Hann C E, Chase J G, Shaw G M
Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
Comput Methods Programs Biomed. 2006 Feb;81(2):181-92. doi: 10.1016/j.cmpb.2005.11.004. Epub 2006 Jan 18.
A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy selection. This paper presents an integral-based parameter identification method for fast, accurate identification of patient specific parameters using limited measured data. The integral method turns a previously non-linear and non-convex optimization problem into a linear and convex identification problem. The model includes ventricular interaction and physiological valve dynamics. A healthy human state and four disease states, valvular stenosis, pulmonary embolism, cardiogenic shock and septic shock are used to test the method. Parameters for the healthy and disease states are accurately identified using only discretized flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms through the aorta and pulmonary artery. These input values can be readily obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care. The method enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 min) to within a mean value of 4-10% in the presence of 5-15% uniformly distributed measurement noise. The specific changes made to simulate each disease state are correctly identified in each case to within 10% without false identification of any other patient specific parameters. Clinically, the resulting patient specific model can then be used to assist medical staff in understanding, diagnosis and treatment selection.
已经开发出一种最小心脏模型，该模型能准确捕捉心血管系统（CVS）的基本动态。然而，利用通常有限的测量值来识别患者特定参数，阻碍了该模型在诊断和治疗选择方面的临床应用。本文提出了一种基于积分的参数识别方法，用于使用有限的测量数据快速、准确地识别患者特定参数。积分方法将先前的非线性和非凸优化问题转化为线性和凸识别问题。该模型包括心室相互作用和生理瓣膜动态。使用健康人体状态以及四种疾病状态——瓣膜狭窄、肺栓塞、心源性休克和感染性休克来测试该方法。仅使用流入和流出两个心腔的离散流量、左右心室的最小和最大容积以及通过主动脉和肺动脉的压力波形，就能准确识别健康和疾病状态的参数。这些输入值可以通过超声心动图和超声轻松无创地获得，或者通过重症监护中常用的导管进行有创获取。该方法能够在存在5 - 15%均匀分布测量噪声的情况下，在临床实时（3 - 5分钟）内快速识别模型参数，使其与特定患者状况匹配，均值误差在4 - 10%以内。在每种情况下，模拟每种疾病状态所做的特定变化都能被正确识别，误差在10%以内，且不会错误识别任何其他患者特定参数。临床上，由此产生的患者特定模型可用于协助医护人员进行理解、诊断和治疗选择。
