A noninvasive method of estimating patient-specific left ventricular pressure waveform

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Highlights

Abstract

Background and objective

The left ventricular pressure waveform is indispensable for the construction of the pressure strain loop when investigating coronary artery disease (CAD) patients. In previous studies by others, exclusion of CAD patients has not allowed a reliable estimation of the left ventricular pressure waveform from the pressure strain loop of these patients. To remedy this, we propose a patient-specific noninvasive method for the estimation of left ventricular pressure.

Methods

A simplified systemic circulation model consisting primarily of a single fiber model and a 1D simulation of the arterial tree was used. Sensitivity analysis based on the Morris method was performed to select a subset of the important parameters. Following this, the important parameter subset and the set of all the parameters were identified in the model using the pressure waveform of a peripheral artery as input, in a two-step process. In addition, the left ventricular pressure waveform was estimated using the set of all parameters.

Results

Reducing the size of the parameter subset significantly decreases the computational cost of parameter optimization in the first step and greatly simplifies the identification of the full parameter set in the second step. Comparison with the reference left ventricular pressure waveform from CAD patients, showed that the proposed method provides a good estimate of the reference left ventricular pressure waveform. The correlation coefficients between the estimated and reference were r = 0.907, r = 0.904 and r = 0.780 for systolic blood pressure, pulse pressure and mean blood pressure, respectively.

Conclusions

This work may provide a convenient surrogate for the estimation of the left ventricular pressure waveform.

Introduction

Coronary artery disease (CAD) is the leading cause of death worldwide [1]. Although there are numerous studies related to the diagnosis and therapy of CAD, more reliable noninvasive techniques are necessary for the diagnosis and therapy of suspected CAD patients. Recently, the left ventricular pressure strain loop has been introduced as a new way to estimate myocardial work and left ventricular function [2]. Up to now it has been used in some clinical applications [3] including cardiovascular diseases and CAD [4], [5], [6], [7].

In the analysis of the pressure strain loop, the left ventricular pressure waveform (LVPW) is a vital component. However, in the initial report of this method [2], CAD patients were excluded from the experiments because fluorodeoxyglucose positron emission tomography assesses glucose metabolism and shows increased uptake in both well oxygenated and poorly oxygenated tissue. Therefore, to overcome this deficiency in the initial study [2], we report here LVPW estimation analysis of the pressure strain loop in CAD patients.

In recent years, some studies have reported the estimation of LVPW under various pathological conditions not including CAD, [8,9] and some are based on data from animals [10,11]. In those concerned with the pressure volume loop, invasive aortic pressure data is needed to generate the LVPW [9]. In others, the left ventricle model is overly simple and there is no peripheral artery information for validation [8,11].

To our knowledge, there is no non-invasive method to estimate LVPW needed to determine the pressure strain loop in CAD patients. Thus the main objective of this study is to propose a patient-specific non-invasive LVPW estimation method for CAD patients to enable investigation of the pressure strain loop. The method is entirely non-invasive and is based on a simplified systemic circulation model and the blood pressure waveform of a peripheral vessel, which in this study is the brachial artery. Sensitivity analysis is performed to seek a subset of the crucial parameters. In this study we focus on the estimation of LVPW which can then be used in the determination of the pressure strain loop for CAD patients.

Section snippets

Method and materials

A three-stage process for patient-specific estimation of the LVPW is proposed (see Fig. 1).

In the first stage, a simplified systemic circulation model which includes the left ventricle, the aortic valve and the brachial artery is constructed. The LVPW is reconstructed by applying the brachial artery blood pressure waveform as input to the proposed model. Sensitivity analysis by means of the Morris method is performed to select a subset of the most important parameters of the proposed model.

In

Sensitivity analysis

Having performed sensitivity analysis using the Morris method, the basic statistical information of EE for the 11 waveform features and the waveform RMSE was obtained. Fig. 4 shows the results of applying μ* and σj2* to the predicted pressure waveform and features with respect to all the parameters.

According to Eq. (17), the important parameter subsets for the LVPW features and RMSE, which are shown in Table 3, can be screened.

In the next step, the union of the above subsets is constructed

Model construction

In this study, a single fiber model of the left ventricle is coupled with a 1D transmission model, terminated by a Windkessel model to construct an overall representation of the systemic circulation, which is used to generate the LVPW and BPW. Based on the BPW, patient-specific parameters can be identified and the LVPW also can be predicted. The single fiber model provides a relatively accurate 0D representation of the left ventricle compared with the time-varying elastance model [11] and is a

Conclusion

We have proposed a simple model-derived LVPW estimation method which can be used to determine the pressure strain loop in CAD patients. Based on the non-invasively determined blood pressure from a peripheral artery and sensitivity analysis of the model parameters, it is straightforward to identify the critical parameters of the model. In contrast to the reference method for determining LVPW, it is possible to estimate the LVPW accurately which makes our approach suitable for the analysis of the

Declaration of Competing Interest

All authors declare no conflict of interest.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 62273082, 61773110, 61701099), the Natural Science Foundation of Liaoning Province (Nos. 20170540312 and 2021-YGJC-14), the Basic Scientific Research Project (Key Project) of Liaoning Provincial Department of Education (LJKZ00042021), the Fundamental Research Funds for the Central Universities (No. N2119008). This research was also supported by the Shenyang Science and Technology Plan Fund (Nos. 21-104-1-24,

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