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左心室收缩末期压力-容积关系的可识别性

Identifiability of left ventricular end-systolic pressure-volume relationships.

作者信息

van der Linden L P, van der Velde E T, Bruschke A V, Baan J

机构信息

Department of Cardiology, Academisch Ziekenhuis Leiden, The Netherlands.

出版信息

Am J Physiol. 1988 Jun;254(6 Pt 2):H1113-24. doi: 10.1152/ajpheart.1988.254.6.H1113.

Abstract

A widely accepted model of the left ventricle (LV) consisting of a time-varying elastance and a nonlinear internal resistance was investigated to make inferences about the identifiability of its parameters by means of simulated experiments. We aimed to retrieve maximum elastance (Emax) and dead volume (Vd) by the usual slope method or end-systolic pressure-volume relations (ESPVR) and by model-based parameter identification. The ESPVR deviated increasingly from the assigned values with increasing internal resistance depending on the type of loading intervention. Model-based parameter identification proved to be hampered by considerable error propagation if applied to single contractions with noise on the data. Better results were obtained by reducing the number of parameters to be estimated or by combining contractions with different loading conditions. The LV model was also matched with experimental data in three open-chest anesthetized dogs when both methods of estimation were used. The trend of ESPVR was in accordance with the model predictions, with larger Emax and larger Vd observed with arterial rather than with venous loading. Inclusion of an internal resistance in the classical elastance model can explain the dependence of the ESPVR on the type of loading intervention. However, application of model-based parameter identification indicates that the model fails to represent the entire systolic pressure-volume time course of the in situ LV.

摘要

研究了一种广泛接受的左心室(LV)模型,该模型由时变弹性和非线性内阻组成,通过模拟实验推断其参数的可识别性。我们旨在通过常用的斜率法或收缩末期压力-容积关系(ESPVR)以及基于模型的参数识别来获取最大弹性(Emax)和死腔容积(Vd)。根据加载干预的类型,随着内阻增加,ESPVR与指定值的偏差越来越大。如果将基于模型的参数识别应用于数据存在噪声的单次收缩,会因显著的误差传播而受阻。通过减少待估计参数的数量或结合不同加载条件的收缩可获得更好的结果。当使用两种估计方法时,LV模型还与三只开胸麻醉犬的实验数据进行了匹配。ESPVR的趋势与模型预测一致,动脉加载比静脉加载观察到更大的Emax和更大的Vd。在经典弹性模型中纳入内阻可以解释ESPVR对加载干预类型的依赖性。然而,基于模型的参数识别应用表明,该模型无法代表原位LV的整个收缩期压力-容积时间过程。

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