Poster 215: Application of Machine Learning for Predicting Opioid Use After Arthroscopic Meniscal Surgery

OBJECTIVES: To identify predictive factors for continued opioid prescriptions after arthroscopic meniscal resection or repair and develop a predictive machine learning model. METHODS: Patients undergoing arthroscopic meniscal surgery between August 2013 and February 2017 at a single institution were retrospectively identified. Patient demographic variables were recorded including age, sex, body mass index, and history of chronic opioid usage (> 1 month). Procedural details were recorded such as concomitant procedures, primary versus revision, and whether a partial debridement or a repair was performed. Intraoperative arthritis severity was measured using the Outerbridge Classification. Types of opioid medications prescribed and in which months were documented. For primary analysis, we used a multivariate Cox-Regression model. We then created a naïve Bayesian model, a machine learning classifier that utilizes Bayes’ theorem with an assumption of independence between the variables collected. The model randomly selected 70% of the sample to be trained on while 30% were tested. RESULTS: A total of 735 patient were reviewed. Postoperative opioid refills occurred in 98 patients (16.9%). Using multivariate logistic modeling, independent risk factors for opioid refills included Male sex, larger BMI, chronic preoperative opioid use while meniscus resection demonstrated decreased likelihood of refills. Concomitant procedures, revisions, and presence of arthritis graded by the Outerbridge classification were not significant predictors of opioid refills. The Naïve Bayesian model for extended postoperative opioid use demonstrated good fit with our cohort with an area under the curve of 0.79, sensitivity of 94.5%, PPV of 83%, and a detection rate of 78.2%. CONCLUSIONS: After arthroscopic meniscus surgery, preoperative opioid consumption had the strongest association with sustained opioid use >1 month. Intraoperative arthritis was not an independent risk factor for continued refills. A novel machine learning algorithm performed with high accuracy and predictive ability to identify patients filling additional narcotic prescriptions after surgery.