In Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE : To develop a predictive machine learning model to identify prognostic factors for continued opioid prescriptions after arthroscopic meniscus surgery.
METHODS : Patients undergoing arthroscopic meniscal surgery, such as meniscus debridement, repair, or revision at a single institution from 2013 to 2017 were retrospectively followed up to 1 year postoperatively. Procedural details were recorded, including concomitant procedures, primary versus revision, and whether a partial debridement or a repair was performed. Intraoperative arthritis severity was measured using the Outerbridge Classification. The number of opioid prescriptions in each month were recorded. Primary analysis used was 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 variables.
RESULTS : A total of 581 patients 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, revision procedures, and presence of arthritis graded by the Outerbridge classification were not significant predictors of postoperative 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%, positive predictive value (PPV) of 83%, and a detection rate of 78.2%. The two most important features in the model were preoperative opioid use and male sex.
CONCLUSION : After arthroscopic meniscus surgery, preoperative opioid consumption and male sex were the most significant predictors for sustained opioid use beyond 1 month postoperatively. Intraoperative arthritis was not an independent risk factor for continued refills. A machine learning algorithm performed with high accuracy, although with a high false positive rate, to function as a screening tool to identify patients filling additional narcotic prescriptions after surgery.
Castle Joshua P, Jildeh Toufic R, Chaudhry Farhan, Turner Elizabeth Hg, Abbas Muhammad J, Mahmoud Ossama, Hengy Meredith, Okoroha Kelechi R, Lynch T Sean
2022-Dec-28
Machine Learning, Meniscal Repair, Meniscal Surgery, Meniscectomy, Opioids
