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Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications

  • Big Data in Neurocritical Care
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Abstract

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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Funding

NB, PH, and SY received funding support from United States Air Force (USAF) (FA8650-18-2-6H18). NB received funding from National Institute of Neurological Disorders and Stroke/National Institutes of Health (NINDS/NIH) (NS10505503).

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Authors and Affiliations

  1. Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, USA

    Jamie Podell, Melissa Pergakis, Ryan Felix, Gunjan Parikh, Peter Hu & Neeraj Badjatia

  2. Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA

    Jamie Podell, Melissa Pergakis, Gunjan Parikh & Neeraj Badjatia

  3. Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, USA

    Shiming Yang & Peter Hu

  4. Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA

    Hegang Chen

  5. Department of Information Systems, College of Engineering and Information Technology, University of Maryland Baltimore County, Baltimore, MD, USA

    Lujie Chen

  6. Enroute Care Division, Department of Aeromedical Research, United States Air Force School of Aerospace Medicine, Wright-Patterson Air Force Base, Dayton, OH, USA

    Catriona Miller

  7. Shock Trauma Neurocritical Care, University of Maryland Medical Center, 22 S. Greene Street, G7k19, Baltimore, MD, 21218, USA

    Neeraj Badjatia

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  1. Jamie Podell
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  9. Peter Hu
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Contributions

NB, JP, MP, PH, and SY: concept and design of the study, analysis of data, writing of the article. HC and LC: analysis of data and critical revision. RF: data collection, administrative support, and critical revision. CM and GP: analysis of data, important intellectual content, and critical revision. The final manuscript was approved by all authors.

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Correspondence to Neeraj Badjatia.

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Podell, J., Pergakis, M., Yang, S. et al. Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications. Neurocrit Care 37 (Suppl 2), 206–219 (2022). https://doi.org/10.1007/s12028-022-01491-6

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  • DOI: https://doi.org/10.1007/s12028-022-01491-6

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