Risk prediction models for prolonged postoperative opioid use: a systematic review and meta-analysis of drug-risk stratification, validation and evidence benchmarks
- 1Department of Anesthesiology, Zigong Fourth People’s Hospital
- 2Department of Anesthesiology, Zigong First People’s Hospital, Zigong Academy of Medical Sciences
Abstract
Prolonged postoperative opioid use represents a medication-safety problem in which short-term μ-opioid receptor agonist exposure may transition to sustained consumption, tolerance, opioid-induced hyperalgesia and dependence in susceptible patients. Risk prediction models could support clinical pharmacology-informed opioid stewardship by identifying patients who require individualised dosing, closer follow-up, alternative analgesic strategies or deprescribing support. We searched PubMed/MEDLINE, Embase, Web of Science, Scopus and the Cochrane Library from inception to 5 July 2026 for studies developing or validating multivariable models predicting opioid use beyond 30 days after surgery. One primary estimate was selected per independent cohort. Area under the receiver operating characteristic curve (AUROC) values were logit-transformed and pooled using random-effects restricted maximum likelihood models with Hartung-Knapp confidence intervals. 35 reports met the eligibility criteria and contributed 35 independent AUROC estimates to the quantitative synthesis. The pooled AUROC was 0.794 (95% CI 0.751–0.832), although between-study heterogeneity was extreme (I ² = 99%; 95% prediction interval, 0.512–0.934). Exploratory analyses suggested numerically lower discrimination in externally or temporally validated models and in opioid-naive cohorts, but these findings were not consistently supported by meta-regression. Current models identify a measurable drug-risk signal, yet few meet all four predefined translational-evidence benchmarks, and insufficient validation, calibration and clinical-utility evidence limits their immediate use for individualised opioid pharmacotherapy.
Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261442043 , identifier CRD420261442043.
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BibTeX
@article{liao2026risk,
title = {{Risk prediction models for prolonged postoperative opioid use: a systematic review and meta-analysis of drug-risk stratification, validation and evidence benchmarks}},
author = {Yin-sheng Liao and Ping Hou and Jian-li Song and Xuan Yu and Qiang Li and Bin Lu and Mao-yao Zheng and Guan-yu Chen},
journal = {Frontiers in Pharmacology},
year = {2026},
month = oct,
doi = {10.3389/fphar.2026.1946700},
url = {https://www.frontiersin.org/articles/10.3389/fphar.2026.1946700/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Risk prediction models for prolonged postoperative opioid use: a systematic review and meta-analysis of drug-risk stratification, validation and evidence benchmarks
AU - Yin-sheng Liao
AU - Ping Hou
AU - Jian-li Song
AU - Xuan Yu
AU - Qiang Li
AU - Bin Lu
AU - Mao-yao Zheng
AU - Guan-yu Chen
PY - 2026
DA - 2026/10/14
JO - Frontiers in Pharmacology
DO - 10.3389/fphar.2026.1946700
UR - https://www.frontiersin.org/articles/10.3389/fphar.2026.1946700/full
AB - Prolonged postoperative opioid use represents a medication-safety problem in which short-term μ-opioid receptor agonist exposure may transition to sustained consumption, tolerance, opioid-induced hyperalgesia and dependence in susceptible patients. Risk prediction models could support clinical pharmacology-informed opioid stewardship by identifying patients who require individualised dosing, closer follow-up, alternative analgesic strategies or deprescribing support. We searched PubMed/MEDLINE, Embase, Web of Science, Scopus and the Cochrane Library from inception to 5 July 2026 for studies developing or validating multivariable models predicting opioid use beyond 30 days after surgery. One primary estimate was selected per independent cohort. Area under the receiver operating characteristic curve (AUROC) values were logit-transformed and pooled using random-effects restricted maximum likelihood models with Hartung-Knapp confidence intervals. 35 reports met the eligibility criteria and contributed 35 independent AUROC estimates to the quantitative synthesis. The pooled AUROC was 0.794 (95% CI 0.751–0.832), although between-study heterogeneity was extreme (I ² = 99%; 95% prediction interval, 0.512–0.934). Exploratory analyses suggested numerically lower discrimination in externally or temporally validated models and in opioid-naive cohorts, but these findings were not consistently supported by meta-regression. Current models identify a measurable drug-risk signal, yet few meet all four predefined translational-evidence benchmarks, and insufficient validation, calibration and clinical-utility evidence limits their immediate use for individualised opioid pharmacotherapy. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261442043 , identifier CRD420261442043.
SN - 1663-9812
ER -