Cluster analysis using physical performance and self-report measures to identify shoulder injury in overhead female athletes

Objectives: To evaluate the diagnostic validity of the Kerlan-Jobe orthopedic clinic shoulder and elbow score (KJOC) and the Closed kinetic upper extremity stability test (CKCUEST) to assess functional impairments associated with shoulder injury in overhead female athletic populations.

Design: Cross-sectional design.

Methods: Thirty-four synchronized swimming and team handball female athletes completed the KJOC and the CKCUEST during their respective team selection trials. Unsupervised learning using k-means algorithm was used on collected data to perform group clustering and classify athletes as Injured or Not Injured. Odds ratios, likelihood ratios, sensitivity and specificity were computed based on the self-reported presence of shoulder injury at the time of testing or during the previous year.

Results: Seven of the 34 athletes were injured or had suffered a time-loss injury in the previous year, representing a 20.5% prevalence rate. Clustering method using KJOC data resulted in a sensitivity of 86%, a specificity of 100% and a 229.67 diagnostic odds ratio. Clustering method using CKCUEST data resulted in a sensitivity of 86%, a specificity of 37% and a 3.53 diagnostic odds ratio.

Conclusions: KJOC had good diagnostic validity to assess shoulder function and differentiate between injured and non-injured elite synchronized swimming and team handball female athletes. The CKCUEST seemed to be a poor screening test but may be an interesting test to evaluate functional upper extremity strength and plyometric capacity. Unsupervised learning methods allow to make decisions based on numerous variables which is an advantage when considering the usually substantial overlap in screening test scores between high- and low-risk athletes.