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

Social Determinants of Long-Term Opioid Use Following Total Knee Arthroplasty

Total knee arthroplasty (TKA) risks persistent pain and long-term opioid use (LTO). The role of social determinants of health (SDoH) in LTO is not well established. We hypothesized that SDoH would be associated with postsurgical LTO after controlling for relevant demographic and clinical variables. This study utilized data from the Veterans Affairs Surgical Quality Improvement Program, VA Corporate Data Warehouse, and Centers for Medicare and Medicaid Services, including Veterans aged ≥ 65 who underwent elective TKA between 2013 and 2019 with no postsurgical complications or history of significant opioid use. LTO was defined as > 90 days of opioid use beginning within 90 days postsurgery. SDoH variables included the Area Deprivation Index, rurality, and housing instability in the last 12 months identified via medical record screener or International Classification of Diseases, Tenth Revision codes. Multivariable risk adjustment models controlled for demographic and clinical characteristics. Of the 9,064 Veterans, 97% were male, 84.2% white, mean age was 70.6 years, 46.3% rural, 11.2% living in highly deprived areas, and 0.9% with a history of homelessness/housing instability. Only 3.7% (n = 336) developed LTO following TKA. In a logistic regression model of only SDoH variables, housing instability (odds ratio [OR] = 2.38, 95% confidence interval [CI]: 1.09-5.22) and rurality conferred significant risk for LTO. After adjusting for demographic and clinical variables, LTO was only associated with increasing days of opioid supply in the year prior to surgery (OR = 1.52, 95% CI: 1.43-1.63 per 30 days) and the initial opioid fill (OR = 1.07; 95% CI: 1.06-1.08 per day). Our primary hypothesis was not supported; however, our findings do suggest that patients with housing instability may present unique challenges for postoperative pain management and be at higher risk for LTO.

Effect of Multidisciplinary Transitional Pain Service on Health Care Use and Costs Following Orthopedic Surgery

Background

Opioid use disorder is a significant cause of morbidity, mortality, and health care costs. A transitional pain service (TPS) approach to perioperative pain management has been shown to reduce opioid use among patients undergoing orthopedic joint surgery. However, whether TPS also leads to lower health care use and costs is unknown.

Methods

We designed this study to estimate the effect of TPS implementation relative to standard care on health care use and associated costs of care following orthopedic surgery. We evaluated postoperative health care use and costs for patients who underwent orthopedic joint surgery at 6 US Department of Veterans Affairs medical centers (VAMCs) between 2018 and 2019 using difference-in-differences analysis. Patients enrolled in the TPS at the Salt Lake City VAMC were matched to control patients undergoing the same surgeries at 5 different VAMCs without a TPS. We stratified patients based on history of preoperative opioid use into chronic opioid use (COU) and nonopioid use (NOU) groups and analyzed them separately.

Results

For NOU patients, TPS was associated with a mean increase in the number of outpatient visits (6.9 visits; P < .001), no change in outpatient costs, and a mean decrease in inpatient costs (-$12,170; P = .02) during the 1-year follow-up period. TPS was not found to increase health care use or costs for COU patients.

Conclusions

Although TPS led to an increase in outpatient visits for NOU patients, there was no increase in outpatient costs and a decrease in inpatient costs after orthopedic surgery. Further, there was no added cost for managing COU patients with a TPS. These findings suggest that TPS can be implemented to reduce opioid use following joint surgery without increasing health care costs.

Opioid Prescribing Patterns for Acute Pain

PURPOSE

The current study aimed to identify patients presenting with acute pain who may be at risk for a complicated trajectory, via identifying clusters of early opioid prescribing patterns.

METHODS

National Veterans Affairs administrative data were utilized to build a cohort of outpatients with acute pain presentations and no more than minimal opioid use in the prior year. Latent Class Analyses (LCA) identified clusters of early opioid prescribing patterns. Risk of progression to long-term opioid use was contrasted between LCA clusters using log-binomial regression, adjusting for confounding variables.

RESULTS

The 2018 cohort included N = 191,283. Among the 27,890 who received an initial opioid prescription, LCA classes were identified using: first supply day, total days dispensed across 30 days, opioid type, dose, and number of prescriptions across the first 30 days. In the three-class model: class 1 indicated an immediate, low-dose, brief supply; class 2 included delayed, low-dose and longer duration prescriptions; and class 3 included delayed high-dose, and moderate duration. Adjusted relative risk ratios for progression to long-term opioid use in the following year were 3.33 (95% CI: 2.71-4.10) for class 1 (absolute risk 1.1%); 7.76 (95% CI: 6.69-8.99) for class 2 (3.1%); and 6.81 (95% CI: 5.72 - 8.12) for class 3 (2.4%); compared to patients who did not receive an acute opioid prescription (0.3%).

CONCLUSIONS

These clusters of acute opioid prescribing could facilitate identification of patients who may benefit from enhanced pain care earlier in the pain trajectory and decrease future reliance on long-term opioid therapy.

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

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.