Machine Learning Algorithms Predict Achievement of Clinically Significant Outcomes Following Orthopaedic Surgery: A Systematic Review.
Review
Overview
abstract
PURPOSE: To determine what subspecialties have applied ML to predict CSO within orthopaedic surgery and to determine whether the performance of these models was acceptable through assessing discrimination and other ML metrics. METHODS: PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases were queried for articles that used ML to predict achieving the minimal clinically important difference (MCID), patient acceptable symptomatic state (PASS), or substantial clinical benefit (SCB) following orthopaedic surgeries. Data pertaining to demographics, subspecialty, specific machine learning algorithms, and algorithm performance were analyzed. RESULTS: Eighteen articles met the inclusion criteria. Seventeen studies developed novel algorithms, while one externally validated an established algorithm. All studies used ML to predict MCID achievement, while three (16.7%) predicted SCB achievement, and none PASS achievement. Seven (38.9%) studies concerned outcomes after spine surgery, six (33.3%) after sports medicine surgery, three (16.7%) after total joint arthroplasty (TJA), and two (11.1%) after shoulder arthroplasty. No studies were found in trauma, hand, elbow, pediatric, or foot/ankle surgery. In spine surgery, c-statistics ranged between 0.65-0.92; in hip arthroscopy, 0.51-0.94; in TJA, 0.63-0.89; in shoulder arthroplasty, between 0.70-0.95. The majority of studies reported c-statistics on the upper end of these ranges, though populations were heterogeneous. CONCLUSION: Currently available ML algorithms can discriminate between propensity to achieve CSO using MCID after spine, TJA, sports medicine, and shoulder surgery with fair to good performance as evidenced by C-statistics ranging between 0.6-0.95 in the majority of analyses. Less evidence is available on the ability of ML to predict achievement of SCB, and no evidence is available for achieving PASS. Such algorithms may augment shared decision-making practices and allow clinicians to provide more appropriate patient expectations using individualized risk assessments. However, these studies remain limited by variable reporting performance metrics, CSO quantification methods, adherence to predictive modeling guidelines, and limited external validation.
