Evidence map›Paper›PMID 30479746›Full record

ArticlePerioperative medicine (London, England)2018

Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.

Daniel I Rhon, Suzanne J Snodgrass, Joshua A Cleland, Charles D Sissel, Chad E Cook

Open access · goldAbstract read
In one paragraph

Article in Perioperative medicine (London, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
7.4field-weighted citation impact, top 3% 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

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.

  1. Pooled it
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  6. Diagnostic profiles associated with long-term opioid therapy in active duty servicemembers.PM & R : the journal of injury, function, and rehabilitation · 2024
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  12. Public Perceptions of Opioid Use Following Orthopedic Surgery: A Survey.HSS journal : the musculoskeletal journal of Hospital for Special Surgery · 2022
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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 5 institutions in 2 countries.

Daniel I Rhon1Center for the Intrepid, Brooke Army Medical Center, 3551 Roger Brooke Drive, JBSA Fort Sam, Houston, TX 78234 USA.ORCID 0000-0002-4320-990X
Suzanne J Snodgrass3School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, University Drive, Callaghan, NSW Australia.
Joshua A Cleland4Department of Physical Therapy, Franklin Pierce University, Manchester, NH USA.
Charles D Sissel5Program Analysis and Evaluation Division, US Army Medical Command, Joint Base San Antonio - Fort Sam Houston, San Antonio, TX 78234 USA.
Chad E Cook6Division of Physical Therapy, Department of Orthopedics, Duke University, Duke MSK, Duke Clinical Research Institute, Durham, NC USA.
Brooke Army Medical Center · USDuke University · USFranklin Pierce University · USUnited States Army Medical Command · USUniversity of Newcastle Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrescription opioid use at high doses or over extended periods of time is associated with adverse outcomes, including dependency and abuse. The aim of this study was to identify mediating variables that predict chronic opioid use, defined as three or more prescriptions after orthopedic surgery.

methodsIndividuals were ages between 18 and 50 years and undergoing arthroscopic hip surgery between 2004 and 2013. Two categories of chronic opioid use were calculated based on individuals (1) having three or more unique opioid prescriptions within 2 years and (2) still receiving opioid prescriptions > 1 year after surgery. Univariate elationships were identified for each predictor variable, then significant variables (

resultsThere were 1642 participants (mean age 32.5 years, SD 8.2, 54.1% male). Nine predictor variables met the criteria after bivariate analysis for potential inclusion in each multivariate model. Eight variables: socioeconomic status (from enlisted rank family), prior use of opioid medication, prior use of non-opioid pain medication, high health-seeking behavior before surgery, a preoperative diagnosis of insomnia, mental health disorder, or substance abuse were all predictive of chronic opioid use in the final model (seven variables for three or more opioid prescriptions; four variables for opioid use still at 1 year; all< 0.05). Post-test probability of having three or more opioid prescriptions was 93.7% if five of seven variables were present, and the probability of still using opioids after 1 year was 69.6% if three of four variables were present.

conclusionA combination of variables significantly predicted chronic opioid use in this cohort. Most of these variables were mediators, indicating that modifying them may be feasible, and the potential focus of interventions to decrease the risk of chronic opioid use, or at minimum better inform opioid prescribing decisions. This clinical prediction rule needs further validation.

Indexed as

Chronic opioid useHip surgeryOpioidsPostoperative carePrescription opioids

Identifiers

PMID30479746
PMCPMC6249901
OpenAlexW2897155112

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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 OpenQuestion graph.