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Article Abstract

Background: The treatment of cardiac arrest (CA) patients is often complicated by post-cardiac arrest syndrome (PCAS), which involves a systemic inflammatory response. Increased levels of certain inflammatory markers (e.g., interleukin-6 and procalcitonin) are associated with higher mortality and worse neurological outcomes. Previous studies have focused on the prognostic value of individual markers, whereas the interplay between inflammatory mediators in the setting of CA remains largely unclear. In this study, we aim to examine the patterns of inflammatory markers and their association with the severity of organ failure and 6-month neurological outcomes in adult CA patients.

Methods: This is a prospective observational single-center study. The study cohort consists of 40 adult CA patients admitted to the ICU after successful resuscitation, and a control group of 40 patients undergoing elective coronary artery bypass graft (CABG) surgery. Patient enrollment started in January 2022 and was completed in April 2025. The last follow-up samples and interviews are due in October 2025. We collect blood samples upon ICU admission, at 4, 8, 12, and 24 h after return of spontaneous circulation (ROSC) and on the mornings of treatment Days 1-4. We combine clinical data with proteomic, transcriptomic, and flow cytometric analyses of blood samples to generate a comprehensive systems immunology landscape of the PCAS. The control group of CABG patients is used to assess inflammatory changes related to surgical trauma and ICU treatment in general, with the goal of distinguishing inflammatory disturbances specific to CA. This allows the identification of complex inflammatory pathways relevant to the pathogenesis of PCAS. The gathered information can then be used to plan future anti-inflammatory interventional trials. The primary outcomes of the study are the severity of organ failure during the first 96 h after ROSC and neurological outcome at 6 months. We will assess these using a modified SOFA score and the Modified Rankin Scale, respectively. We will use principal component analysis, latent profile analysis, and hierarchical clustering to identify patient subgroups with similar immunological response profiles. We will assess the correlation of different immunological response profiles with primary outcomes using linear mixed-effects models.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12236270PMC
http://dx.doi.org/10.1111/aas.70087DOI Listing

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