A tissue framework for simulating the effects of gastric electrical stimulation and in vivo validation.

  • Du P
  • O'Grady G
  • Windsor J
 et al. 
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Abstract

Gastric pacing is used to modulate normal or abnormal gastric slow-wave activity for therapeutic purposes. New protocols are required that are optimized for motility outcomes and energy efficiency. A computational tissue model was developed, incorporating smooth muscle and interstitial cell of Cajal layers, to enable predictive simulations of slow-wave entrainment efficacy under different pacing frequencies. Concurrent experimental validation was performed via high-resolution entrainment mapping in a porcine model (bipolar pacing protocol: 2 mA amplitude; 400 ms pulsewidth; 17-s period; midcorpus). Entrained gastric slow-wave activity was found to be anisotropic (circular direction: 8.51 mm x s(-1); longitudinal: 4.58 mm x s(-1)), and the simulation velocities were specified accordingly. Simulated and experimental slow-wave activities demonstrated satisfactory agreement, showing similar propagation patterns and frequencies (3.5-3.6 cycles per minute), and comparable zones of entrainment (ZOEs; 64 cm(2)). The area of ZOE achieved was found to depend on the phase interactions between the native and entrained activities. This model allows the predictions of phase interactions between native and entrained activities, and will be useful for determining optimal frequencies for gastric pacing, including multichannel pacing studies. The model provides a framework for the development of more sophisticated predictive gastric pacing simulations in future.

Author-supplied keywords

  • Animals
  • Biological
  • Computer Simulation
  • Electric Stimulation
  • Electric Stimulation: methods
  • Gastrointestinal Motility
  • Gastrointestinal Motility: physiology
  • Models
  • Muscle
  • Muscle Contraction
  • Muscle Contraction: physiology
  • Smooth
  • Smooth: physiology
  • Stomach
  • Stomach: physiology
  • Swine

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Authors

  • Peng Du

  • Gregory O'Grady

  • John A Windsor

  • Leo K Cheng

  • Andrew J Pullan

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