Computational Stability Analysis to Predict Heart Failure after Myocardial Infarction

预测心肌梗死后心力衰竭的计算稳定性分析

• 批准号: 10669258
• 负责人: Martin R Pfaller
• 金额: $ 15.74万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10669258
• 关键词: American; Biology; Biomechanics; Calibration; Cardiac; Cardiovascular Models; Cardiovascular system; Cell model; Cells; Clinical; Clinical Data; Collagen; Compensation; Computer Models; Confidence Intervals; Data; Data Set; Development; Development Plans; Diagnostic; Dimensions; Elastin; Engineering; Environment; Equilibrium; Family suidae; Feedback; Fibrosis; Foundations; Funding; Genetic; Goals; Growth; Heart; Heart Diseases; Heart failure; Homeostasis; Human; Impairment; Infarction; Knowledge; Left ventricular structure; Link; Location; Measurement; Mechanics; Medical Device Designs; Medical Imaging; Mentors; Modeling; Muscle Cells; Myocardial Infarction; Myocardial tissue; Myocardium; Organ; Patient-Focused Outcomes; Patients; Pattern; Performance; Phase; Physiologic intraventricular pressure; Prediction of Response to Therapy; Process; Productivity; Property; Publications; Publishing; Quality of life; Records; Research; Resources; Risk; Risk Factors; Sarcomeres; Scientist; Shapes; Stimulus; Structure; Testing; Therapeutic; Time; Tissues; Training; Universities; Validation; Ventricular; biobank; cardiac magnetic resonance imaging; career; career development; clinical decision-making; clinical predictors; clinical translation; extracellular; heart function; hemodynamics; human subject; improved; in vivo; insight; kinematics; multi-scale modeling; neglect; novel; personalized diagnostics; personalized medicine; predictive modeling; pressure; prevent; skills; targeted treatment; theories; virtual environment

PROJECT SUMMARY Myocardial infarction (MI) can lead to heart failure (HF), which severely impacts the quality of life of millions of Americans. MI triggers a cascade of cardiac growth and remodeling (G&R) patterns. They change ventricular size, shape, and function, driven by biomechanical, neurohormonal, and genetic stimuli. Adaptive short-term G&R can stabilize cardiac performance. Yet, in many patients, adverse long-term G&R is unstable and progresses to HF. Unfortunately, those patients lack robust clinical predictors because the biomechanical stimuli of adverse G&R patterns are still unclear. Computational models of full-heart biomechanics, informed by cardiac magnetic resonance imaging (CMR), show high potential to fill this gap. The foundation of this project is a novel microstructure-based model of cell-scale G&R based on the homogenized constrained mixture theory, co-developed by the applicant, Dr. Pfaller. In addition, this research plan will leverage a multiscale model that combines cell-scale G&R and organ-scale cardiac contraction and validation with CMR in swine and humans to predict the propensity to develop HF with the mechanobiological stability theory. In Aim 1, Dr. Pfaller will refine and validate a framework for subject-specific models of cardiac G&R. After calibrating the model to pressure and kinematic CMR measurements in control swine, he will introduce MI to the multiscale model and validate the prediction of G&R with matching measurements in post-MI swine. In Aim 2, Dr. Pfaller will quantify the propensity of developing adverse G&R with the mechanobiological stability theory and identify risk factors of post-MI HF from infarct properties. He will test the validity of his HF prediction with longitudinal human CMR and clinical data from the UK Biobank. Dr. Pfaller has excellent prior training in cardiac biomechanics, medical imaging, and computational engineering with an established publication record in cardiac and cardiovascular biomechanics. His career development plan (K99-phase) will provide additional training in cardiac biology and using CMR for human subjects. Dr. Pfaller will also receive a wealth of informal and didactic training at Stanford University, which will be critical for Dr. Pfaller to gain autonomy and launch a productive career as an independent engineering-scientist. Mentor Dr. Marsden is a leading expert in patient- specific modeling of the cardiovascular system. Co-Mentor Dr. Ennis (CMR) and advisors Drs. Humphrey (cell- scale modeling), Cyron (stability theory), Kuhl (organ-scale modeling), Yang (cardiac biology), Salerno (heart failure) offer complementary expertise. Dr. Pfaller will receive the necessary guidance and resources to accomplish these goals and efficiently transition to independence (R00-phase). In summary, the strong mentoring environment and training plan will fully prepare Dr. Pfaller to launch his independent career. The proposed studies promise to offer insights into biomechanical stimuli of adverse G&R and help optimize diagnostics and therapies that predict and ultimately prevent HF after MI.

