Association Between Preoperative Patient Factors and Clinically Meaningful Outcomes After Hip Arthroscopy for Femoroacetabular Impingement Syndrome: A Machine Learning Analysis

Background: The International Hip Outcome Tool 12-Item Questionnaire (IHOT-12) has been proposed as a more appropriate outcome assessment for hip arthroscopy populations. The extent to which preoperative patient factors predict achieving clinically meaningful outcomes among patients undergoing hip arthroscopy for femoroacetabular impingement syndrome (FAIS) remains poorly understood. Purpose: To determine the predictive relationship of preoperative imaging, patient-reported outcome measures, and patient demographics with achievement of the minimal clinically important difference (MCID), Patient Acceptable Symptom State (PASS), and substantial clinical benefit (SCB) for the IHOT-12 at a minimum of 2 years postoperatively. Study Design: Case-control study; Level of evidence, 3. Methods: Data were analyzed for consecutive patients who underwent hip arthroscopy for FAIS between 2012 and 2018 and completed the IHOT-12 preoperatively and at a minimum of 2 years postoperatively. Fifteen novel machine learning algorithms were developed using 47 potential demographic, clinical, and radiographic predictors. Model performance was evaluated with discrimination, calibration, decision-curve analysis and the brier score. Results: A total of 859 patients were identified, with 685 (79.7%) achieving the MCID, 535 (62.3%) achieving the PASS, and 498 (58.0%) achieving the SCB. For predicting the MCID, discrimination for the best-performing models ranged from fair to excellent (area under the curve [AUC], 0.69-0.89), although calibration was excellent (calibration intercept and slopes: –0.06 to 0.02 and 0.24 to 0.85, respectively). For predicting the PASS, discrimination for the best-performing models ranged from fair to excellent (AUC, 0.63-0.81), with excellent calibration (calibration intercept and slopes: 0.03-0.18 and 0.52-0.90, respectively). For predicting the SCB, discrimination for the best-performing models ranged from fair to good (AUC, 0.61-0.77), with excellent calibration (calibration intercept and slopes: –0.08 to 0.00 and 0.56 to 1.02, respectively). Thematic predictors for failing to achieve the MCID, PASS, and SCB were presence of back pain, anxiety/depression, chronic symptom duration, preoperative hip injections, and increasing body mass index (BMI). Specifically, thresholds associated with lower likelihood to achieve a clinically meaningful outcome were preoperative Hip Outcome Score–Activities of Daily Living <55, preoperative Hip Outcome Score–Sports Subscale >55.6, preoperative IHOT-12 score ≥48.5, preoperative modified Harris Hip Score ≤51.7, age >41 years, BMI ≥27, and preoperative α angle >76.6°. Conclusion: We developed novel machine learning algorithms that leveraged preoperative demographic, clinical, and imaging-based features to reliably predict clinically meaningful improvement after hip arthroscopy for FAIS. Despite consistent improvements after hip arthroscopy, meaningful improvements are negatively influenced by greater BMI, back pain, chronic symptom duration, preoperative mental health, and use of hip corticosteroid injections.

Opioid Medication Disposal Among Patients Following Hand Surgery

Background: Despite increased public awareness to dispose of unused narcotics, opioids prescribed postoperatively are retained, which may lead to drug diversion and abuse. This study assessed retention of unused opioids among hand surgery patients and describes disposal methods and barriers. Methods: Participants undergoing hand surgery were given an opioid disposal information sheet preoperatively (N = 222) and surveyed postoperatively to assess disposal or retention of unused opioids, disposal methods, and barriers to disposal. A binomial logistic regression was conducted to assess whether age, sex, pain intensity, and/or the type of procedure were predictors of opioid disposal. Results: There were 171 patients included in the analysis (n = 51 excluded; finished prescription or continued opioid use for pain control). Unused opioids were retained by 134 patients (78%) and disposal was reported by 37 patients (22%). Common disposal methods included returning opioids to a pharmacy (49%) or mixing them with an unwanted substance (24%). Reasons for retention included potential future use (54%), inconvenient disposal methods (21%), or keeping an unfilled prescription (9%). None of the patient factors analyzed (age, sex, type of procedure performed, or pain score) were predictors of disposal of unused narcotics ( P > .05). Conclusions: Most patients undergoing hand surgery retained prescribed opioids for future use or due to impractical disposal methods. The most common disposal methods included returning narcotics to a pharmacy or mixing opioids with unwanted substances. Identifying predictors of disposal may provide important information when developing strategies to increase opioid disposal.