A machine learning analysis of patient and imaging factors associated with achieving clinically substantial outcome improvements following total shoulder arthroplasty: Implications for selecting anatomic or reverse prostheses.

Abstract

Background: Indications for reverse total shoulder arthroplasty(rTSA) continue to expand making it challenging to predict whether patients will benefit more from anatomic TSA(aTSA) or rTSA. The purpose of this study was to determine which factors differ between aTSA and rTSA patients that achieve meaningful outcomes and may influence surgical indication.

Methods: Random Forest dimensionality reduction was applied to reduce 23 features into a model optimizing substantial clinical benefit (SCB) prediction of the American Shoulder and Elbow Surgeon score using 1117 consecutive patients with 2-year follow up. Features were compared between aTSA patients stratified by SCB achievement and subsequently with rTSA SCB achievers.

Results: Eight combined features optimized prediction (accuracy = 87.1%, kappa = 0.73): (1) age, (2) body mass index (BMI), (3) sex, (4) history of rheumatic disease, (5) humeral head subluxation (HH) on computed tomography (CT), (6) HH-acromion distance on X-ray, (7) glenoid retroversion on CT, and (8) Walch classification on CT. A higher proportion of males (65.6% vs. 54.9%, p = 0.022), Walch B-C glenoid morphologies (49.5% vs. 37.9%, p < 0.001), and greater BMI (30.1 vs. 26.5 kg/m², p = 0.038) were observed in aTSA nonachievers compared with aTSA achievers, while aTSA nonachievers were statistically similar to rTSA achievers.

Discussion: Patients with glenohumeral osteoarthritis and intact rotator cuffs that have a BMI > 30 kg/m² and exhibit Walch B-C glenoids may be less likely to achieve the SCB following aTSA and should be considered for rTSA.