Abstract
The purpose of this project was to develop a computer model of cerebrovascular hemodynamics interacting with a pharmacokinetic drug model to examine the effects of various stimuli on cerebral blood flow and intracranial pressure during anesthesia.
The mathematical model of intracranial hemodynamics is a seven-compartment, constant-volume system. A series of resistance relate blood and cerebrospinal fluid fluxes to pressure gradients between compartments. Arterial, venous, and tissue compliance are also included. Autoregulation is modeled by transmural pressure-dependent, arterial-arteriolar resistance. The effect of a drug (thiopental) on cerebrovascular circulation was simulated by a variable arteriolar-capillary resistance. Thiopental concentration was predicted by a three-compartment, pharmacokinetic model. The effect site compartment was included to account for a disequilibrium between drug plasma and biophase concentrations. The model was validated by comparing simulation results with available experimental observations. The simulation program is written in VisSim dynamic simulation language for an IBM-compatible PC.
The model developed was used to calculate the cerebral blood flow and intracranial pressure changes that occur during the induction phase of general anesthesia. Responses to laryngoscopy and intubation were predicted for simulated patients with elevated intracranial pressure and non-autoregulated cerebral circulation. Simulation shows that the induction dose of thiopental reduces intracranial pressure up to 15%. The duration of this effect is limited to less than 3 minutes by rapid redistribution of thiopental and cerebral autoregulation. Subsequent laryngoscopy causes acute intracranial hypertension, exceeding the initial intracranial pressure. Further simulation predicts that this untoward effect can be minimized by an additional dose of thiopental administered immediately prior to intubation.
The presented simulation allows comparison of various drug administration schedules to control intracranial pressure and preserve cerebral blood flow during induction of anesthesia. The model developed can be extended to analyze more complex intraoperative events by adding new submodels.