ArticlePharmacoepidemiology and drug safety2019
Identifying and classifying opioid-related overdoses: A validation study.
Article in Pharmacoepidemiology and drug safety, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 4 of them syntheses that pooled it.
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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
45 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Predictive Models for Identifying Adult Patients at High Risk of Developing Opioid-Related Harms: a Systematic Review.Drug safety · 2025Pooled it
- Sensitivity and specificity of International Classification of Diseases algorithms (ICD-9 and ICD-10) used to identify opioid-related overdose cases: A systematic review and an example of estimation using Bayesian latent class models in the absence of gold standards.Canadian journal of public health = Revue canadienne de sante publique · 2024Pooled it
- Extracting social determinants of health from electronic health records using natural language processing: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled it
- Opioid Toxicity Following Concomitant Use of Macrolide Antibiotics with Fentanyl, Hydromorphone, or Oxycodone: A Population-Based Study.Clinical pharmacology and therapeutics · 2026Article
- Long-Term Opioid Therapy Tapering and Risk of Substance Use Disorder and Overdose: Differences by Opioid Dose and Duration.Journal of general internal medicine · 2026Article
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
- Buprenorphine and Methadone Discontinuation During Pregnancy and the Postpartum Period: A Nationwide Cohort Study.The American journal of psychiatry · 2025Article
- Treatment Initiation and Outcomes Associated With Receipt of Emergency Department-Based Peer Support Following Opioid Overdose.Annals of emergency medicine · 2025Article
- Population-level individualized prospective prediction of opioid overdose using machine learning.Molecular psychiatry · 2025Article
- A cohort study of predictors of short-term nonfatal suicidal and self-harm events among individuals with mental health disorders treated in the emergency department.Journal of psychiatric research · 2025Article
- Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.Drug safety · 2025Article
- Randomized Pilot of a Clinical Decision Support Tool to Increase Suicide Screening for at-Risk Primary Care Patients With Opioid Use Disorder.AJPM focus · 2024Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Article
- Association of medications for opioid use disorder with reduced risk of repeat opioid overdose in Medicaid: A cohort study.Journal of substance use and addiction treatment · 2024Article
- Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data.JAMIA open · 2023Article
- Fatal and nonfatal opioid overdose risk following release from prison: A retrospective cohort study using linked administrative data.Journal of substance use and addiction treatment · 2023Article
- Duration of medication treatment for opioid-use disorder and risk of overdose among Medicaid enrollees in 11 states: a retrospective cohort study.Addiction (Abingdon, England) · 2022Article
- Sensitivity of health records for self-reported nonfatal drug and alcohol overdose.The American journal on addictions · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe study aims to develop and validate algorithms to identify and classify opioid overdoses using claims and other coded data, and clinical text extracted from electronic health records using natural language processing (NLP).
methodsPrimary data were derived from Kaiser Permanente Northwest (2008-2014), an integrated health care system (~n > 475 000 unique individuals per year). Data included International Classification of Diseases, Ninth Revision (ICD-9) codes for nonfatal diagnoses, International Classification of Diseases, Tenth Revision (ICD-10) codes for fatal events, clinical notes, and prescription medication records. We assessed sensitivity, specificity, positive predictive value, and negative predictive value for algorithms relative to medical chart review and conducted assessments of algorithm portability in Kaiser Permanente Washington, Tennessee State Medicaid, and Optum.
resultsCode-based algorithm performance was excellent for opioid-related overdoses (sensitivity = 97.2%, specificity = 84.6%) and classification of heroin-involved overdoses (sensitivity = 91.8%, specificity = 99.0%). Performance was acceptable for code-based suicide/suicide attempt classifications (sensitivity = 70.7%, specificity = 90.5%); sensitivity improved with NLP (sensitivity = 78.7%, specificity = 91.0%). Performance was acceptable for the code-based substance abuse-involved classification (sensitivity = 75.3%, specificity = 79.5%); sensitivity improved with the NLP-enhanced algorithm (sensitivity = 80.5%, specificity = 76.3%). The opioid-related overdose algorithm performed well across portability assessment sites, with sensitivity greater than 96% and specificity greater than 84%. Cross-site sensitivity for heroin-involved overdose was greater than 87%, specificity greater than or equal to 99%.
conclusionsCode-based algorithms developed to detect opioid-related overdoses and classify them according to heroin involvement perform well. Algorithms for classifying suicides/attempts and abuse-related opioid overdoses perform adequately for use for research, particularly given the complexity of classifying such overdoses. The NLP-enhanced algorithms for suicides/suicide attempts and abuse-related overdoses perform significantly better than code-based algorithms and are appropriate for use in settings that have data and capacity to use NLP.
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