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

利用机器学习分析与全肩关节置换术后临床疗效显著改善相关的患者和影像学因素:对选择解剖型或反向型假体的启示

阅读:1

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/m(2), 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/m(2) and exhibit Walch B-C glenoids may be less likely to achieve the SCB following aTSA and should be considered for rTSA.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。