ArticleJournal of general internal medicine2018
Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy.
Article in Journal of general internal medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 3 of them syntheses that pooled it.
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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.
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.
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Who cites it
62 citing papers in PubMed, 3 syntheses or guidelines pooled it, 92 citations in OpenAlex.
- Predictive Models for Identifying Adult Patients at High Risk of Developing Opioid-Related Harms: a Systematic Review.Drug safety · 2025Pooled it
- Predictors of fatal and nonfatal overdose after prescription of opioids for chronic pain: a systematic review and meta-analysis of observational studies.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2023Pooled it
- Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.Journal of addiction medicinePooled it
- Naloxone Co-Dispensing with Opioids: a Cluster Randomized Pragmatic Trial.Journal of general internal medicine · 2022Trial
- Development and validation of a risk-score model for opioid overdose using a national claims database.Scientific reports · 2022Trial
- A clinical prediction model for outcome and therapy delivery in transplant-ineligible patients with myeloma (UK Myeloma Research Alliance Risk Profile): a development and validation study.The Lancet. Haematology · 2019Trial
- Methodological innovations to advance substance use disorder research: Proceedings of a NIDA workshop on target trial emulation and translational testing of digital health tools.Journal of substance use and addiction treatment · 2026Article
- Identification of antibiotic allergies in children using prescribing patterns in the electronic medical record.British journal of clinical pharmacology · 2026Article
- Review
- Opioid Overdose Death Prediction with Graph Neural Networks.medRxiv : the preprint server for health sciences · 2026Article
- Imminent opioid overdose risk prediction using classical and machine learning survival models following first recorded opioid-related diagnosis: a prospective cohort study from theThe American journal of drug and alcohol abuse · 2026Article
- Development and evaluation of machine learning algorithms for the prediction of opioid-related deaths among UK patients with non-cancer pain.PLOS digital health · 2026Article
- Opioid Overdose Death Prediction with Graph Neural Networks.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Community opioid dispensing prevalence and patterns in adults with spinal cord injury in Queensland: a retrospective data linkage study.Spinal cord · 2025Article
- Modeling opioid overdose events recurrence with a covariate-adjusted triggering point process.PLoS computational biology · 2025Article
- Predicting Fatal Drug Poisoning Among People Living with HIV-HCV Co-Infection.Canadian liver journal · 2025Article
- Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.IEEE journal of biomedical and health informatics · 2025Article
- Article
- Association between chiropractic spinal manipulation for sciatica and opioid-related adverse events: A retrospective cohort study.PloS one · 2025Article
- Long-Term Opioid Therapy and Risk of Opioid Overdose by Derived Clinical Indication in North Carolina, 2006-2018.Pharmacoepidemiology and drug safety · 2025Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundNaloxone is a life-saving opioid antagonist. Chronic pain guidelines recommend that physicians co-prescribe naloxone to patients at high risk for opioid overdose. However, clinical tools to efficiently identify patients who could benefit from naloxone are lacking.
objectiveTo develop and validate an overdose predictive model which could be used in primary care settings to assess the need for naloxone.
designRetrospective cohort.
settingDerivation site was an integrated health system in Colorado; validation site was a safety-net health system in Colorado.
participantsWe developed a predictive model in a cohort of 42,828 patients taking chronic opioid therapy and externally validated the model in 10,708 patients. MAIN MEASURES: Potential predictors and outcomes (nonfatal pharmaceutical and heroin overdoses) were extracted from electronic health records. Fatal overdose outcomes were identified from state vital records. To match the approximate shelf-life of naloxone, we used Cox proportional hazards regression to model the 2-year risk of overdose. Calibration and discrimination were assessed. KEY
resultsA five-variable predictive model showed good calibration and discrimination (bootstrap-corrected c-statistic = 0.73, 95% confidence interval [CI] 0.69-0.78) in the derivation site, with sensitivity of 66.1% and specificity of 66.6%. In the validation site, the model showed good discrimination (c-statistic = 0.75, 95% CI 0.70-0.80) and less than ideal calibration, with sensitivity and specificity of 82.2% and 49.5%, respectively.
conclusionsAmong patients on chronic opioid therapy, the predictive model identified 66-82% of all subsequent opioid overdoses. This model is an efficient screening tool to identify patients who could benefit from naloxone to prevent overdose deaths. Population differences across the two sites limited calibration in the validation site.
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Registered trials
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.