Smart Cardiac ICU: Digital Integration, Predictive Analytics, and Perioperative Inflammation

Abstract

Contemporary intensive care is operating in an environment with high complexity cases, large volumes of information, and vast physiological, biological and therapeutical data, collected from laboratory results, investigations, therapies for organ support, collected from systems that operate in parallel. The lack of interoperability contributes to infor-mation overload, alarm fatigue, and delayed decision-making. The Smart ICU concept has emerged to address these limitations by integrating medical devices, information systems and artificial intelligence into a unified system that allows interoperable data integration and predictive analytics. Aim: The purpose of this article is to provide a narrative review of the Smart ICU concept, with a specific focus on the cardiac intensive care unit. It summarizes Smart ICU architec-ture, data integration, clinical support and applicability in monitoring perioperative in-flammation in cardiac surgery. We describe the Smart ICU architecture, from data acquisition to storage and analytics, highlighting the difference between Smart ICU, Artificial Intelligence and Tele-ICU, and we underlie the predictive analytics as a supportive tool, and its influence on clinical out-come. Cardiac ICU application: Cardiac ICUs offer a data-dense, temporally well-defined model following cardiac surgery with cardiopulmonary bypass, where data concerning patients hemodynamics, perfusion data, biological and inflammatory markers intertwine. Cardiac Smart ICU models could recognize early signs of hemodynamic compromise and low car-diac output states and identify early indicators of post-cardiac surgery complications. Neutrophil activation and complete blood count–derived indices may be used as dy-namic biological data for Smart Cardiac ICU models. Conclusion: The Smart Cardiac ICU may support earlier risk stratification, therefore earlier diagnostic and therapeutic interventions, but its clinical value requires prospective, mul-ticentre validation. Cardiopulmonary bypass–induced inflammation may offer an ideal setting to integrate physiological, procedural, and immunological data into bedside pre-dictive models.