Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, USA.
Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, USA.
J Shoulder Elbow Surg. 2021 Oct;30(10):2375-2385. doi: 10.1016/j.jse.2021.02.024. Epub 2021 Mar 19.
Patients undergoing total shoulder arthroplasty (TSA) can have varying levels of improvement after surgery. As patients typically demonstrate a nonlinear recovery trajectory, advanced analysis investigating the degrees of variation in outcomes is needed. Latent class analysis (LCA) is a mixed and multilevel model that estimates random slope variance to evaluate heterogeneity in outcome patterns among patient subgroups and can be used to outline differing recovery trajectories. The purpose of this study was to determine recovery trajectory patterns after TSA and to identify factors that predict a given trajectory.
Data from a prospectively collected single institutional database of patients undergoing anatomic and reverse TSA were utilized. Patients were included if they had American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES) scores preoperatively, as well as postoperative scores at 6 weeks, 6 months, 1 year, and 2 years. Patients were excluded if they underwent a revision procedure or hemiarthroplasty or had prior infection. LCA was used to subdivide the patient cohort into subclasses based on postoperative recovery trajectory. This was performed for all patients as well as anatomic TSA and reverse TSA as separate groups. Unpaired Student t tests, analysis of variance, and Fisher exact test were used to compare classes based on factors including age, body mass index, sex, preoperative diagnosis, and type of arthroplasty.
A total of 244 TSAs were included in the final analysis, comprising 89 anatomic TSA and 155 reverse TSA. In the combined group, LCA modeling revealed 3 patterns for recovery: Resistant Responders had low baseline scores (ASES < 30) and poor final results (ASES < 50), Steady Progressors had moderate baseline scores (ASES 30-50) with moderate final results (ASES 50-75), and High Performers had moderate baseline scores (ASES > 50) with excellent final results (ASES > 75). For anatomic TSA, we identified Delayed Responders with moderate baseline scores and a delayed response before ultimately achieving moderate final results, Steady Progressors with moderate baseline scores and a steady progression to achieve moderate final results, and High Performers who had moderate baseline scores and excellent final results. For reverse TSA, we identified Late Regressors with low baseline scores and poor final results, Steady Progressors with moderate baseline scores and moderate final results, and High Performers with moderate baseline scores and excellent final results.
Patients recover in a heterogenous manner following TSA. Through LCA, we identified different recovery trajectories for patients undergoing anatomic TSA and reverse TSA.
接受全肩关节置换术（TSA）的患者术后可能会有不同程度的改善。由于患者通常表现出非线性的恢复轨迹，因此需要对结果的变化程度进行高级分析。潜在类别分析（LCA）是一种混合和多层次模型，可估计随机斜率方差，以评估患者亚组之间结果模式的异质性，并可用于概述不同的恢复轨迹。本研究的目的是确定 TSA 后的恢复轨迹模式，并确定预测特定轨迹的因素。
使用前瞻性收集的解剖型和反式 TSA 单机构数据库中的数据。如果患者术前具有美国肩肘外科医师协会（ASES）标准肩部评估表（ASES）评分，以及术后 6 周、6 个月、1 年和 2 年的评分，则将其纳入研究。如果患者接受了翻修手术或半肩置换术或有既往感染，则将其排除在外。LCA 用于根据术后恢复轨迹将患者队列细分为亚类。对所有患者以及解剖型 TSA 和反式 TSA 作为单独的组进行了此操作。使用未配对的 Student t 检验、方差分析和 Fisher 确切检验，根据年龄、体重指数、性别、术前诊断和关节置换类型等因素对类进行比较。
共有 244 例 TSA 最终纳入分析，其中包括 89 例解剖型 TSA 和 155 例反式 TSA。在联合组中，LCA 建模显示有 3 种恢复模式：耐药反应者基线评分较低（ASES<30），最终结果较差（ASES<50）；稳定进展者基线评分中等（ASES 30-50），最终结果中等（ASES 50-75）；高表现者基线评分中等（ASES>50），最终结果良好（ASES>75）。对于解剖型 TSA，我们确定了延迟反应者，其基线评分中等，反应延迟，最终结果中等；稳定进展者，其基线评分中等，稳定进展，最终结果中等；高表现者，其基线评分中等，最终结果良好。对于反式 TSA，我们确定了低基线评分和较差最终结果的迟滞消退者、中等基线评分和中等最终结果的稳定进展者以及中等基线评分和良好最终结果的高表现者。
患者接受 TSA 治疗后恢复方式存在异质性。通过 LCA，我们确定了接受解剖型 TSA 和反式 TSA 治疗的患者的不同恢复轨迹。
