1. Development and validation of a predictive model for persistent opioid use in new opioid analgesic users via a nationwide claims database.
- Author
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Noh, Yoojin, Heo, Kyu‐Nam, Cho, Won Bean, Lee, Ju‐Yeun, and Ah, Young‐Mi
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SUBSTANCE abuse risk factors , *DATABASES , *RISK assessment , *MEDICAL care use , *PREDICTION models , *HEALTH insurance reimbursement , *RESEARCH funding , *MULTIPLE regression analysis , *MULTIVARIATE analysis , *DESCRIPTIVE statistics , *AGE distribution , *TREATMENT duration , *POLYPHARMACY , *OPIOID analgesics , *RESEARCH methodology , *PHYSICIAN practice patterns , *DRUG prescribing , *CONFIDENCE intervals , *DRUG utilization , *COMORBIDITY , *EVALUATION - Abstract
Background: Chronic opioid use is associated with problematic opioid use, such as opioid abuse. It is important to develop a prediction model for safe opioid use. In this study, we aimed to develop and validate a risk score model for chronic opioid use in opioid‐naïve, noncancer patients, using data from a nationwide database. Methods: Data from the National Health Insurance Claims Database in the Republic of Korea from 2016 to 2018 were used, and adult, noncancer patients who were started on non‐injectable opioid analgesics (NIOAs) were included. The risk score model was developed using the β coefficient of each variable in the multivariable logistic regression analysis. Results: Overall, 676,676 noncancer patients were started on NIOAs, of which 65,877 (9.7%) were prescribed NIOAs chronically. Age, baseline healthcare utilization, comorbidities, co‐medications, and pattern of first NIOA prescription were identified as risk factors for chronic opioid use. The c‐static for the performance of our risk score model was 0.754 (95% confidence interval, 0.750–0.758). Conclusion: To our knowledge, this is the first tool that can predict chronic opioid use in the Korean population. The model can help physicians examine the risk of chronic opioid use by patients who are started on NIOA. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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