OBJECTIVE: Identify opioid abuse risk factors among chronic noncancer pain (CNCP) patients receiving long-term opioid therapy and assess healthcare resource use (HRU) among patients at elevated abuse risk. DESIGN: Data were obtained from an integrated administrative claims database. Classification and Regression Tree (CART) analysis identified risk factors potentially predictive of opioid abuse, which were used to classify the overall population into cohorts defined by levels of abuse risk. Multivariable logistic regression compared HRU across risk cohorts. SETTING: Retrospective cohort study. PATIENTS, PARTICIPANTS: 21,072 patients aged ≥18 years diagnosed with ≥1 of 5 types of CNCP and a prescription for Schedule II or III/IV opioid medication used long-term (≥90 days). MAIN OUTCOME MEASURES: (1) Opioid abuse risk factors; (2) HRU differences between risk cohorts. RESULTS: CART analysis identified four groups at elevated opioid abuse risk defined by three factors (age, daily opioid dose, and total days' supply of opioids); sensitivity: 70.3 percent, specificity: 74.1 percent, and positive predictive value: 5.6 percent. The analysis results were used to classify patients into low-risk (72.5 percent), at-risk (25.4 percent), and opioid-abuser (2.2 percent) cohorts. In multivariable analysis, emergency department (ED) use was higher among at-risk vs low-risk patients (odds ratio [OR]: 1.14; p<0.05); hospitalization and ED visits were higher for opioid-abusers vs low-risk patients (OR: 2.33 and 2.14, respectively; p<0.05). CONCLUSIONS: This study identifies a subpopulation of CNCP patients at risk of opioid abuse. However, limited sensitivity and specificity of criteria defining this subpopulation reinforce the importance of physician discretion in patient-level treatment decisions.
OBJECTIVE: Identify opioid abuse risk factors among chronic noncancer pain (CNCP) patients receiving long-term opioid therapy and assess healthcare resource use (HRU) among patients at elevated abuse risk. DESIGN: Data were obtained from an integrated administrative claims database. Classification and Regression Tree (CART) analysis identified risk factors potentially predictive of opioid abuse, which were used to classify the overall population into cohorts defined by levels of abuse risk. Multivariable logistic regression compared HRU across risk cohorts. SETTING: Retrospective cohort study. PATIENTS, PARTICIPANTS: 21,072 patients aged ≥18 years diagnosed with ≥1 of 5 types of CNCP and a prescription for Schedule II or III/IV opioid medication used long-term (≥90 days). MAIN OUTCOME MEASURES: (1) Opioid abuse risk factors; (2) HRU differences between risk cohorts. RESULTS: CART analysis identified four groups at elevated opioid abuse risk defined by three factors (age, daily opioid dose, and total days' supply of opioids); sensitivity: 70.3 percent, specificity: 74.1 percent, and positive predictive value: 5.6 percent. The analysis results were used to classify patients into low-risk (72.5 percent), at-risk (25.4 percent), and opioid-abuser (2.2 percent) cohorts. In multivariable analysis, emergency department (ED) use was higher among at-risk vs low-risk patients (odds ratio [OR]: 1.14; p<0.05); hospitalization and ED visits were higher for opioid-abusers vs low-risk patients (OR: 2.33 and 2.14, respectively; p<0.05). CONCLUSIONS: This study identifies a subpopulation of CNCP patients at risk of opioid abuse. However, limited sensitivity and specificity of criteria defining this subpopulation reinforce the importance of physician discretion in patient-level treatment decisions.
Authors: Lauren A Walter; Li Li; Erik P Hess; Joel B Rodgers; Jennifer J Hess; Rachel M Skains; Matthew C Delaney; James Booth Journal: JMIR Res Protoc Date: 2021-04-29