Current risk stratification for Post-cardiac Arrest Syndrome (PCAS) relies mainly on static admission variables and may fail to capture the dynamic systemic evolution. This study aimed to identify trajectory-defined thrombo-inflammatory phenotypes in PCAS using longitudinal trajectories of platelets, white blood cells (WBC), hemoglobin, and body temperature, and to evaluate their association with 30-day ICU mortality. We conducted a multicenter retrospective cohort study using the MIMIC-IV, MIMIC-III, and eICU-CRD databases, including adult patients with ICU stays of 2-90 days after cardiac arrest. A Multivariate Process Joint Latent Class Mixed Model (mJLCMM) identified latent classes from 30-day biomarker trajectories. The primary outcome was 30-day ICU mortality. Associations were evaluated using Inverse Probability Weighting (IPW) and Doubly Robust Estimation (DRE). Prognostic accuracy was compared against SOFA and OASIS scores using time-dependent Receiver Operating Characteristic (ROC) analysis. A total of 5,099 patients were included. Two phenotypes were identified: Class 1 ("Rapid Decline and Recovery") and Class 2 ("Mild Decline and Recovery"). Class 1 was associated with higher 30-day ICU mortality (eICU: 46.7%; MIMIC: 24.6%). In doubly robust analyses, the class 2 remained associated with lower ICU mortality in both cohorts, with odds ratios (ORs) of 0.82 (95% CI, 0.72-0.96) in eICU and 0.74 (95% CI, 0.55-0.95) in MIMIC. By Day 30, the trajectory model outperformed SOFA and OASIS, with an AUC of 0.74 versus 0.54 and 0.59, respectively. This trajectory-based classification showed superior prognostic performance for 30-day ICU mortality and highlights the potential value of dynamic monitoring in post-cardiac arrest management.
Zhang et al. (Thu,) studied this question.
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