Inconsistent selection of outcomes and measurement devices found in shoulder arthroplasty research: An analysis of studies on ClinicalTrials.gov

Abstract

Introduction: Recent evidence suggests a lack of standardization of shoulder arthroplasty outcomes. This issue is a limiting factor in systematic reviews. Core outcome set (COS) methodology could address this problem by delineating a minimum set of outcomes for measurement in all shoulder arthroplasty trials.

Methods: A ClinicalTrials.gov search yielded 114 results. Eligible trials were coded on the following characteristics: study status, study type, arthroplasty type, sample size, measured outcomes, outcome measurement device, specific metric of measurement, method of aggregation, outcome classification, and adverse events.

Results: Sixty-six trials underwent data abstraction and data synthesis. Following abstraction, 383 shoulder arthroplasty outcomes were organized into 11 outcome domains. The most commonly reported outcomes were shoulder outcome score (n = 58), pain (n = 33), and quality of life (n = 15). The most common measurement devices were the Constant-Murley Shoulder Outcome Score (n = 38) and American Shoulder and Elbow Surgeons Shoulder Score (n = 33). Temporal patterns of outcome use was also found.

Conclusion: Our study suggests the need for greater standardization of outcomes and instruments. The lack of consistency across trials indicates that developing a core outcome set for shoulder arthroplasty trials would be worthwhile. Such standardization would allow for more effective comparison across studies in systematic reviews, while at the same time consider important outcomes that may be underrepresented otherwise. This review of outcomes provides an evidence-based foundation for the development of a COS for shoulder arthroplasty.

Fig 1. PRISMA flowchart.

PRISMA flowchart displaying the search results along with the included and excluded studies.

Fig 2. Smoothed scatterplots of outcome domain use over time.

The frequency of reported outcomes over time are shown in these nonparametric regression locally weighted scatterplot smoothing method (LOWESS).