Personalized and real time hemodynamic management in critical care using Dynamic Cohort Ensemble Learning (DynaCEL) - Lingzhong Meng; Jiangqiong Li; Xiang Liu; Yanhua Sun; Zuotian Li; Jinjin Cai; Ameya D. Parab; George Lu; Aishwarya Budhkar; Saravanan Kanakasabai; David C. Adams; Ziyue Liu; et al. (npj Digital Medicine)
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.