Personalized and real time hemodynamic management in critical care using Dynamic Cohort Ensemble Learning (DynaCEL)

Abstract

Effective hemodynamic management in the intensive care unit requires individualized targets that adapt to dynamic clinical conditions. We developed Dynamic Cohort Ensemble Learning (DynaCEL), a real-time framework that recommends personalized heart rate and systolic blood pressure targets by modeling each time point post-intensive care unit admission as a distinct temporal cohort. Trained on eICU data and validated on MIMIC-IV and Indiana University Health datasets, DynaCEL demonstrated robust predictive performance (AUCs 0.83–0.91). In the MIMIC-IV cohort, proximity to DynaCEL-predicted targets was associated with lower 24-hour mortality compared to fixed targets, after adjustment using propensity score matching. Dose-response and comparative analyses revealed that greater deviations from personalized targets were associated with higher mortality. Case studies illustrated temporal and inter-individual variation in optimal targets. DynaCEL offers interpretable and scalable support for exploring precision hemodynamic management, although its clinical utility remains to be established in prospective trials.