Dynamic biomarker trajectories in the first 72 h after infarct-related cardiac arrest: a novel approach to early risk stratification.

Background: Cardiac arrest caused by acute myocardial infarction (AMI) is associated with high mortality. Although risk stratification scores exist, they rely primarily on static variables obtained at admission, which do not capture the dynamic pathophysiology of the post-resuscitation phase. This study aimed to evaluate the prognostic value of serial biomarker trajectories during the first 72 h after AMI-induced cardiac arrest. Methods: In this single-center cohort study, 181 patients with AMI-induced cardiac arrest between 2018 and 2024 were analyzed. Routinely measured laboratory biomarkers were assessed over the first three days in the intensive care unit (ICU). Multivariable logistic regression models adjusted for key clinical covariates were used to evaluate associations between biomarker trajectories and in-hospital mortality. Secondary analyses included t-distributed stochastic neighbor embedding cluster (machine learning), radar, Sankey and trend plots to visualize biomarker patterns in survivors and non-survivors. Results: Of the 181 patients, 65.2% survived to hospital discharge. Survivors and non-survivors showed overlapping biomarker profiles on day one, with clearer separation emerging by day three. Non-survivors demonstrated progressive multi-organ dysfunction, including elevated levels of creatinine, potassium, creatine kinase, lactate, neuron-specific enolase, leukocytes and persistent coagulopathy, while survivors showed restoration of physiological homeostasis. Several biomarkers and their dynamic changes over 72 h independently predicted mortality. Cluster, radar, Sankey and trend plot analyses supported the concept of diverging physiological trajectories between survivors and non-survivors over time. Conclusions: In patients who survive the initial critical phase after cardiac arrest, early prognostication remains limited due to evolving clinical trajectories. Admission biomarkers alone are insufficient for making definitive decisions. The post-resuscitation period represents a critical "second hit" characterized by systemic inflammation and organ dysfunction. Integrating serial biomarker trends into dynamic risk models, such as with machine learning, offers a more individualized and accurate approach to post-cardiac arrest prognostication and care.