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A Noninvasive Method of Estimating Patient-Specific Left Ventricular Pressure Waveform.

Computer methods and programs in biomedicine(2022)

Cited 0|Views15
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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 pa-rameter 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. (c) 2022 Elsevier B.V. All rights reserved.
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Key words
Sensitivity analysis,Left ventricular pressure waveform,estimation,Parameter subset selection,Morris method,Model parameter identification
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