Risk scoring systems for early prediction of short-term mortality in resuscitated out-of-hospital cardiac arrest patients

Introduction: Out-of-hospital cardiac arrest (OHCA) is a critical condition associated with high mortality rates and neurological impairment among survivors. In comatose OHCA patients who achieve return of spontaneous circulation, early risk stratification is important to inform treatment pathways and potentially improve outcomes. A range of prognostic tools have been developed to predict survival and neurological recovery. Each tool incorporates a unique combination of clinical, biochemical and physiological markers. Areas covered: This review article evaluates the required clinical data, predictive performances and practical applicability of major risk scores. A literature review was conducted in PubMed and Embase for studies published between January 2000 and October 2024. The review emphasizes the variability in discriminative power among the selected scores, with some models offering high sensitivity and specificity in outcome prediction, while others prioritize simplicity and accessibility. Expert opinion: Despite the advancements of these tools, limitations persist in data dependency and the clinical adaptability, highlighting areas for future improvement. Integrating artificial intelligence and real-time analytics could enhance predictive accuracy, offering dynamic prognostic capabilities that adapt to individual patient trajectories. This evolution must be grounded in ethical considerations to ensure predictive technologies complement rather than replace clinical judgment, balancing technology's potential with the complexities of individualized patient care.