PROJECT TITLE :

Adaptive Parameter Estimation of Blood Pressure Dynamics Subject to Vasoactive Drug Infusion

ABSTRACT:

Dynamic modeling of arterial blood pressure modification subject to vasoactive drug infusion will be a valuable tool for computerized decision support of drug administration also for automated closed-loop drug delivery to treat hypotension in emergency trauma care. A time-varying time-delayed first-order dynamic model is considered to describe blood pressure response thanks to continuous injection of the vasorestrictive drug phenylephrine (PHP). The patient-to-patient plus intrapatient variability of the dynamic response is taken under consideration by on-line identification of the varying model parameters. A multiple-model extended Kalman filter (MMEKF) structure is developed for the important-time estimation of mean arterial pressure and the dynamic blood pressure response model to PHP infusion to help treatment. Convergence analysis is distributed, together with comparison with offline identification ways. Static drug-response curves for dosage recommendation are obtained from the estimation of the model sensitivity parameter. Finally, a detection algorithm is proposed to spot abrupt model variations caused by sudden physiological changes, like hemorrhage. The proposed MMEKF parameter estimation method and the hemorrhage detection algorithm are tested and validated using knowledge from animal experiments on anesthetized healthy and hemorrhagic swine that are subject to PHP infusion.