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相关概念视频

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

102
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
102
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

398
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
398
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

29
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...
29
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

42
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
42
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

79
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
79
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

3.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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相关实验视频

Updated: Jun 7, 2025

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery
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Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Published on: December 6, 2024

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冠状动脉循环模型的多可靠性估计器在临床信息数据不确定性的基础上

Jongmin Seo1, Casey Fleeter2, Andrew M Kahn3

  • 1Department of Pediatrics (Cardiology), Bioengineering and ICME, Stanford University, Stanford, California, USA.

International journal for uncertainty quantification
|November 11, 2024
PubMed
概括

在冠状动脉疾病模型中量化不确定性对于准确的诊断至关重要. 本研究使用多忠实蒙特卡洛方法来提高患者特定血流模拟的可靠性,降低计算成本并提高准确性.

关键词:
进行心血管模拟.冠状动脉血动力学 冠状动脉血动力学多忠诚度框架多忠诚度框架不确定性量化不确定性的量化.

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科学领域:

  • 计算流体动力学的流体动力学.
  • 生物医学工程 生物医学工程
  • 心血管研究的心血管研究.

背景情况:

  • 数字模型对于诊断冠状动脉疾病 (CAD) 和计划治疗至关重要.
  • 由于输入不确定性,目前的决定性模型缺乏对模拟输出变量的定量评估.
  • 准确的患者特定模型需要精确的参数,如大动脉压力波形和心内压力.

研究的目的:

  • 量化输入参数不确定性对冠状动脉循环模型中临床相关输出的影响.
  • 开发和验证患者特定冠状动脉模型中不确定性量化计算框架.
  • 为了降低复杂的血液动力学模拟中不确定性传播的计算成本.

主要方法:

  • 开发了左冠状动脉的可变形模型,使用任意-拉格朗基-欧勒尔的流体结构相互作用框架.
  • 从重复的冠心内导管测量和文献数据中估计的随机输入不确定性.
  • 采用多真实性蒙特卡洛估计器,包括0D一次性参数模型,以降低计算成本和改进差异估计.

主要成果:

  • 与传统的蒙特卡洛方法相比,多忠实蒙特卡洛估计器显著降低了差异,并提高了准确性.
  • 将3D血液动力学模拟与0D块式参数网络模型相结合,产生了最准确的结果.
  • 综合方法的计算开销是可以忽略不计的 (不到1%).

结论:

  • 不确定性量化对于临床实践中可靠的患者特异性冠状动脉循环建模至关重要.
  • 多真实性蒙特卡洛方法为复杂的心血管模拟中的不确定性传播提供了一种高效和准确的方法.
  • 低保真模型与高保真模拟的整合提供了一个强大的工具,用于在CAD中推进非侵入性诊断和治疗规划.