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

摘要

Dynamic modeling of arterial blood pressure change subject to vasoactive drug infusion can be a valuable tool for computerized decision support of drug administration as well as 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 due to continuous injection of the vasorestrictive drug phenylephrine (PHP). The patient-to-patient as well as intrapatient variability of the dynamic response is taken into account by online identification of the varying model parameters. A multiple-model extended Kalman filter (MMEKF) structure is developed for the real-time estimation of mean arterial pressure and the dynamic blood pressure response model to PHP infusion to assist treatment. Convergence analysis is carried out, along with comparison with offline identification methods. Static drug-response curves for dosage recommendation are obtained from the estimation of the model sensitivity parameter. Finally, a detection algorithm is proposed to identify abrupt model variations caused by sudden physiological changes, such as hemorrhage. The proposed MMEKF parameter estimation method and the hemorrhage detection algorithm are tested and validated using data from animal experiments on anesthetized healthy and hemorrhagic swine that are subject to PHP infusion.