Review of factors, methods, and outcome definition in designing opioid abuse predictive models

Abdullah H. Alzeer, Josette Jones, Matthew Bair

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Objective. Several opioid risk assessment tools are available to prescribers to evaluate opioid analgesic abuse among chronic patients. The objectives of this study are to 1) identify variables available in the literature to predict opioid abuse; 2) explore and compare methods (population, database, and analysis) used to develop statistical models that predict opioid abuse; and 3) understand how outcomes were defined in each statistical model predicting opioid abuse. Design. The OVID database was searched for this study. The search was limited to articles written in English and published from January 1990 to April 2016. This search generated 1,409 articles. Only seven studies and nine models met our inclusion-exclusion criteria. Results. We found nine models and identified 75 distinct variables. Three studies used administrative claims data, and four studies used electronic health record data. The majority, four out of seven articles (six out of nine models), were primarily dependent on the presence or absence of opioid abuse or dependence (ICD-9 diagnosis code) to define opioid abuse. However, two articles used a predefined list of opioid-related aberrant behaviors. Conclusions. We identified variables used to predict opioid abuse from electronic health records and administrative data. Medication variables are the recurrent variables in the articles reviewed (33 variables). Age and gender are the most consistent demographic variables in predicting opioid abuse. Overall, there is similarity in the sampling method and inclusion/exclusion criteria (age, number of prescriptions, follow-up period, and data analysis methods). Intuitive research to utilize unstructured data may increase opioid abuse models? accuracy.

Original languageEnglish (US)
Pages (from-to)997-1009
Number of pages13
JournalPain Medicine (United States)
Volume19
Issue number5
DOIs
StatePublished - May 1 2018

Fingerprint

Opioid Analgesics
Electronic Health Records
Statistical Models
Demography
Databases
International Classification of Diseases
Prescriptions

Keywords

  • Addiction
  • Misuse
  • Opioid Abuse
  • Risk Assessment

ASJC Scopus subject areas

  • Clinical Neurology
  • Anesthesiology and Pain Medicine

Cite this

Review of factors, methods, and outcome definition in designing opioid abuse predictive models. / Alzeer, Abdullah H.; Jones, Josette; Bair, Matthew.

In: Pain Medicine (United States), Vol. 19, No. 5, 01.05.2018, p. 997-1009.

Research output: Contribution to journalArticle

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