A Dynamic Early Warning System for Postoperative Delirium After Cardiac Surgery Integrating EEG, Cerebral Oxygenation, Hemodynamic Physiology, and Clinical Risk Factors

Postoperative delirium (POD) following cardiac surgery has an incidence of 20-50% and is associated with prolonged mechanical ventilation, extended ICU and hospital stay, and increased mortality. Its pathophysiology involves a complex interplay of cerebral hypoperfusion, neuroinflammation, blood-brain barrier disruption, and neurotransmitter imbalances-a "multiple-hit" model. Current clinical assessment relies on static risk stratification or post-hoc diagnostic tools , which lack the dynamic, pre-symptomatic warning capability needed for timely intervention. The increasing availability of multimodal ICU monitoring (EEG, near-infrared spectroscopy, invasive hemodynamics) and advanced time-series deep learning provides an unprecedented opportunity to capture the evolution of physiological uncoupling before the clinical manifestation of delirium.

Objective: This study aims to develop and validate a multimodal deep learning model that fuses continuous central electrophysiology (frontal EEG), regional cerebral oxygen saturation (rScO₂), macro-hemodynamic parameters, and clinical static/dynamic risk factors to provide a dynamic early warning of POD 1-6 hours in advance.

Study Design: This is a single-center, prospective, observational cohort study. Setting and Population: The study will enroll consecutive adult patients (18-80 years) admitted to the Department of Critical Care Medicine at Zhongda Hospital, Southeast University, after cardiac surgery (CABG, valve repair/replacement, major aortic surgery, or combined procedures) between May 1, 2026 and December 30, 2027. All eligible patients must have multimodal monitoring including continuous EEG, bilateral frontal rScO₂, and invasive arterial blood pressure, and must provide informed consent.

Key Exclusion Criteria: Pre-existing dementia, psychiatric illness or long-term antipsychotic use precluding accurate delirium assessment; severe hepatic (Child-Pugh C) or renal insufficiency (eGFR <30 mL/min/1.73 m²); significant brain injury or seizure history; inability to obtain adequate signal quality; expected death within 24 hours.

Data Collection and Monitoring: Data will be captured at multiple time windows: preoperative baseline, intraoperative period, and postoperative time points (immediately upon ICU arrival, 6h, 24h, 48h, and at the moment delirium is first detected). Preoperative phenotyping includes demographics, MoCA, Clinical Frailty Scale, and EuroSCORE II. Continuous EEG features (power spectral density, burst suppression ratio, complexity indices) and rScO₂ (baseline, desaturation events >20%, autoregulation index COx) will be recorded. Hemodynamic variables include heart rate, beat-to-beat blood pressure variability, and, where available, derived cardiac output metrics. Comprehensive clinical data (laboratory values, sedation/analgesic dosing, vasoactive-inotropic score, mechanical ventilation parameters, and SOFA/APACHE II scores) will be collected concurrently.