Adaptive optimal target controlled infusion algorithm to prevent hypotension associated with labor epidural: An adaptive dynamic programming approach. (May 2020)

• Record Type: Journal Article
• Authors: Davoud, Sherwin; Gao, Weinan; Riveros-Perez, Efrain
• Is Part Of: ISA transactions. Volume 100(2020)
• Page Start: 74
• Page End: 81
• Publication Date: 2020-05
• Subjects: Norepinephrine pharmacokinetics -- Obstetric anesthesia -- Adaptive optimal control -- Adaptive dynamic programming (ADP) -- Pregnancy -- Target controlled infusion; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2019.11.017
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Patients receiving labor epidurals commonly experience arterial hypotension as a complication of neuraxial block. The purpose of this study was to design an adaptive optimal controller for an infusion system to regulate mean arterial pressure. A state–space model relating mean arterial pressure to Norepinephrine (NE) infusion rate was derived for controller design. A data-driven adaptive optimal control algorithm was developed based on adaptive dynamic programming (ADP). The stability and disturbance rejection ability of the closed-loop system were tested via a simulation model calibrated using available clinical data. Simulation results indicated that the settling time was six minutes and the system showed effective disturbance rejection. The results also demonstrate that the adaptive optimal control algorithm would achieve individualized control of mean arterial pressure in pregnant patients with no prior knowledge of patient parameters. Highlights: Construct the pharmacologic model for Norepinephrine in pregnant females based on both pharmacokinetics and pharmacodynamics. Construct the pharmacologic model for Norepinephrine in pregnant females. Apply data-driven optimal control theory on target controlled infusion system design. The proposed controller design does not rely on the knowledge of patient parameters. Ensure safety and reliability of pregnant patients as receiving epidural anesthesia.