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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

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Related Experiment Video

Updated: May 14, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Analysis of different model-based approaches for estimating dFRC for real-time application.

Erwin J van Drunen1, J Geoffrey Chase, Yeong Shiong Chiew

  • 1University of Canterbury, Christchurch, 8041, New Zealand. erwin.vandrunen@pg.canterbury.ac.nz

Biomedical Engineering Online
|February 2, 2013
PubMed
Summary

Model-based methods accurately estimate dynamic functional residual capacity (dFRC) in patients with Acute Respiratory Distress Syndrome (ARDS) during mechanical ventilation (MV). These non-invasive approaches offer a viable clinical solution for optimizing lung recruitment and PEEP titration.

Related Experiment Videos

Last Updated: May 14, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Area of Science:

  • Pulmonary Medicine
  • Critical Care
  • Biomedical Engineering

Background:

  • Acute Respiratory Distress Syndrome (ARDS) involves lung inflammation and fluid accumulation.
  • Mechanical ventilation (MV) with positive end-expiratory pressure (PEEP) is crucial for lung recruitment in ARDS.
  • Simple, non-invasive methods to estimate dynamic functional residual capacity (dFRC) are currently lacking.

Purpose of the Study:

  • To evaluate the performance of four model-based methods for estimating dFRC.
  • To compare these methods using clinical data from two separate cohorts.
  • To identify reliable non-invasive approaches for dFRC estimation in ARDS patients.

Main Methods:

  • Four model-based methods were assessed, derived from stress-strain theory or single compartment lung models.
  • Methods utilized common parameters like lung compliance, plateau airway pressure, and pressure-volume (PV) data.
  • Performance was evaluated by comparing estimated dFRC values to clinically measured values across different PEEP levels.

Main Results:

  • The stress-strain method using multiple breaths at multiple PEEP levels achieved a high correlation (R² = 0.966).
  • A combined single and multiple PEEP level method also showed strong correlation (R² = 0.963).
  • Single breath methods demonstrated lower correlation coefficients (R² = 0.530 and R² = 0.415).

Conclusions:

  • Model-based, non-invasive methods for estimating dFRC appear clinically viable during MV.
  • These models allow dFRC estimation across various PEEP levels.
  • Method limitations and estimation errors restrict application at very low PEEP levels.