Objective: Develop a risk index to estimate the likelihood of life-threatening respiratory depression or overdose among medical users of prescription opioids.
Subjects, design, and methods: A case-control analysis of administrative health care data from the Veterans' Health Administration identified 1,877,841 patients with a pharmacy record for an opioid prescription between October 1, 2010 and September 30, 2012. Overdose or serious opioid-induced respiratory depression (OSORD) occurred in 817. Ten controls were selected per case (n = 8,170). Items for an OSORD risk index (RIOSORD) were selected through logistic regression modeling, with point values assigned to each predictor. Modeling of risk index scores produced predicted probabilities of OSORD; risk classes were defined by the predicted probability distribution.
Results: Fifteen variables most highly associated with OSORD were retained as items, including mental health disorders and pharmacotherapy; impaired drug metabolism or excretion; pulmonary disorders; specific opioid characteristics; and recent hospital visits. The average predicted probability of experiencing OSORD ranged from 3% in the lowest risk decile to 94% in the highest, with excellent agreement between predicted and observed incidence across risk classes. The model's C-statistic was 0.88 and Hosmer-Lemeshow goodness-of-fit statistic 10.8 (P > 0.05).
Conclusion: RIOSORD performed well in identifying medical users of prescription opioids within the Veterans' Health Administration at elevated risk of overdose or life-threatening respiratory depression, those most likely to benefit from preventive interventions. This novel, clinically practical, risk index is intended to provide clinical decision support for safer pain management. It should be assessed, and refined as necessary, in a more generalizable population, and prospectively evaluated.
Keywords: Index; Opioid; Overdose; Questionnaire; Respiratory Depression; Risk.
© 2015 The Authors Pain Medicine published by Wiley Periodicals, Inc. on behalf of American Academy of Pain Medicine.