Data_Sheet_1_Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling.PDF

Introduction: Computational models of the cardiovascular system are widely used to simulate cardiac (dys)function. Personalization of such models for patient-specific simulation of cardiac function remains challenging. Measurement uncertainty affects accuracy of parameter estimations. In this study, we present a methodology for patient-specific estimation and uncertainty quantification of parameters in the closed-loop CircAdapt model of the human heart and circulation using echocardiographic deformation imaging. Based on patient-specific estimated parameters we aim to reveal the mechanical substrate underlying deformation abnormalities in patients with arrhythmogenic cardiomyopathy (AC).
Methods: We used adaptive multiple importance sampling to estimate the posterior distribution of regional myocardial tissue properties. This methodology is implemented in the CircAdapt cardiovascular modeling platform and applied to estimate active and passive tissue properties underlying regional deformation patterns, left ventricular volumes, and right ventricular diameter. First, we tested the accuracy of this method and its inter- and intraobserver variability using nine datasets obtained in AC patients. Second, we tested the trueness of the estimation using nine in silico generated virtual patient datasets representative for various stages of AC. Finally, we applied this method to two longitudinal series of echocardiograms of two pathogenic mutation carriers without established myocardial disease at baseline.
Results: Tissue characteristics of virtual patients were accurately estimated with a highest density interval containing the true parameter value of 9% (95% CI [0–79]). Variances of estimated posterior distributions in patient data and virtual data were comparable, supporting the reliability of the patient estimations. Estimations were highly reproducible with an overlap in posterior distributions of 89.9% (95% CI [60.1–95.9]). Clinically measured deformation, ejection fraction, and end-diastolic volume were accurately simulated. In presence of worsening of deformation over time, estimated tissue properties also revealed functional deterioration.
Conclusion: This method facilitates patient-specific simulation-based estimation of regional ventricular tissue properties from non-invasive imaging data, taking into account both measurement and model uncertainties. Two proof-of-principle case studies suggested that this cardiac digital twin technology enables quantitative monitoring of AC disease progression in early stages of disease.

引言：心血管系统计算模型被广泛用于模拟心脏（功能异常）状态。针对患者特异性心脏功能模拟的此类模型的个性化定制仍颇具挑战，而测量不确定性会对参数估计的准确性造成负面影响。本研究提出一种方法，利用超声心动图形变成像，对人类心脏与循环系统的闭环CircAdapt模型中的参数进行患者特异性估计与不确定性量化。基于患者特异性估计得到的参数，本研究旨在揭示致心律失常性心肌病（arrhythmogenic cardiomyopathy, AC）患者形变异常背后的力学基础。
方法：本研究采用自适应多重重要性采样来估计局部心肌组织特性的后验分布。该方法已在CircAdapt心血管建模平台中实现，被用于估计支撑局部形变模式、左心室容积及右心室直径的主动与被动组织特性。首先，我们使用9例AC患者的数据集，验证了该方法的准确性及其观察者间与观察者内变异。其次，我们采用9组计算机模拟生成的、代表AC不同疾病阶段的虚拟患者数据集，检验了该估计方法的真实性。最后，我们将该方法应用于2例致病性突变携带者的纵向超声心动图序列，这2名受试者在基线时未确诊心肌疾病。
结果：虚拟患者的组织特性被准确估计，包含真实参数值的最高密度区间占比为9%（95%置信区间[0–79]）。患者数据集与虚拟数据集的估计后验分布方差相当，证实了患者参数估计的可靠性。该估计具有高度可重复性，其后验分布重叠率达89.9%（95%置信区间[60.1–95.9]）。临床测量得到的形变、射血分数与舒张末期容积均被准确模拟。当形变随时间出现恶化时，估计得到的组织特性也反映出功能恶化情况。
结论：该方法可基于无创成像数据，同时考虑测量与模型不确定性，实现局部心室组织特性的患者特异性模拟估计。两项原理验证案例研究表明，这项心脏数字孪生技术可实现AC疾病早期阶段的定量病情监测。