ArticleJournal of general internal medicine2018
Prediction of Future Chronic Opioid Use Among Hospitalized Patients.
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 27 papers.
What it found
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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
27 citing papers in PubMed, 56 citations in OpenAlex.
- The Patterns and Trajectory of Continued Opioid Use in Opioid-Naïve Patients After Curative-Intent Elective Cancer Surgery-A Prospective Cohort Study.Pain practice : the official journal of World Institute of Pain · 2026Article
- High-impact chronic pain after total joint arthroplasty: a longitudinal cohort study.Pain reports · 2026Article
- Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning.Journal of substance use and addiction treatment · 2025Article
- A systematic review of machine learning applications in predicting opioid associated adverse events.NPJ digital medicine · 2025Article
- Long-Term Opioid Therapy and Risk of Opioid Overdose by Derived Clinical Indication in North Carolina, 2006-2018.Pharmacoepidemiology and drug safety · 2025Article
- Ten Priorities for Research Addressing the Intersections of Brain Injury, Mental Health and Addictions: A Stakeholder-Driven Priority-Setting Study.Health expectations : an international journal of public participation in health care and health policy · 2024Article
- Predictors of frequent emergency department visits among hospitalized cancer patients: a comparative cohort study using integrated clinical and administrative data to improve care delivery.BMC health services research · 2023Article
- External validation of a model to predict future chronic opioid use among hospitalized patients.Journal of hospital medicine · 2023Article
- Risk factors for opioid use disorder after severe burns in adults.American journal of surgery · 2023Article
- Intended and unintended consequences: Changes in opioid prescribing practices for postsurgical, acute, and chronic pain indications following two policies in North Carolina, 2012-2018 - Controlled and single-series interrupted time series analyses.Drug and alcohol dependence · 2023Article
- Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data.Complex psychiatry · 2022Article
- Opioid and benzodiazepine prescribing after COVID-19 hospitalization.Journal of hospital medicine · 2022Article
- Chronic Pain Prevalence and Factors Associated With High Impact Chronic Pain following Total Joint Arthroplasty: An Observational Study.The journal of pain · 2022Observational
- The path from big data analytics capabilities to value in hospitals: a scoping review.BMC health services research · 2022Article
- Identifying risk of opioid use disorder for patients taking opioid medications with deep learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Effect of preoperative long-term opioid therapy on patient outcomes after total knee arthroplasty: an analysis of multicentre population-based administrative data.Canadian journal of surgery. Journal canadien de chirurgie · 2021Article
- A spatiotemporal analysis of opioid poisoning mortality in Ohio from 2010 to 2016.Scientific reports · 2021Article
- Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients.Frontiers in artificial intelligence · 2021Article
- Development of a machine learning algorithm for early detection of opioid use disorder.Pharmacology research & perspectives · 2020Article
- Demographic, Clinical, and Prescribing Characteristics Associated with Future Opioid Use in an Opioid-Naive Population in an Integrated Health System.The Permanente journal · 2020Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundOpioids are commonly prescribed in the hospital; yet, little is known about which patients will progress to chronic opioid therapy (COT) following discharge. We defined COT as receipt of ≥ 90-day supply of opioids with < 30-day gap in supply over a 180-day period or receipt of ≥ 10 opioid prescriptions over 1 year. Predictive tools to identify hospitalized patients at risk for future chronic opioid use could have clinical utility to improve pain management strategies and patient education during hospitalization and discharge.
objectiveThe objective of this study was to identify a parsimonious statistical model for predicting future COT among hospitalized patients not on COT before hospitalization.
designRetrospective analysis electronic health record (EHR) data from 2008 to 2014 using logistic regression. PATIENTS: Hospitalized patients at an urban, safety net hospital. MAIN MEASUREMENTS: Independent variables included medical and mental health diagnoses, substance and tobacco use disorder, chronic or acute pain, surgical intervention during hospitalization, past year receipt of opioid or non-opioid analgesics or benzodiazepines, opioid receipt at hospital discharge, milligrams of morphine equivalents prescribed per hospital day, and others. KEY
resultsModel prediction performance was estimated using area under the receiver operator curve, accuracy, sensitivity, and specificity. A model with 13 covariates was chosen using stepwise logistic regression on a randomly down-sampled subset of the data. Sensitivity and specificity were optimized using the Youden's index. This model predicted correctly COT in 79% of the patients and no COT correctly in 78% of the patients.
conclusionsOur model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.
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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.