利用纵向血液动力学测量检查模型优化参数的时间变化。

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Nikolai L Bjørdalsbakke, Jacob Sturdy, Ulrik Wisløff, Leif R Hellevik
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引用次数: 0

摘要

背景:我们以前曾应用血液动力学数据对以物理可解释参数表示的循环数学模型进行过个性化处理。本研究的目的是找出数据中可能解释估计参数变化的模式。这包括研究这些参数是否可用于跟踪体育锻炼对高血压的影响。临床试验已多次检测到体育锻炼后血压的有益变化,并发现了低水平表型(如血管硬化或高阻力)的变化。这些表型可以用描述循环系统机械特性的参数来表征。这些参数可被纳入基于物理学的循环心血管模型中,并与之相结合,成为未来监测心血管疾病进展和管理的工具:方法:根据一项为期 12 周的运动干预试验研究的数据,对左心室和全身循环的闭环和开环模型进行了优化。试验中收集了基础特征和血液动力学数据,如颈动脉、肱动脉和指动脉的血压,以及左心室流出道血流轨迹。根据试验期间不同日期的测量结果估算出的模型参数用于计算总外周阻力、全身动脉顺应性和左心室最大弹性的参数变化。通过相关性分析,我们将这些基于心血管模型的估计值的变化与不使用物理模型的常规估计值的变化进行了比较。此外,我们还采用了普通线性回归和线性混合效应模型来确定对所选参数最有参考价值的测量值。我们通过回归分析和案例研究,应用最大有氧能力(以 VO2max 测量)数据来研究运动是否会对参数产生影响:结果和结论:使用心血管模型估算的动脉参数变化与传统估算值的相关性适中。基于颈动脉压力波形的估算值比手指动脉压力的估算值具有更高的相关性(0.59 及以上,P 0.05)。在为期 12 周的研究过程中,参数变化的幅度与相同参数在剧烈运动后的短期变化相近。短期变化是根据心肺运动测试前和测试后 24 小时的测量值计算得出的,心肺运动测试用于测量最大氧饱和度。回归分析表明,VO2max 的变化并不能解释总外周阻力、全身动脉顺应性或最大左心室弹性的大量变化。相反,每搏量的变化所解释的变异性要大得多。研究结果表明,要想使用整块参数模型准确跟踪运动诱发的高血压前期和高血压患者的血管变化,还需要进行更多的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Examining temporal changes in model-optimized parameters using longitudinal hemodynamic measurements.

Background: We previously applied hemodynamic data to personalize a mathematical model of the circulation expressed as physically interpretable parameters. The aim of this study was to identify patterns in the data that could potentially explain the estimated parameter changes. This included investigating whether the parameters could be used to track the effect of physical activity on high blood pressure. Clinical trials have repeatedly detected beneficial changes in blood pressure after physical activity and uncovered changes in lower level phenotypes (such as stiffened or high-resistance blood vessels). These phenotypes can be characterized by parameters describing the mechanical properties of the circulatory system. These parameters can be incorporated in and contextualized by physics-based cardiovascular models of the circulation, which in combination can become tools for monitoring cardiovascular disease progression and management in the future.

Methods: Closed-loop and open-loop models of the left ventricle and systemic circulation were previously optimized to data from a pilot study with a 12-week exercise intervention period. Basal characteristics and hemodynamic data such as blood pressure in the carotid, brachial and finger arteries, as well as left-ventricular outflow tract flow traces were collected in the trial. Model parameters estimated for measurements made on separate days during the trial were used to compute parameter changes for total peripheral resistance, systemic arterial compliance, and maximal left-ventricular elastance. We compared the changes in these cardiovascular model-based estimates to changes from more conventional estimates made without the use of physics-based models by correlation analysis. Additionally, ordinary linear regression and linear mixed-effects models were applied to determine the most informative measurements for the selected parameters. We applied maximal aerobic capacity (measured as VO2max ) data to examine if exercise had any impact on parameters through regression analysis and case studies.

Results and conclusions: Parameter changes in arterial parameters estimated using the cardiovascular models correlated moderately well with conventional estimates. Estimates based on carotid pressure waveforms gave higher correlations (0.59 and above when p < 0.05 ) than those for finger arterial pressure. Parameter changes over the 12-week study duration were of similar magnitude when compared to short-term changes after a bout of intensive exercise in the same parameters. The short-term changes were computed from measurements made immediately before and 24 h after a cardiopulmonary exercise test used to measure VO2max . Regression analysis indicated that changes in VO2max did not account for any substantial amount of variability in total peripheral resistance, systemic arterial compliance, or maximal left-ventricular elastance. On the contrary, changes in stroke volume contributed to far more explained variability. The results suggest that more research is required to be able to accurately track exercise-induced changes in the vasculature for people with pre-hypertension and hypertension using lumped-parameter models.

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来源期刊
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
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