A noninvasive method of estimating patient-specific left ventricular pressure waveform. (December 2022)

• Record Type: Journal Article
• Authors: Liu, Jun; Hao, Liling; van de Vosse, Frans; Xu, Lisheng
• Is Part Of: Computer methods and programs in biomedicine. Volume 227(2022)
• Publication Date: 2022-12
• Subjects: Sensitivity analysis -- Left ventricular pressure waveform estimation -- Parameter subset selection -- Morris method -- Model parameter identification; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107192
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Simplified systemic circulation is modelled for estimation. Peripheral artery pressure is measured non-invasively. Sensitivity analysis is used to select a subset of the important model parameters. Optimization of important parameter subset facilitates parameter identification. Left ventricle pressure in coronary artery disease patients can be estimated. 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.