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Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

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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.
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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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...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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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...
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Autoregulation of Blood Flow01:17

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Autoregulation mechanisms are characterized by their inherent capacity for self-regulation without necessitating specific nervous stimulation or endocrine control. These mechanisms facilitate the adjustment of blood flow and, therefore, perfusion specific to each tissue region. This self-regulation encompasses chemical signals and myogenic controls.
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Updated: Oct 13, 2025

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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Parameter estimation for closed-loop lumped parameter models of the systemic circulation using synthetic data.

Nikolai L Bjørdalsbakke1, Jacob T Sturdy1, David R Hose2

  • 1Department of Structural Engineering, Norwegian University of Science and Technology (NTNU), Richard Birkelandsvei 1a, Trondheim, 7491, Norway.

Mathematical Biosciences
|November 10, 2021
PubMed
Summary

Personalizing physics-based hemodynamic models requires careful parameter estimation. Continuous waveform data and focusing on sensitive parameters improve accuracy, accelerating personalized medicine development.

Keywords:
Lumped parameter modelsMathematical optimizationParameter estimationSensitivity analysisSystemic circulation

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Area of Science:

  • Physiology
  • Biomedical Engineering
  • Computational Biology

Background:

  • Physics-based models offer potential for personalized medicine by describing physiological mechanisms in health and disease.
  • Personalizing systemic hemodynamic models requires accurate estimation of model parameters.

Purpose of the Study:

  • To investigate the feasibility of personalizing a lumped parameter model of the left heart and systemic circulation.
  • To evaluate the impact of different data sets and noise levels on parameter estimation accuracy.

Main Methods:

  • Utilized a step-wise subset reduction method for parameter estimation.
  • Performed structural identifiability and sensitivity analyses to rank parameter influence.
  • Optimized the model to synthetic and noisy data using continuous waveforms and clinical indices.

Main Results:

  • Noiseless calibration achieved <10^-3% error with time series data, but >100% error with clinical indices.
  • With 5% noise, accuracy was within 10% for the five most sensitive parameters; least sensitive parameters were unreliable.
  • Continuous waveform data yielded significantly better parameter estimates than clinical indices, even with noise.

Conclusions:

  • Prioritizing measurement of the three least sensitive parameters is crucial for improving model accuracy.
  • Continuous waveform data provides superior parameter estimation compared to standard clinical indices.
  • Adding venous pressure or fixing venous compliance improved parameter estimates, particularly for diastolic filling parameters.