Machine Learning Can Predict Level of Improvement in Shoulder Arthroplasty. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: McLendon, Paul B.; Christmas, Kaitlyn N.; Simon, Peter; Plummer, Otho R.; Hunt, Audrey; Ahmed, Adil S.; Mighell, Mark A.; Frankle, Mark A.
• Is Part Of: JB & JS open access. Volume 6:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Orthopedic surgery -- Periodicals; Orthopedic surgery; Periodicals; 617.4705
• DOI: 10.2106/JBJS.OA.20.00128
• Languages: English
• ISSNs: 2472-7245

Abstract

Abstract : Background: The ability to accurately predict postoperative outcomes is of considerable interest in the field of orthopaedic surgery. Machine learning has been used as a form of predictive modeling in multiple health-care settings. The purpose of the current study was to determine whether machine learning algorithms using preoperative data can predict improvement in American Shoulder and Elbow Surgeons (ASES) scores for patients with glenohumeral osteoarthritis (OA) at a minimum of 2 years after shoulder arthroplasty. Methods: This was a retrospective cohort study that included 472 patients (472 shoulders) diagnosed with primary glenohumeral OA (mean age, 68 years; 56% male) treated with shoulder arthroplasty (431 anatomic total shoulder arthroplasty and 41 reverse total shoulder arthroplasty). Preoperative computed tomography (CT) scans were used to classify patients on the basis of glenoid and rotator cuff morphology. Preoperative and final postoperative ASES scores were used to assess the level of improvement. Patients were separated into 3 improvement ranges of approximately equal size. Machine learning methods that related patterns of these variables to outcome ranges were employed. Three modeling approaches were compared: a model with the use of all baseline variables (Model 1), a model omitting morphological variables (Model 2), and a model omitting ASES variables (Model 3). Results: Improvement ranges of ⩽28 points (class A), 29 to 55 points (class B), and >55 points (class C) were established. Using all follow-up time intervals, Model 1 gave the most accurate predictions, with probability values of 0.94, 0.95, and 0.94 for classes A, B, and C, respectively. This was followed by Model 2 (0.93, 0.80, and 0.73) and Model 3 (0.77, 0.72, and 0.71). Conclusions: Machine learning can accurately predict the level of improvement after shoulder arthroplasty for glenohumeral OA. This may allow physicians to improve patient satisfaction by better managing expectations. These predictions were most accurate when latent variables were combined with morphological variables, suggesting that both patients' perceptions and structural pathology are critical to optimizing outcomes in shoulder arthroplasty. Level of Evidence: Therapeutic Level IV . See Instructions for Authors for a complete description of levels of evidence.