Long-term patient outcomes following buprenorphine/naloxone treatment for opioid use disorder: a retrospective analysis in a commercially insured population.

Background: While buprenorphine/naloxone (buprenorphine) has been demonstrated to be an effective medication for treating opioid use disorder (OUD), an important question exists about how long patients should remain in treatment.Objective: To examine the relationship between treatment duration and patient outcomes for individuals with OUD who have been prescribed buprenorphine.Methods: We conducted a retrospective, longitudinal study using the Massachusetts All Payer Claims Database, 2013 to 2017. The study comprised over 2,500 patients, approximately one-third of whom were female, who had been prescribed buprenorphine for OUD. The outcomes were hospitalizations and emergency room (ER) visits at 36 months following treatment initiation and 12 months following treatment discontinuation. Patients were classified into four groups based on treatment duration and medication adherence: poor adherence, duration <12 months; good adherence, duration <6 months; good adherence, duration 6 to 12 months, and good adherence, duration >12 months. We conducted analyses at the patient level of the relationship between duration and outcomes.Results: Better outcomes were observed for patients whose duration was greater than 12 months. Patients in the other groups had higher odds of hospitalization at 36 months following treatment initiation: poor adherence (2.71), <6 months (1.53), and 6 to 12 months (1.42). They also had higher odds of ER visits: poor adherence (1.69), <6 months (1.51), and 6 to 12 months (1.30). Similar results were observed following treatment discontinuation.Conclusions: OUD treatment with buprenorphine should be continued for at least 12 months to reduce hospitalizations and ED visits.

A machine learning based two-stage clinical decision support system for predicting patients' discontinuation from opioid use disorder treatment: retrospective observational study.

BACKGROUND: Buprenorphine is a widely used treatment option for patients with opioid use disorder (OUD). Premature discontinuation from this treatment has many negative health and societal consequences. OBJECTIVE: To develop and evaluate a machine learning based two-stage clinical decision-making framework for predicting which patients will discontinue OUD treatment within less than a year. The proposed framework performs such prediction in two stages: (i) at the time of initiating the treatment, and (ii) after two/three months following treatment initiation. METHODS: For this retrospective observational analysis, we utilized Massachusetts All Payer Claims Data (MA APCD) from the year 2013 to 2015. Study sample included 5190 patients who were commercially insured, initiated buprenorphine treatment between January and December 2014, and did not have any buprenorphine prescription at least one year prior to the date of treatment initiation in 2014. Treatment discontinuation was defined as at least two consecutive months without a prescription for buprenorphine. Six machine learning models (i.e., logistic regression, decision tree, random forest, extreme-gradient boosting, support vector machine, and artificial neural network) were tested using a five-fold cross validation on the input data. The first-stage models used patients' demographic information. The second-stage models included information on medication adherence during the early phase of treatment based on the proportion of days covered (PDC) measure. RESULTS: A substantial percentage of patients (48.7%) who started on buprenorphine discontinued the treatment within one year. The area under receiving operating characteristic curve (C-statistic) for the first stage models varied within a range of 0.55 to 0.59. The inclusion of knowledge regarding patients' adherence at the early treatment phase in terms of two-months and three-months PDC resulted in a statistically significant increase in the models' discriminative power (p-value < 0.001) based on the C-statistic. We also constructed interpretable decision classification rules using the decision tree model. CONCLUSION: Machine learning models can predict which patients are most at-risk of premature treatment discontinuation with reasonable discriminative power. The proposed machine learning framework can be used as a tool to help inform a clinical decision support system following further validation. This can potentially help prescribers allocate limited healthcare resources optimally among different groups of patients based on their vulnerability to treatment discontinuation and design personalized support systems for improving patients' long-term adherence to OUD treatment.