Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data.
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Saghafi Soheil, Li Qiao, Neylan Thomas C, T Tommy Thomas, Stevens Jennifer S, Jovanovic Tanja, Germine Laura T, Bucher Meredith A, Huibregtse Megan E, Linnstaedt Sarah D, An Xinming, Harnett Nathaniel G, Norrholm Seth D, Conti Alana C, Seligowski Antonia V, Dillon Daniel G, Vizer Lisa M, McKibben Lauren A, Albertorio-Saez Liz Marie, Beaudoin Francesca L, Matson Liana, Calhoun Vince D, Harte Steven E, Bruce Steven E, Haran John P, Storrow Alan B, Lewandowski Christopher, Musey Paul I, Hendry Phyllis L, Swor Robert A, Pearson Claire, Peak David A, O'Neil Brian J, Kessler Ronald C, Koenen Karestan C, McLean Samuel A, Clifford Gari D, Rad Ali Bahrami
Аннотация
METHODS: We evaluated daily longitudinal sleep/wake data from a prospective cohort of more than 2,000 emergency department patients with and without blood biomarker-documented TBI (Glial Fibrillary Acidic Protein - GFAP $ > 268 \frac{pg}{ml}$). We utilized a deep learning model to identify the impact of time from trauma and duration of data collection on the model's ability to distinguish between TBI-positive (TBI+) and TBI-negative (TBI-) cases.
RESULTS: Our analysis showed that sleep/wake data from the first 7 days after TBI most accurately identified TBI. Sleep-wake data from the first 7, 14, and 21 days after trauma achieved sensitivity/specificity of 81%/25%, 40%/66%, and 45%/58%, respectively. F1 scores of deep learning models developed from the first 7, 14, and 21 days were 22%, 21%, and 20%, respectively.
CONCLUSIONS: The results suggest that early sleep/wake data has promise for assisting with TBI identification.
SIGNIFICANCE: In the future, the incorporation of sleep/wake derived biomarkers into TBI identification tools could assist in the identification of individuals with potential TBI for further screening and intervention.