Journal
NEUROCRITICAL CARE
Volume 37, Issue SUPPL 2, Pages 206-219Publisher
HUMANA PRESS INC
DOI: 10.1007/s12028-022-01491-6
Keywords
Autonomic nervous system; Machine learning; Traumatic brain injury; Ischemic stroke; Neurological decline
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Funding
- United States Air Force (USAF) [FA8650-18-2-6H18]
- National Institute of Neurological Disorders and Stroke/National Institutes of Health (NINDS/NIH) [NS10505503]
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Subtle and profound changes in autonomic nervous system function occur in critical illness, particularly in neurocritical illness, and can be measured quantitatively at the bedside. Data analytics can identify physiological changes that precede clinical detection of meaningful events, providing a window for time-sensitive therapies. This review discusses data-analytic approaches to measuring autonomic nervous system dysfunction and their potential for early detection and monitoring.
Subtle and profound changes in autonomic nervous system (ANS) function affecting sympathetic and parasympathetic homeostasis occur as a result of critical illness. Changes in ANS function are particularly salient in neurocritical illness, when direct structural and functional perturbations to autonomic network pathways occur and may herald impending clinical deterioration or intervenable evolving mechanisms of secondary injury. Sympathetic and parasympathetic balance can be measured quantitatively at the bedside using multiple methods, most readily by extracting data from electrocardiographic or photoplethysmography waveforms. Work from our group and others has demonstrated that data-analytic techniques can identify quantitative physiologic changes that precede clinical detection of meaningful events, and therefore may provide an important window for time-sensitive therapies. Here, we review data-analytic approaches to measuring ANS dysfunction from routine bedside physiologic data streams and integrating this data into multimodal machine learning-based model development to better understand phenotypical expression of pathophysiologic mechanisms and perhaps even serve as early detection signals. Attention will be given to examples from our work in acute traumatic brain injury on detection and monitoring of paroxysmal sympathetic hyperactivity and prediction of neurologic deterioration, and in large hemispheric infarction on prediction of malignant cerebral edema. We also discuss future clinical applications and data-analytic challenges and future directions.
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