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1.
IEEE/ACM Trans Comput Biol Bioinform ; 19(3): 1365-1378, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-34166200

RESUMO

Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed annually in the United States. Concussion is a heterogeneous disorder in causation, symptoms, and outcome making precision medicine approaches to this disorder important. Persistent disabling symptoms sometimes delay recovery in a difficult to predict subset of mTBI patients. Despite abundant data, clinicians need better tools to assess and predict recovery. Data-driven decision support holds promise for accurate clinical prediction tools for mTBI due to its ability to identify hidden correlations in complex datasets. We apply a Locality-Sensitive Hashing model enhanced by varied statistical methods to cluster blood biomarker level trajectories acquired over multiple time points. Additional features derived from demographics, injury context, neurocognitive assessment, and postural stability assessment are extracted using an autoencoder to augment the model. The data, obtained from FITBIR, consisted of 301 concussed subjects (athletes and cadets). Clustering identified 11 different biomarker trajectories. Two of the trajectories (rising GFAP and rising NF-L) were associated with a greater risk of loss of consciousness or post-traumatic amnesia at onset. The ability to cluster blood biomarker trajectories enhances the possibilities for precision medicine approaches to mTBI.


Assuntos
Concussão Encefálica , Aprendizado de Máquina não Supervisionado , Biomarcadores , Concussão Encefálica/diagnóstico , Humanos
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5514-5518, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019228

RESUMO

Clinicians need better tools to assess severity, prognosis, and recovery from mild Traumatic Brain Injury (mTBI), which can cause long term impairment. To enable better mTBI outcome prediction, an initial step is to analyze the trajectory of recovery metrics over time. This study provides an assessment of recovery trajectories of mTBI while incorporating heterogeneity of individual responses. We analyze the trajectories over multiple discrete time points from baseline to 6 months post injury using a combination of neurocognitive and postural stability assessments and serum biomarkers. The data, obtained from FITBIR, consists of concussed subjects and a matched control group, to allow for comparison in prognostic assessment. Outcomes derived from this exploratory analysis will aid future studies in developing a mTBI recovery timeline model.Clinical relevance- This study further informs clinicians as to the recovery trajectory of clinical measures and biomarkers after mTBI to support return to play decisions. GFAP biomarker and measures related to balance, memory, orientation, and concentration were significantly different than controls early after mTBI.


Assuntos
Concussão Encefálica , Biomarcadores , Concussão Encefálica/diagnóstico , Humanos , Prognóstico
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