Developing Insights for Possible and Probable Acute Concussions Using Cluster Analysis.

Abstract

Few studies have analyzed the Sport Concussion Assessment Tool's (SCAT) utility among athletes whose concussion assessment is challenging. Using a previously published algorithm, we identified possible and probable concussions at <6 h (n = 393 males, n = 265 females) and 24-48 h (n = 323 males, n = 236 females) post-injury within collegiate student-athletes and cadets from the Concussion Assessment, Research, and Education (CARE) Consortium. We applied cluster analysis to characterize performance on the Standard Assessment of Concussion (SAC), Balance Error Scoring System (BESS), and the SCAT symptom checklist for these athletes. Among the cluster sets that best separated acute concussions and normal performances, total symptom number raw score and change and post-traumatic migraine raw score and change score were the most frequent clustering variables across males and females at <6 h and 24-48 h. Similarly, total symptom number raw score and change score and post-traumatic migraine raw score and change score were most significantly different between clusters for males and females at <6 h and 24-48 h. Our results suggest that clinicians should focus on total symptom number, post-traumatic migraine symptoms, and cognitive-fatigue symptoms when assessing possible and probable concussions, followed by the SAC and BESS scores.