Deep phenotyping of cardiac function in heart transplant patients using cardiovascular system models
Deep phenotyping of cardiac function in heart transplant patients using cardiovascular system models
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DOI：
10.1113/jp279393
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发表时间：
2020-06-23
影响因子：
5.5
通讯作者：
Carlson, Brian E.
中科院分区：
文献类型：
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作者：
Colunga, Amanda L.;Kim, Karam G.;Carlson, Brian E.
要点来自心脏移植患者临床记录的右心导管插入数据用于识别患者特定的心血管系统模型。这些患者特异性心血管模型代表了给定移植后恢复时间点心血管功能的快照。该方法用于描述 10 名心脏移植患者的心脏功能，其中 5 名患者接受了多次右心导管插入术，可以随时间评估心脏功能。这些患者特定模型用于以左右心室压力-容量环和心室功率的形式预测心血管功能，这是心脏功能临床评估的重要指标。纵向跟踪患者的结果表明，我们的方法能够从五名患者中识别出一名表现出移植后心血管并发症的患者。心脏移植患者定期接受右心导管检查 (RHC)，以识别移植后并发症并指导治疗。移植后的积极结果与右心室和肺动脉压力的稳定降低有关，接近 RHC 测量的右侧压力的正常水平（约 20 mmHg）。这项研究表明，通过将标准 RHC 测量与机械计算心血管系统模型相结合，可以获得有关患者进展的更多信息。这项研究的目的有两个：了解如何使用心血管系统模型来代表患者的心血管状态，并使用这些模型来跟踪移植后的恢复和结果。为了获得数据集内和数据集之间可比较的可靠参数估计，我们使用敏感性分析、参数子集选择和优化来确定可以从 RHC 数据中可靠提取的患者特定机械参数。从首次移植后 RHC 中确定了 10 名患者的患者特异性模型，并对 5 名患者进行了纵向分析。敏感性分析和子集选择的结果表明，我们可以可靠地估计七个不可测量的量；即心室舒张期舒张、全身阻力、肺静脉弹性、肺阻力、肺动脉弹性、肺动脉瓣阻力和全身动脉弹性。利用移植后参数的变化和预测的心血管功能来评估五名患者恢复期间的心血管状态。在这五名患者中，只有一名在恢复过程中心室压力-容积关系和功率输出表现出不一致的趋势。在移植后四年的时间点，该患者表现出双心室衰竭和移植物功能障碍，而其余四名患者没有表现出心血管并发症。
Key pointsRight heart catheterization data from clinical records of heart transplant patients are used to identify patient-specific models of the cardiovascular system. These patient-specific cardiovascular models represent a snapshot of cardiovascular function at a given post-transplant recovery time point. This approach is used to describe cardiac function in 10 heart transplant patients, five of which had multiple right heart catheterizations allowing an assessment of cardiac function over time. These patient-specific models are used to predict cardiovascular function in the form of right and left ventricular pressure-volume loops and ventricular power, an important metric in the clinical assessment of cardiac function. Outcomes for the longitudinally tracked patients show that our approach was able to identify the one patient from the group of five that exhibited post-transplant cardiovascular complications. Heart transplant patients are followed with periodic right heart catheterizations (RHCs) to identify post-transplant complications and guide treatment. Post-transplant positive outcomes are associated with a steady reduction of right ventricular and pulmonary arterial pressures, toward normal levels of right-side pressure (about 20 mmHg) measured by RHC. This study shows that more information about patient progression is obtained by combining standard RHC measures with mechanistic computational cardiovascular system models. The purpose of this study is twofold: to understand how cardiovascular system models can be used to represent a patient's cardiovascular state, and to use these models to track post-transplant recovery and outcome. To obtain reliable parameter estimates comparable within and across datasets, we use sensitivity analysis, parameter subset selection, and optimization to determine patient-specific mechanistic parameters that can be reliably extracted from the RHC data. Patient-specific models are identified for 10 patients from their first post-transplant RHC, and longitudinal analysis is carried out for five patients. Results of the sensitivity analysis and subset selection show that we can reliably estimate seven non-measurable quantities; namely, ventricular diastolic relaxation, systemic resistance, pulmonary venous elastance, pulmonary resistance, pulmonary arterial elastance, pulmonary valve resistance and systemic arterial elastance. Changes in parameters and predicted cardiovascular function post-transplant are used to evaluate the cardiovascular state during recovery of five patients. Of these five patients, only one showed inconsistent trends during recovery in ventricular pressure-volume relationships and power output. At the four-year post-transplant time point this patient exhibited biventricular failure along with graft dysfunction while the remaining four exhibited no cardiovascular complications.