Defining recovery trajectories after shoulder arthroplasty: a latent class analysis of patient-reported outcomes

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: Patients recover in a heterogenous manner following TSA. Through LCA, we identified different recovery trajectories for patients undergoing anatomic TSA and reverse TSA.

Keywords: Total shoulder arthroplasty; latent class analysis; patient-reported outcomes; postoperative trajectory; reverse total shoulder arthroplasty; shoulder arthritis.