An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle.
An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle.
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DOI：
10.1371/journal.pcbi.1010017
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发表时间：
2022-09
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
对肺血管疾病和肺动脉高压(PH)的体内研究为右室(RV)功能障碍的进展提供了关键的见解。其他使用多尺度计算模型的电子计算机实验已经提供了在PH存在的情况下双心室力学和血流动力学功能的更多细节，但很少有人在数据收集之前评估模型参数是否实际可识别。此外，还没有人使用建模来设计协同实验设计。为了解决这一知识差距，我们通过四个模拟实验设计对一个多尺度心血管模型进行了实际可识别性分析。我们使用Morris筛选和局部灵敏度分析的组合来确定一组参数，并使用基于轮廓似然的置信度区间来测试实际的可识别性。我们使用马尔可夫链蒙特卡罗(MCMC)技术来量化参数，并在存在噪声污染的数据中对预测不确定性进行建模。我们的结果表明，仅对RV压力进行模型校准存在实际可辨识性问题，并且在输出空间存在较大的预测不确定性。相反，一旦包括额外的左心室(LV)压力和容量数据，参数和模型预测的不确定性就大大减少了。单点收缩和舒张期LV数据与连续的、随时间变化的LV压力-容量数据的比较表明，至少应该包括来自两个心室的一些定量数据用于未来的实验研究。心脏动力学的计算模型在理解疾病的潜在机制方面正变得越来越有用。电子计算机分析对了解肺血管疾病和最终的RV功能障碍特别有洞察力，因为这些情况在疾病发生后几个月到几年才被诊断出来。许多研究人员将计算模型与体内PH实验模型结合在一起，但很少有人在设计实验之前评估哪些数据可能是参数推断所必需的或足够的。在这里，我们考虑了一个包括肌节动力学、双心室相互作用和血管血流动力学的多尺度计算模型，并评估了在有限的心脏数据下是否可以准确地推断参数。我们使用敏感性分析、轮廓似然可信区间和MCMC来量化参数的影响和不确定性。我们观察到，RV压力本身不足以推断模型中的影响参数，而RV和LV中的联合压力和体积数据减少了模型参数和模型预测中的不确定性。我们的结论是，利用计算模型的协同PH研究包括这些数据，以减少与实际参数可辨识性的问题，并将不确定性降至最低。
In-vivo studies of pulmonary vascular disease and pulmonary hypertension (PH) have provided key insight into the progression of right ventricular (RV) dysfunction. Additional in-silico experiments using multiscale computational models have provided further details into biventricular mechanics and hemodynamic function in the presence of PH, yet few have assessed whether model parameters are practically identifiable prior to data collection. Moreover, none have used modeling to devise synergistic experimental designs. To address this knowledge gap, we conduct a practical identifiability analysis of a multiscale cardiovascular model across four simulated experimental designs. We determine a set of parameters using a combination of Morris screening and local sensitivity analysis, and test for practical identifiability using profile likelihood-based confidence intervals. We employ Markov chain Monte Carlo (MCMC) techniques to quantify parameter and model forecast uncertainty in the presence of noise corrupted data. Our results show that model calibration to only RV pressure suffers from practical identifiability issues and suffers from large forecast uncertainty in output space. In contrast, parameter and model forecast uncertainty is substantially reduced once additional left ventricular (LV) pressure and volume data is included. A comparison between single point systolic and diastolic LV data and continuous, time-dependent LV pressure-volume data reveals that at least some quantitative data from both ventricles should be included for future experimental studies. Computational models of cardiac dynamics are becoming increasingly useful in understanding the underlying mechanisms of disease. In-silico analyses are especially insightful in understanding pulmonary vascular disease and eventual RV dysfunction, as these conditions are diagnosed months to years after disease onset. Many researchers couple computational models with in-vivo experimental models of PH, yet few ever assess what data might be necessary or sufficient for parameter inference prior to designing their experiments. Here, we considered a multiscale computational model including sarcomere dynamics, biventricular interactions, and vascular hemodynamics, and assessed whether parameters could be inferred accurately given limited cardiac data. We utilized sensitivity analyses, profile likelihood confidence intervals, and MCMC to quantify parameter influence and uncertainty. We observed that RV pressure alone is not sufficient to infer the influential parameters in the model, whereas combined pressure and volume data in both the RV and LV reduced uncertainty in model parameters and in model forecasts. We conclude that synergistic PH studies utilizing computational modeling include these data to reduce issues with practical parameter identifiability and minimize uncertainty.
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DOI：
10.1093/function/zqaa018
发表时间：
2020
期刊：
Function (Oxford, England)
影响因子：
--
作者：
Lopez R;Marzban B;Gao X;Lauinger E;Van den Bergh F;Whitesall SE;Converso-Baran K;Burant CF;Michele DE;Beard DA
通讯作者：
Beard DA
DOI：
10.1137/090757009
发表时间：
2011-01-01
期刊：
SIAM review. Society for Industrial and Applied Mathematics
影响因子：
--
作者：
Miao H;Xia X;Perelson AS;Wu H
通讯作者：
Wu H
影响因子：
4.9
作者：
Campolongo, Francesca;Cariboni, Jessica;Saltelli, Andrea
通讯作者：
Saltelli, Andrea
影响因子：
5.5
作者：
Colunga, Amanda L.;Kim, Karam G.;Carlson, Brian E.
通讯作者：
Carlson, Brian E.
影响因子：
2.5
作者：
Gerringer JW;Wagner JC;Vélez-Rendón D;Valdez-Jasso D
通讯作者：
Valdez-Jasso D