New Tools and Predictive Markers in the Prognosis of Hypoxic-ischemic Encephalopathy Following Cardiac Arrest

Cardiac arrest (CA) remains a major cause of mortality and long-term neurological disability worldwide. Despite advances in resuscitation, many survivors suffer from post-cardiac syndrome encompassing PCAS brain injury (PCABI), due to primary (ischemic) and secondary (reperfusion) injury, myocardial dysfunction, systemic response, and persistent underlying causes. PCABI plays a critical role in this complex condition, which is characterized by ischemia, inflammation, and microvascular dysfunction.Current methods to predict neurological outcomes are limited, leading to challenges in clinical decision-making and the risk of premature withdrawal of life-sustaining therapies. This study aims to improve prognostication in CA patients by integrating advanced serum biomarker profiling with cerebral ultrasound (CU) techniques. The investigators hypothesize that combining these tools with clinical data will enhance the accuracy of neurological outcome predictions and deepen understanding of PCABI pathophysiology. The investigators will prospectively enroll 50 CA patients admitted to the intensive care unit at Cliniques Universitaires Saint-Luc. Serum proteomics will be performed using the Reveal panel from Olink®, which analyzes over 1,000 proteins involved in inflammation and thrombosis implicated in PCABI. Concurrently, cerebral ultrasound will assess optic nerve sheath diameter (ONSD) and cerebral blood flow velocities (CBFV) at multiple time points post-resuscitation (at admission, on day 1-2 and on day 3-5). These non-invasive bedside measurements may serve as early indicators of elevated intracranial pressure and cerebral hemodynamic abnormalities.The primary endpoint is poor neurological outcome at one month, defined by the Cerebral Performance Category (CPC) scale. Statistical analyses will evaluate the prognostic value of biomarkers and ultrasound parameters individually and in combination with established clinical predictors.This multimodal approach promises to refine prognostic accuracy, improve clinical decision-making, and identify novel therapeutic targets. Ultimately, our findings may lead to improved patient outcomes and guide future multicenter studies for validation and clinical implementation.