Cluster analysis of cutting technique-a valuable approach for assessing anterior cruciate ligament injury risk?

Background: Despite extensive efforts to pinpoint singular biomechanical risk factors for anterior cruciate ligament (ACL) injuries, research findings are still inconclusive. By combining multiple biomechanical variables, cluster analyses could help us identify safe and risky cutting technique strategies. Purpose: To identify common movement strategies during cutting maneuvers and to assess their association with ACL injury risk. Methods: A total of 754 female elite handball and football players, including 59 with a history of ACL injury, performed a sport-specific cutting task while 3D biomechanics were recorded. Over an 8-year follow-up period, 43 of these players sustained a primary ACL injury and 13 players a secondary ACL injury. Cutting technique was described using 36 discrete kinematic variables. To identify different cutting techniques, we employed a K-means clustering algorithm on data subsets involving different numbers of kinematic variables (36, 13 and 5 variables) and different sports (handball, football, and both combined). To assess the impact of the identified cutting technique clusters on ACL injury risk, we compared the proportion of injured players between these clusters using the Fisher-Freeman-Halton Exact test and adjusted rand indices (ARI). Results: We identified two distinguishable cutting technique clusters in the subset involving both sports and five kinematics variables (average silhouette score, ASS = 0.35). However, these clusters were formed based on sport- or task-related differences (Fisher's Conclusion: No safe or risky cutting technique strategies could be discerned among female elite handball and football players. Cluster analysis of cutting technique, using a K-means algorithm, did not prove to be a valuable approach for assessing ACL injury risk in this dataset.