Abstract
Haemodynamic management in critical care typically relies on static, population-based targets that overlook patient-specific physiology and the evolving nature of illness. We develop and validate a framework for real-time, personalised haemodynamic management using a time-dependent Cox model that integrates static and dynamic clinical data to predict survival probabilities and derive optimal heart rate and systolic blood pressure targets over time. Trained on the electronic Intensive Care Unit dataset and externally validated with Medical Information Mart for Intensive Care IV and Indiana University Health cohorts, the model demonstrates high predictive accuracy (c-index up to 0.931) and generalisability across diverse populations. Patients with heart rate and systolic blood pressure values closer to model-predicted targets exhibit significantly lower intensive care unit mortality than those aligned with fixed, population-based thresholds. Exploratory dose-response and propensity score-matched analyses confirm outcome relevance, while case studies illustrate feasibility in critical care settings. This personalised, dynamic approach-termed Haemodynamic Management by Time-Adaptive, Risk-Guided Estimation of Targets (HM-TARGET)-offers a scalable framework for precision haemodynamic management in critically ill patients. Prospective trials are warranted to evaluate clinical impact.