项目摘要 心肌梗死（MI）可导致心力衰竭（HF），严重影响数百万人的生活质量。 美国人MI触发心脏生长和重塑（G & R）模式的级联。它们改变了心室 大小，形状和功能，由生物力学，神经激素和遗传刺激驱动。适应性短期 G & R可以稳定心脏性能。然而，在许多患者中，不利的长期G & R是不稳定的， 进展为HF。不幸的是，这些患者缺乏可靠的临床预测，因为生物力学 不利G & R模式的刺激仍然不清楚。全心脏生物力学的计算模型，由 心脏磁共振成像（CMR）显示出填补这一空白的巨大潜力。这个项目的基础 是一种基于均匀化约束混合的细胞尺度G & R模型 该理论由申请人Pfaller博士共同开发。此外，该研究计划将利用多尺度 结合细胞级G & R和器官级心脏收缩的模型，并在猪体内进行CMR验证 和人类预测的倾向，发展HF与机械生物学稳定性理论。在目标1中，博士 Pfaller将完善和验证心脏G & R的特定主题模型的框架。校准后， 模型的压力和运动学CMR测量控制猪，他将介绍MI的多尺度 在MI后猪中用匹配的测量值对G & R的预测进行建模和验证。在Aim 2中，Pfaller博士 将用机械生物学稳定性理论量化发展不良G & R的倾向，并确定 心肌梗死后HF的危险因素。他将测试他的HF预测的有效性与纵向 人类CMR和来自英国生物库的临床数据。Pfaller医生之前接受过良好的心脏 生物力学，医学成像和计算工程，并在 心脏和心血管生物力学。他的职业发展计划（K99阶段）将提供额外的 心脏生物学培训和对人类受试者使用CMR。Pfaller博士还将获得丰富的非正式 以及在斯坦福大学的教学培训，这对Pfaller博士获得自主权和启动一个 作为一名独立的工程科学家，他的职业生涯富有成效。导师马斯登博士是一位领先的专家，在病人- 心血管系统的具体建模。共同导师恩尼斯博士（CMR）和顾问汉弗莱博士（细胞- 规模建模）、Cyron（稳定性理论）、Kuhl（器官规模建模）、Yang（心脏生物学）、萨莱诺（心脏 #21453;的专业知识进行补充。Pfaller博士将得到必要的指导和资源， 实现这些目标，并有效地过渡到独立（R00阶段）。总之，强者 指导环境和培训计划将为普法勒博士开始他的独立职业生涯做好充分准备。的 拟议的研究有望提供对不利G & R的生物力学刺激的见解， 预测并最终预防MI后HF的诊断和治疗。

项目成果

Learning reduced-order models for cardiovascular simulations with graph neural networks.

使用图神经网络学习心血管模拟的降阶模型。

• DOI: 10.1016/j.compbiomed.2023.107676
• 发表时间: 2024
• 期刊: Computers in biology and medicine
• 影响因子: 7.7
• 作者: Pegolotti,Luca;Pfaller,MartinR;Rubio,NataliaL;Ding,Ke;BrugarolasBrufau,Rita;Darve,Eric;Marsden,AlisonL
• 通讯作者: Marsden,AlisonL

A probabilistic neural twin for treatment planning in peripheral pulmonary artery stenosis.

用于外周肺动脉狭窄治疗计划的概率神经双胞胎。

• DOI: 10.1002/cnm.3820
• 发表时间: 2024
• 期刊: International journal for numerical methods in biomedical engineering
• 影响因子: 2.1
• 作者: Lee,JohnD;Richter,Jakob;Pfaller,MartinR;Szafron,JasonM;Menon,Karthik;Zanoni,Andrea;Ma,MichaelR;Feinstein,JeffreyA;Kreutzer,Jacqueline;Marsden,AlisonL;Schiavazzi,DanieleE
• 通讯作者: Schiavazzi,DanieleE

A homogenized constrained mixture model of cardiac growth and remodeling: analyzing mechanobiological stability and reversal.

• DOI: 10.1007/s10237-023-01747-w
• 发表时间: 2023-12
• 期刊: BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
• 影响因子: 3.5
• 作者: Gebauer, Amadeus M.;Pfaller, Martin R.;Braeu, Fabian A.;Cyron, Christian J.;Wall, Wolfgang A.
• 通讯作者: Wall, Wolfgang A.

Non-invasive estimation of pressure drop across aortic coarctations: validation of 0D and 3D computational models with in vivo measurements.

主动脉缩窄压降的无创估计：通过体内测量验证 0D 和 3D 计算模型。

• DOI: 10.1101/2023.09.05.23295066
• 发表时间: 2023
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Nair,PriyaJ;Pfaller,MartinR;Dual,SerainaA;McElhinney,DoffB;Ennis,DanielB;Marsden,AlisonL
• 通讯作者: Marsden,AlisonL