(595h) Vagal Nerve Stimulation for Gastric Function Via Model Based Closed Loop Control

Vagal nerve stimulation (VNS) is an emerging therapeutic approach for gastric disorders such as gastroparesis and functional dyspepsia, which involve impairments in gastric accommodation, antral motility, bile reflux, gastric emptying, and mixing efficiency. Although open-loop VNS has been shown to modulate gastric motility in animal models, optimizing stimulation parameters remains a significant challenge due to the complexity and dynamic nature of gastric function.

This study presents a model-based closed-loop control strategy to optimize VNS in real time, aiming to enhance its efficacy compared to traditional open-loop methods. A computationally efficient compartmental vago-vagal reflex model is developed, capturing key physiological mechanisms, including intramural and vagal reflex pathways. Model Predictive Control (MPC) is employed to adaptively tune VNS parameters based on real-time feedback of gastric motility and emptying.

The control framework integrates two compartmental models: a healthy gastric model, which provides reference trajectories for variables such as gastric emptying rate, intragastric pressure, mixing efficiency, and reverse transpyloric flow; and a disease-state model, representing pathological gastric function. A cost function minimizes deviations between the two models, allowing the MPC to iteratively adjust VNS inputs to steer gastric behavior toward physiological norms.

Simulation results show that MPC-driven VNS can effectively regulate gastric motility and enhance gastric emptying, underscoring its potential for personalized neuromodulation therapies. This framework lays the groundwork for experimental validation and clinical translation, offering a promising step toward adaptive VNS treatments for gastric disorders.