Comprehensive Summary
Eshraghi et al. present a comprehensive review of multimodal neuromonitoring approaches in both neurological and medical intensive care unit (ICU) patients. The authors highlight that neurological complications ranging from non-convulsive status epilepticus to neuromuscular weakness commonly occur not only in patients with primary neurological injury but also among those admitted for other medical concerns. The review synthesizes evidence on commonly utilized tools for gathering neurological data in these settings, including automated pupillometry, continuous electroencephalography (cEEG), somatosensory evoked potentials, and cerebral perfusion and oxygenation monitoring. The article also examines the growing role of artificial intelligence in synthesizing these multimodal signals into useful considerations for real-time clinical decisions in critical care units. By addressing the inconsistencies and subjective evidence of traditional neurological examinations on sedated and intubated patients, the authors emphasize how advanced monitoring can improve early detection and management of secondary brain injury and altered consciousness in ICU patients with primary conditions or secondary effects of treatment which could otherwise mask these findings.
Outcomes and Implications
The review underscores the clinical significance of continuous neuromonitoring as a means of reducing morbidity and mortality in critically ill patients. Integrating real-time physiological, electrical, and metabolic data allows clinicians to detect neurologic deterioration before irreversible damage occurs, especially in patients unable to participate in standard neurological exams. The paper advocates for the broader adoption of technologies such as near-infrared spectroscopy, intracranial pressure monitoring, and jugular venous oxygen saturation measurement in ICU protocols. Furthermore, the authors suggest that AI-driven multimodal systems could soon enable automated recognition of patterns like non-convulsive seizures or cerebral hypoperfusion, streamlining interventions and treatment adjustments. Such integration of advanced monitoring and AI analytics holds promise to transform neurocritical care into a more proactive, precision-based practice model.