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  • Title: Association Between Preoperative Patient Factors and Clinically Meaningful Outcomes After Hip Arthroscopy for Femoroacetabular Impingement Syndrome: A Machine Learning Analysis.
    Author: Kunze KN, Polce EM, Clapp IM, Alter T, Nho SJ.
    Journal: Am J Sports Med; 2022 Mar; 50(3):746-756. PubMed ID: 35006010.
    Abstract:
    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.
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