PMID 36520865›Full record

ArticlePloS one2022

Determinants of long-term opioid use in hospitalized patients.

Siyana Kurteva, Michal Abrahamowicz, Daniala Weir, Tara Gomes, Robyn Tamblyn

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

4 citing papers in PubMed, 7 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

5 authors at 4 institutions in 2 countries.

Siyana KurtevaDepartment of Epidemiology and Biostatistics, McGill University, Montreal, Canada.ORCID 0000-0002-1785-223X
Michal AbrahamowiczDepartment of Epidemiology and Biostatistics, McGill University, Montreal, Canada.
Daniala WeirDivision of Pharmacoepidemiology and Clinical Pharmacology, Department of Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands.
Tara GomesInstitute of Health Policy Management and Evaluation, Toronto, Canada.
Robyn TamblynDepartment of Epidemiology and Biostatistics, McGill University, Montreal, Canada.
McGill University · CAMcGill University Health Centre · CASt. Michael's Hospital · CAUtrecht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLong-term opioid use is an increasingly important problem related to the ongoing opioid epidemic. The purpose of this study was to identify patient, hospitalization and system-level determinants of long term opioid therapy (LTOT) among patients recently discharged from hospital.

designTo be eligible for this study, patient needed to have filled at least one opioid prescription three-months post-discharge. We retrieved data from the provincial health insurance agency to measure medical service and prescription drug use in the year prior to and after hospitalization. A multivariable Cox Proportional Hazards model was utilized to determine factors associated with time to the first LTOT occurrence, defined as time-varying cumulative opioid duration of ≥ 60 days.

resultsOverall, 22.4% of the 1,551 study patients were classified as LTOT, who had a mean age of 66.3 years (SD = 14.3). Having no drug copay status (adjusted hazard ratio (aHR) 1.91, 95% CI: 1.40-2.60), being a LTOT user before the index hospitalization (aHR 6.05, 95% CI: 4.22-8.68) or having history of benzodiazepine use (aHR 1.43, 95% CI: 1.12-1.83) were all associated with an increased likelihood of LTOT. Cardiothoracic surgical patients had a 40% lower LTOT risk (aHR 0.55, 95% CI: 0.31-0.96) as compared to medical patients. Initial opioid dispensation of > 90 milligram morphine equivalents (MME) was also associated with higher likelihood of LTOT (aHR 2.08, 95% CI: 1.17-3.69). CONCLUSIONS AND RELEVANCE: Several patient-level characteristics associated with an increased risk of ≥ 60 days of cumulative opioid use. The results could be used to help identify patients who are at high-risk of continuing opioids beyond guideline recommendations and inform policies to curb excessive opioid prescribing.

Indexed as

Analgesics, OpioidOpioid-Related DisordersAftercareAgedHumansPatient DischargePractice Patterns, Physicians'Retrospective StudiesAnalgesics, Opioid

Identifiers

PMID36520865
PMCPMC9754198
OpenAlexW4311552277

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.