Evidence map›Paper›PMID 39466155›Full record

Observational studyCritical care explorations2024

Risk Factors for Increased Opioid Use During Postoperative Intensive Care.

Lauriane Guichard, Milo C Engoren, Yi-Ju Li, Matthew J Sigakis, Xinming An, Chad M Brummett, Matthew C Mauck, Karthik Raghunathan, Daniel J Clauw, Vijay Krishnamoorthy

Abstract readObservational Study
In one paragraph

Observational study in Critical care explorations, 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
–field-weighted citation impact
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. Observational
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

10 authors.

Lauriane GuichardDuke University Department of Anesthesiology and Critical Care Medicine, Critical Care and Perioperative Population Health Research (CAPER) Group, Durham, NC.ORCID 0000-0001-9537-2592
Milo C EngorenDepartment of Anesthesiology and Critical Care, University of Michigan, Ann Arbor, MI.
Yi-Ju LiDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC.
Matthew J SigakisDepartment of Anesthesiology and Critical Care, University of Michigan, Ann Arbor, MI.
Xinming AnDepartment of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Chad M BrummettDepartment of Anesthesiology and Critical Care, University of Michigan, Ann Arbor, MI.
Matthew C MauckDepartment of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Karthik RaghunathanDuke University Department of Anesthesiology and Critical Care Medicine, Critical Care and Perioperative Population Health Research (CAPER) Group, Durham, NC.
Daniel J ClauwChronic Pain and Fatigue Research Center, Department of Anesthesiology, University of Michigan, Ann Arbor, MI.
Vijay KrishnamoorthyDuke University Department of Anesthesiology and Critical Care Medicine, Critical Care and Perioperative Population Health Research (CAPER) Group, Durham, NC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

importanceIn the ICU, opioids treat pain and improve ventilator tolerance as part of an analgosedation approach. Identifying predictors of opioid consumption during the ICU course might highlight actionable items to reduce opioid consumption.

objectivesTo identify risk factors for opioid use during a postoperative ICU course. DESIGN, SETTING, AND

participantsPatients enrolled in the Michigan Genomics Initiative single-center prospective observational cohort study completed baseline preoperative sociodemographic and mental/physical health questionnaires and provided blood samples for genetic analysis. Included patients were 18 years old and older, admitted to ICU postoperatively, and received opioids postoperatively. MAIN OUTCOMES AND MEASURES: The primary outcome was ICU mean daily oral morphine equivalent (OME) use. The association between OME and phenotypic risk factors and genetic variants previously associated with pain were analyzed through univariable and multivariable linear regression models.

resultsThe cohort consisted of 1865 mixed-surgical patients with mean age of 56 years (sd, 15 yr). Preoperative opioid users were more likely to continue to receive opioids throughout their ICU stay than opioid-naive patients. OME (log10 scale) was most strongly associated with ICU mechanical ventilation (β = 0.27; 95% CI, 0.15-0.38; p < 0.0001; effect size 1.85 for receiving > 24 hours of mechanical ventilation), preoperative opioid use (β = 0.22; 95% CI, 0.16-0.29; p < 0.0001; effect size 1.67 for receiving preoperative opioids), major surgery (β = 0.21; 95% CI, 0.12-0.30; p < 0.0001; effect size 1.62 compared with minor surgery), and current/former illicit drug use (β = 0.12; 95% CI, 0.01-0.23; p = 0.04; effect size 1.30 for drug use). Younger age, centralized pain, and longer anesthetic duration were also significantly associated with OME but with smaller effect sizes. Selected genetic variants (FKBP5, COMT, and OPRM1) were not associated with OME use. CONCLUSIONS AND RELEVANCE: Mechanical ventilation and preoperative opioids were the strongest risk factors for postoperative ICU opioid consumption, whereas psychologic factors and genetic variants were not associated.

Indexed as

Analgesics, OpioidIntensive Care UnitsPostoperative PainAdultAgedCohort StudiesCritical CareFemaleHumansMaleMichiganMiddle AgedPostoperative CareProspective StudiesRespiration, ArtificialRisk FactorsAnalgesics, Opioid

Identifiers

PMID39466155
PMCPMC11519407

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.