J Douglas Thornton1, Nilanjana Dwibedi2, Virginia Scott3, Charles D Ponte4, Douglas Ziedonis5, Nethra Sambamoorthi6, Usha Sambamoorthi3. 1. Assistant Professor, College of Pharmacy, Department of Pharmaceutical Health Outcomes and Policy, University of Houston, TX. 2. Assistant Professor, School of Pharmacy, Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown. 3. Professor, School of Pharmacy, Department of Pharmaceutical Systems and Policy, West Virginia University. 4. Professor, Department of Clinical Pharmacy, School of Pharmacy, West Virginia University. 5. Professor and Associate Vice Chancellor for Health Sciences, University of California San Diego. 6. Adjunct Faculty, School of Professional Studies, Northwestern University, Chicago, IL.
Abstract
BACKGROUND: Opioids have been prescribed and used for chronic noncancer pain at prolific rates in the United States during the past 2 decades. Patients who transition to incident chronic opioid therapy are at increased risk for significant negative health consequences, including cardiovascular risk, endocrine disorders, opioid use disorder, and death. OBJECTIVE: To identify the leading predictors associated with transitioning to incident chronic opioid therapy among working-age adults without cancer. METHOD: This retrospective observational cohort study is based on medical and pharmacy claims of a nationally representative sample of adults enrolled in commercial health insurance plans. Standard parametric (logistic regressions) and nonparametric methods based on a decision tree were used for prediction. To facilitate comparison with the available published literature, we also present adjusted odds ratios (AORs) and 95% confidence intervals (CIs). The 10% random sample of 491,442 patients included in the study who were working-age adults (age, 28-63 years) were insured in a commercial health plan, did not have cancer, and initiated opioid therapy between January 2007 and May 2015. Transition to incident chronic opioid therapy was defined as having claims for at least a 90-day supply of opioids within 120 days after the index date (ie, initiation of opioid therapy). Predictive models used for the analysis comprised a comprehensive list of factors available in the claims data, including opioid regimen characteristics, pain conditions, physical and mental health conditions, concomitant medications use (ie, benzodiazepine, stimulants, nonopioid analgesics, and polypharmacy), patient characteristics, and health insurance type. RESULTS: In our sample, the transition to incident chronic opioid therapy was 1.3% and pain-specific diagnoses were documented for only one-third (31.7%) of patients. The 4 leading predictors of chronic opioid therapy were opioid duration of action (AOR, 12.28; 95% CI, 8.06-18.72), the parent opioid compound (eg, tramadol vs codeine; AOR, 7.26; 95% CI, 5.20-10.13), the presence of conditions that are very likely to cause chronic pain (AOR, 5.47; 95% CI, 3.89-7.68), and drug use disorders (AOR, 4.02; 95% CI, 2.53-6.40). CONCLUSION: The initial opioid regimen's characteristics are powerful predictors of chronic opioid therapy. Predictive algorithms created from readily available claims data can be used to develop real-time predictions of the future risk for a patient's transition to chronic opioid use.
BACKGROUND: Opioids have been prescribed and used for chronic noncancer pain at prolific rates in the United States during the past 2 decades. Patients who transition to incident chronic opioid therapy are at increased risk for significant negative health consequences, including cardiovascular risk, endocrine disorders, opioid use disorder, and death. OBJECTIVE: To identify the leading predictors associated with transitioning to incident chronic opioid therapy among working-age adults without cancer. METHOD: This retrospective observational cohort study is based on medical and pharmacy claims of a nationally representative sample of adults enrolled in commercial health insurance plans. Standard parametric (logistic regressions) and nonparametric methods based on a decision tree were used for prediction. To facilitate comparison with the available published literature, we also present adjusted odds ratios (AORs) and 95% confidence intervals (CIs). The 10% random sample of 491,442 patients included in the study who were working-age adults (age, 28-63 years) were insured in a commercial health plan, did not have cancer, and initiated opioid therapy between January 2007 and May 2015. Transition to incident chronic opioid therapy was defined as having claims for at least a 90-day supply of opioids within 120 days after the index date (ie, initiation of opioid therapy). Predictive models used for the analysis comprised a comprehensive list of factors available in the claims data, including opioid regimen characteristics, pain conditions, physical and mental health conditions, concomitant medications use (ie, benzodiazepine, stimulants, nonopioid analgesics, and polypharmacy), patient characteristics, and health insurance type. RESULTS: In our sample, the transition to incident chronic opioid therapy was 1.3% and pain-specific diagnoses were documented for only one-third (31.7%) of patients. The 4 leading predictors of chronic opioid therapy were opioid duration of action (AOR, 12.28; 95% CI, 8.06-18.72), the parent opioid compound (eg, tramadol vs codeine; AOR, 7.26; 95% CI, 5.20-10.13), the presence of conditions that are very likely to cause chronic pain (AOR, 5.47; 95% CI, 3.89-7.68), and drug use disorders (AOR, 4.02; 95% CI, 2.53-6.40). CONCLUSION: The initial opioid regimen's characteristics are powerful predictors of chronic opioid therapy. Predictive algorithms created from readily available claims data can be used to develop real-time predictions of the future risk for a patient's transition to chronic opioid use.
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