针对患者特定建模的心血管一次性参数模型的灵敏度分析和优化
Siti Munirah Muhammad Ali1,2, Wahbi El-Bouri3, Wan Naimah Wan Ab Naim1
1Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia.
概括
准确的患者特异性心血管模型至关重要. 这项研究使用灵敏度分析和遗传算法改进了参数估计,提高了预测平均动脉压 (MAP) 的模型准确性.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 心血管系统 心血管系统
背景情况:
- 准确的患者特异性心血管模型对于理解和治疗心血管疾病至关重要.
- 这些复杂模型中的参数估计是一个重大挑战,影响了它们的临床适用性.
- 一次性参数模型为心血管模拟提供了一种简单但有效的方法.
研究的目的:
- 开发和验证一个框架,用于增强参数估计患者特异性一次性参数心血管模型.
- 为了提高心血管模型输出的准确性,特别是平均动脉压 (MAP).
- 利用灵敏度分析和多目标遗传算法进行强大的参数优化.
主要方法:
- 实施了一个框架,将参数识别的灵敏度分析与多目标遗传算法优化相结合.
- 识别和优化了四个关键的影响参数,这些参数在加量参数心血管模型中具有重要影响.
- 经验证的模型输出,特别是平均动脉压 (MAP),与来自公共数据库的临床数据相比.
主要成果:
- 优化模型表明模拟和临床平均动脉压 (MAP) 之间存在高度显著的相关性 (r = 0.99997,p < 0.001).
- 模型的MAP和临床MAP之间的统计等价性使用t-测试 (p = 0.752) 得到证实.
- 灵敏度分析成功地确定了优化最有影响力的参数.
结论:
- 拟议的框架显著提高了患者特异性心血管模型中的参数估计准确性.
- 灵敏度分析和遗传算法的结合为优化一次性参数模型提供了强大的工具.
- 这种方法具有很大的潜力,可以提高患者特异性心血管模拟的可靠性和临床效用.
相关概念视频
01:26Pharmacokinetic Models: Comparison and Selection Criterion
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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 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.
153
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
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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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01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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103
01:15Model 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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00:57Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models
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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...
156
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
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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...
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...
131