Heterogeneity in Blood Biomarker Trajectories After Mild TBI Revealed by Unsupervised Learning

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 d...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2022-05, Vol.19 (3), p.1365-1378
Main Authors: Bui, Lien A., Yeboah, Dacosta, Steinmeister, Louis, Azizi, Sima, Hier, Daniel B., Wunsch, Donald C., Olbricht, Gayla R., Obafemi-Ajayi, Tayo
Format: Article
Language:English
Subjects:
Amnesia
Athletes
Biological system modeling
Biomarkers
Blood
Brain Concussion - diagnosis
Clustering
Cognition
Concussion
concussions
Data recovery
GFAP
Glial fibrillary acidic protein
Head injuries
Heterogeneity
Humans
Injuries
Mathematical models
Medicine
NF-L
Precision medicine
predictive modeling
Proteins
Sports
Stability analysis
Stability augmentation
statistical analysis
Statistical methods
tau
Trajectory
Traumatic brain injury
UCH-L1
Unsupervised learning
Unsupervised Machine Learning
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Summary: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.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2021.3091972