Utility of accumulated opioid supply days and individual patient factors in predicting probability of transitioning to long-term opioid use: An observational study in the Veterans Health Administration.

Abstract

Initial supply days dispensed to new users is strongly predictive of future long‐term opioid use (LTO). The objective was to examine whether a model integrating additional clinical variables conferred meaningful improvement in predicting LTO, beyond a simple approach using only accumulated supply. Three cohorts were created using Veteran's Health Administration data based on accumulated supply days during the 90 days following opioid initiation: (a) <30 days, (b) ≥30 days, (c) ≥60 days. A base, unadjusted probability of subsequent LTO (days 91‐365) was calculated for each cohort, along with an associated risk range based on midpoint values between cohorts. Within each cohort, log‐binomial regression modeled the probability of subsequent LTO, using demographic, diagnostic, and medication characteristics. Each patient's LTO probability was determined using their individual characteristic values and model parameter estimates, where values falling outside the cohort's risk range were considered a clinically meaningful change in predictive value. Base probabilities for subsequent LTO and associated risk ranges by cohort were as follows: (a) 3.92% (0%‐10.75%), (b) 17.59% (10.76%‐28.05%), (c) 38.53% (28.06%‐47.55%). The proportion of patients whose individual probability fell outside their cohort's risk range was as follows: 1.5%, 4.6%, and 9.2% for cohorts 1, 2, and 3, respectively. The strong relationship between accumulated supply days and future LTO offers an opportunity to leverage electronic healthcare records for decision support in preventing the initiation of inappropriate LTO through early intervention. More complex models are unlikely to meaningfully guide decision making beyond the single variable of accumulated supply days.