The silent development and progression of neurologic morbidity, or neuromorbidity, among hospitalized, critically ill patients represents a newly recognized and emerging epidemic. This includes patients admitted to intensive care units with primary neurologic diagnoses, those at increased risk based on their underlying disease (e.g. neurotropic viral infections including COVID19), and those where the development of neuromorbidity is occult and unexpected. Neuromorbidity associated with critical illness can be caused by physiologic instability, biochemical derangements, side effects of medications, invasive mechanical support, immobility, and/or other therapies used to prevent death. It spans the age spectrum from neonates to the elderly, occurs across gender and race, and is underrecognized in patients with systemic illnesses (e.g. sepsis, viral infections, and other inflammatory conditions) and critical organ failure (e.g. acute respiratory distress syndrome, cancer, hepatic and renal failure). In the U.S. the incidence of neuromorbidity ranges from 5-47% in critically ill children and adults, thus impacting hundreds of thousands of patients annually. Often neuromorbidity evolves undetected until after clinical manifestations emerge and is irreversible. Neuromorbidity can strike acutely, e.g. seizures, stroke, intracerebral hemorrhage, cerebral edema, and/or delirium, or in a more protracted fashion, e.g. neuromuscular weakness and/or cognitive decline, and is often permanent, endured throughout the remainder of a person’s lifetime. No standard clinical tool exists to identify patients at risk for neuromorbidity or for real-time neurologic monitoring, in stark contrast to the heart, kidney, liver, and many other organs. To fill this gap and transform the way clinicians detect and monitor for evolving brain injury, we developed a Bio-digital Rapid Alert to Identify Neuromorbidity (BRAIN) that continuously feeds electronic health record (EHR) variables in 9 clinical domains (A through I) into proprietary informatics and machine learning platforms. Prototype BRAIN A-I models are robust and predict clinician concern for neuromorbidity before clinical action is taken. To link biological and digital signatures, we have defined a panel of serum biomarkers that can identify time-documented neuromorbidity before clinical detection. Using a “Bayesian to Bedside” approach, we have created a live data pipeline bridging the EHR and a dedicated host server, establishing the infrastructure necessary to operationalize BRAIN A-I as an embedded predictive analytic and decision-driving support tool. In this proposal we will test the hypothesis that digital signatures in the EHR coupled with brain-specific biomarkers can rapidly detect neuromorbidity in critically ill children. Successful deployment of interoperable, 24/7 point-of-care neurologic monitoring for early detection of neuromorbidity would represent a breakthrough for the clinical management of critica...