A comparison of overall mathematical models of the cardiovascular system for simulating response to orthostatic

E H Karam1, R S Srinivasan, J B Charles

  • 1Space Biomedical Research Institute, NASA Johnson Space Center, Houston, TX 77058.

The Physiologist
|January 1, 1993
PubMed

Insights

This study evaluates three mathematical models of the cardiovascular system (CVS) for their accuracy in simulating responses to various orthostatic stresses like lower body negative pressure (LBNP) and blood loss.

Area of Science:

  • Physiology
  • Biomedical Engineering
  • Mathematical Modeling

Background:

  • Numerous cardiovascular system (CVS) models exist, but few comprehensively model the heart, vasculature, and control elements.
  • Existing models vary in complexity and are often stimulus-specific, with limited reported accuracy across diverse stimuli.
  • The ability of mathematical models to accurately simulate responses to a wide range of orthostatic stresses has not been well-established.

Purpose of the Study:

  • To examine and compare the performance of three distinct mathematical models of the CVS.
  • To assess the models' accuracy in simulating short-term orthostatic responses to lower body negative pressure (LBNP), head-up tilt, and blood loss.
  • To validate model responses against available experimental data for orthostatic stress simulations.

Main Methods:

  • Evaluation of three mathematical models: Croston and Fitzjerrell's (C), Jaron et al.'s (J), and Pullen's (P).
  • Simulation of cardiovascular responses to orthostatic stresses including lower body negative pressure (LBNP), head-up tilt, and simulated blood loss.
  • Comparison of simulated short-term orthostatic responses with existing experimental data.

Main Results:

  • Model C was used to study lower body negative pressure (LBNP) and head-up tilt responses.
  • Model J was employed for simulating +Gz response.
  • Model P was utilized for simulating response to blood loss. (Note: Specific quantitative results comparing models to data are not detailed in the abstract).

Conclusions:

  • The study provides an examination of three mathematical models for cardiovascular system (CVS) simulation under orthostatic stress.
  • Comparison of model responses to experimental data is crucial for validating their predictive capabilities.
  • Further research is needed to establish the accuracy and limitations of these models across various physiological challenges.

Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Neural Regulation of Blood Pressure01:18

Neural Regulation of Blood Pressure

The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...
Cardiac Output I:Effect of Heart Rate on Cardiac Output01:19

Cardiac Output I:Effect of Heart Rate on Cardiac Output

Cardiac Output
Cardiac output (CO) refers to the total amount of blood ejected by one of the ventricles in liters per minute (L/min). In a resting adult, CO ranges from 5 to 6 L/min, adjusting according to the body's metabolic requirements.
Effect of Heart Rate on Cardiac Output
Cardiac output adapts to metabolic demands during stress, physical activity, or illness. The autonomic nervous system regulates heart rate via the sinoatrial node. The parasympathetic nervous system decreases heart rate...