B - 57 Examining Symptom Patterns in Anomalous Health Incidents: a Spectral Co-Clustering Analysis Using the Post-Concussion Symptom Scale

Abstract

Abstract Objective The ImPACT Post-Concussion Symptom Scale (PCSS) is widely used to document post-concussion symptoms. This study investigated the structural patterns of PCSS using spectral co-clustering on data from U.S. diplomats and their families who served in Cuba and China, who reported experiencing Anomalous Health Incidents (AHIs). Method Clinicians at the University of Pennsylvania evaluated 65 individuals with potential AHI exposure (Median age-group = 35–45 years; 73.84% in Cuba; 49.23% females). PCSS feature scores were discretized into 4-level symptom severity expressions: none, mild, moderate, and severe. The preprocessed data were fit with spectral co-clustering model to extract optimal number of biclusters, by measuring scree plot with principal component analysis and Calinski-Harabasz (CH) score. Final model was also evaluated by CH score and a random label t-test to compare the significance of the clustering results. Results Overall, spectral co-clustering yielded two biclusters with CH score of 20.29 and random label t-test of p < 0.001, suggesting reasonable clustering quality. Biclustering identified two sets of interpretable PCSS symptom characteristics. Cluster centroids X and Y (IDs retracted) were estimated using Manhattan distance, summarizing symptom characteristics of each cluster. More precisely, centroid X represented mild to moderate symptom profiles (e.g., headache, dizziness, drowsiness, and nervousness), while centroidY represented a relative absence of symptoms, except for mild nervousness and emotionality. Conclusion The spectral co-clustering of PCSS reveals patterns of elevated symptoms in one cluster and minimal symptom burden in the other, similar to outcomes observed after mild TBI. These patterns can guide future research directions and ultimately improve patient outcomes in various medical contexts.