Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning

Abstract

Multimodal monitoring (MMM) in the intensive care unit (ICU) has become increasingly sophisticated with the integration of neurophysical principles. However, the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient outcomes. This manuscript reviewed current neuromonitoring tools, focusing on intracranial pressure, cerebral electrical activity, metabolism, and invasive and noninvasive autoregulation monitoring. In addition, the integration of advanced machine learning and data science tools within the ICU were discussed. Invasive monitoring includes analysis of intracranial pressure waveforms, jugular venous oximetry, monitoring of brain tissue oxygenation, thermal diffusion flowmetry, electrocorticography, depth electroencephalography, and cerebral microdialysis. Noninvasive measures include transcranial Doppler, tympanic membrane displacement, near-infrared spectroscopy, optic nerve sheath diameter, positron emission tomography, and systemic hemodynamic monitoring including heart rate variability analysis. The neurophysical basis and clinical relevance of each method within the ICU setting were examined. Machine learning algorithms have shown promise by helping to analyze and interpret data in real time from continuous MMM tools, helping clinicians make more accurate and timely decisions. These algorithms can integrate diverse data streams to generate predictive models for patient outcomes and optimize treatment strategies. MMM, grounded in neurophysics, offers a more nuanced understanding of cerebral physiology and disease in the ICU. Although each modality has its strengths and limitations, its integrated use, especially in combination with machine learning algorithms, can offer invaluable information for individualized patient care.

Keywords: Neurocritical care, Critical care, Multimodal monitoring, Machine learning, Neurophysics, Cerebral hemodynamics, Cerebral energetics, Transcranial Doppler, Cerebral microdialysis, Near-infrared spectroscopy

Core Tip: This manuscript provided a comprehensive review of multimodal monitoring (MMM) in the intensive care unit, emphasizing the integration of neurophysics to optimize patient outcomes. It covered invasive and noninvasive neuromonitoring tools and highlighted the role of machine learning in real-time data analysis and interpretation from MMM tools, aiding in precise clinical decision-making. By integrating diverse data streams through MMM, machine learning algorithms enhance the understanding of cerebral physiology and disease, offering invaluable insights for personalized patient care in the intensive care unit. This integration aids the neurointensivist in more accurate neuroprognostication and in future avenues for targeted therapeutic interventions.