Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands.
Nat Rev Cardiol. 2019 Feb;16(2):100-111. doi: 10.1038/s41569-018-0104-y.
The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.
心脏病学实践中对个体患者的治疗越来越依赖于先进的影像学、基因筛查和器械。随着影像学和其他诊断数据的增加，以及治疗个性化能力的提高，利用患者的全部测量值来确定最佳治疗方案的难度似乎也在增加。计算模型通过为整合来自个体患者的多个数据集提供一个通用框架来逐步解决这个问题。这些模型基于生理学和物理学，而不是基于人口统计学，使计算模拟能够揭示原本隐藏的诊断信息，并预测个体患者的治疗结果。心脏病学中对患者特异性模型的内在需求是显而易见的，这推动了用于创建个性化方法以指导药物治疗、器械部署和手术干预的工具和技术的快速发展。
