Computational modelling for congenital heart disease: how far are we from clinical translation?
Computational modelling for congenital heart disease: how far are we from clinical translation?
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DOI：
10.1136/heartjnl-2016-310423
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发表时间：
2017-01-15
期刊：
影响因子：
--
通讯作者：
Schievano S
中科院分区：
文献类型：
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作者：
Biglino G;Capelli C;Bruse J;Bosi GM;Taylor AM;Schievano S
在过去 20 年里，先天性心脏病 (CHD) 的计算模型变得越来越复杂。它们可以深入了解复杂的流动现象，允许根据患者特定的解剖结构（先心病前或先心病修复后）测试设备并生成预测数据。这已应用于不同的先心病情况，包括单心室、法洛四联症、主动脉缩窄和大动脉转位患者。事实证明，针对特定患者的模拟可以为复杂病例的术前规划提供信息，从而允许虚拟支架部署。统计形状模型等新技术可以进一步帮助冠心病的形态学评估、患者的风险分层以及可能识别新的“形状生物标志物”。心血管统计形状模型可以为法洛四联症中的心室生长或修复缩窄中的主动脉弓形态差异等现象提供有价值的见解。在不断向更真实的模拟迈进的过程中，模型还可以解释多尺度现象（例如血栓形成），并且重要的是包括不确定性的测量（即围绕模拟结果的 CI）。虽然它们在帮助理解先心病、手术/程序决策和个性化治疗方面的潜力是不可否认的，但在先心病领域计算模型的临床转化之前仍然缺乏重要的要素，即大型验证研究、成本效益评估和确定患者结果的可能改善。
Computational models of congenital heart disease (CHD) have become increasingly sophisticated over the last 20 years. They can provide an insight into complex flow phenomena, allow for testing devices into patient-specific anatomies (pre-CHD or post-CHD repair) and generate predictive data. This has been applied to different CHD scenarios, including patients with single ventricle, tetralogy of Fallot, aortic coarctation and transposition of the great arteries. Patient-specific simulations have been shown to be informative for preprocedural planning in complex cases, allowing for virtual stent deployment. Novel techniques such as statistical shape modelling can further aid in the morphological assessment of CHD, risk stratification of patients and possible identification of new ‘shape biomarkers’. Cardiovascular statistical shape models can provide valuable insights into phenomena such as ventricular growth in tetralogy of Fallot, or morphological aortic arch differences in repaired coarctation. In a constant move towards more realistic simulations, models can also account for multiscale phenomena (eg, thrombus formation) and importantly include measures of uncertainty (ie, CIs around simulation results). While their potential to aid understanding of CHD, surgical/procedural decision-making and personalisation of treatments is undeniable, important elements are still lacking prior to clinical translation of computational models in the field of CHD, that is, large validation studies, cost-effectiveness evaluation and establishing possible improvements in patient outcomes.