Hemodynamic Monitoring via Model-Based Extended Kalman Filtering: Hemorrhage Resuscitation and Sedation Case Study

Abstract

This letter investigates the potential of model-based extended Kalman filtering (EKF) for hemodynamic monitoring in a hemorrhage resuscitation-sedation case study. To the best of our knowledge, it may be the first model-based state estimation study conducted in the context of hemodynamic monitoring. Built upon a grey-box mathematical model with parametric uncertainty as process noise, the EKF can estimate cardiac output (CO) and total peripheral resistance (TPR) continuously from mean arterial pressure (AP) measurements against inter-individual physiological and pharmacological variability. Its unique practical strengths include: it does not require AP waveform as in existing AP-based pulse-contour CO (PCCO) monitors; and it can estimate CO and TPR with explicit account for the effect of sedative drugs. The efficacy of the EKF-based hemodynamic monitoring was evaluated based on a large number of plausible virtual patients generated using a collective inference algorithm, which demonstrated that it has significant advantage over open-loop pure prediction, and that its accuracy is comparable to PCCO.