Evidence map›Paper›PMID 38641873›Full record

ArticleJournal of health psychology2024

Opioid Risk Tool, in-hospital opioid exposure, and opioid demand predict pain outcomes following traumatic injury.

Danielle A Kessler, Heather E Webber, Constanza de Dios, Jin H Yoon, Joy M Schmitz, Scott D Lane, John A Harvin, Angela M Heads, Charles E Green, Shweta Kapoor and 3 more

Open access · greenAbstract read
In one paragraph

Article in Journal of health psychology, 2024. 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
0.5field-weighted citation impact, top 33% 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, 1 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

13 authors at 3 institutions in 1 country.

Danielle A KesslerDrexel University College of Medicine at Tower Health, USA.
Heather E WebberUniversity of Texas Health Science Center at Houston, USA.ORCID 0000-0002-0910-3095
Constanza de DiosUniversity of Texas Health Science Center at Houston, USA.ORCID 0000-0003-0230-5222
Jin H YoonUniversity of Texas Health Science Center at Houston, USA.
Joy M SchmitzUniversity of Texas Health Science Center at Houston, USA.
Scott D LaneUniversity of Texas Health Science Center at Houston, USA.
John A HarvinUniversity of Texas Health Science Center at Houston, USA.
Angela M HeadsUniversity of Texas Health Science Center at Houston, USA.
Charles E GreenUniversity of Texas Health Science Center at Houston, USA.
Shweta KapoorMayo Clinic Alix School of Medicine, Mayo Clinic, USA.
Angela L StottsUniversity of Texas Health Science Center at Houston, USA.
Kandice L MotleyUniversity of Texas Health Science Center at Houston, USA.
Robert SuchtingUniversity of Texas Health Science Center at Houston, USA.
The University of Texas Health Science Center at Houston · USDrexel University · USMayo Clinic in Arizona · US

Funding

Convalescent Plasma to Limit Coronavirus Associated ComplicationsUL1TR003167 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KARP, DANIEL D, MCPHERSON, DAVID D · 2019 to 2023
$45.3M
Using event-related potentials to predict treatment outcomes in cocaine use disorderF32DA048542 · NIDA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI WEBBER, HEATHER E · 2020 to 2021
$134k
NCATS NIH HHS UL1 TR003167NIDA NIH HHS F32 DA048542
6 · The paper itself

Abstract

Prescribed opioids are a mainstay pain treatment after traumatic injury, but a subgroup of patients may be at risk for continued opioid use. We evaluated the predictive utility of a traditional screening tool, the Opioid Risk Tool (ORT), and two other measures: average in-hospital milligram morphine equivalents (MME) per day and an assessment of opioid demand in predicting pain outcomes. Assessments of pain-related outcomes (pain intensity, interference, injury-related stress, and need for additional pain treatment) were administered at 2 weeks and 12 months post-discharge in a sample of 34 patients hospitalized for traumatic injury. Bayesian linear models were used to evaluate changes in responses over time as a function of predictors. High-risk ORT, higher MME per day, and greater opioid demand predicted less change in outcomes over time. This report provides first evidence that malleable factors of opioid and opioid demand have utility in predicting pain outcomes following traumatic injury.

Indexed as

Analgesics, OpioidPainWounds and InjuriesAdultBayes TheoremFemaleHumansMaleMiddle AgedOpioid-Related DisordersPain MeasurementRisk AssessmentYoung AdultAnalgesics, Opioiddrugshospitalizationpainrisk factorstrauma

Identifiers

PMID38641873
PMCPMC12781970
OpenAlexW4394978909

What OpenQuestion holds

Textmetadata
LicenceTDM
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.