Abstract
Central blood pressure (cBP) is a highly prognostic cardiovascular (CV) risk factor whose accurate, invasive assessment is costly and carries risks to patients. We developed and assessed novel algorithms for estimating cBP from noninvasive aortic hemodynamic data and a peripheral blood pressure measurement. These algorithms were created using three blood flow models: the two- and three-element Windkessel (0-D) models and a one-dimensional (1-D) model of the thoracic aorta. We tested new and existing methods for estimating CV parameters (left ventricular ejection time, outflow BP, arterial resistance and compliance, pulse wave velocity, and characteristic impedance) required for the cBP algorithms, using virtual (simulated) subjects (n = 19,646) for which reference CV parameters were known exactly. We then tested the cBP algorithms using virtual subjects (n = 4,064), for which reference cBP were available free of measurement error, and clinical datasets containing invasive (n = 10) and noninvasive (n = 171) reference cBP waves across a wide range of CV conditions. The 1-D algorithm outperformed the 0-D algorithms when the aortic vascular geometry was available, achieving central systolic blood pressure (cSBP) errors≤2.1 ± 9.7mmHg and root-mean-square errors (RMSEs)≤6.4 ± 2.8mmHg against invasive reference cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBP errors ≤ 6.0 ± 4.7mmHg and RMSEs ≤ 5.9 ± 2.4mmHg against noninvasive reference cBP waves (n = 171), outperforming the two-element 0-D algorithm. All CV parameters were estimated with mean percentage errors ≤ 8.2%, except for the aortic characteristic impedance (≤13.4%), which affected the three-element 0-D algorithm’s performance. The freely available algorithms developed in this work enable fast and accurate calculation of the cBP wave and CV parameters in datasets containing noninvasive ultrasound or magnetic resonance imaging data.New & NoteworthyFirst, our proposed methods for CV parameter estimation and a comprehensive set of methods from the literature were tested using in silico and clinical datasets. Second, optimized algorithms for estimating cBP from aortic flow were developed and tested for a wide range of cBP morphologies, including catheter cBP data. Third, a dataset of simulated cBP waves was created using a three-element Windkessel model. Fourth, the Windkessel model dataset and optimized algorithms are freely available.
Highlights
Recent clinical studies have shown that central blood pressure is a better cardiovascular risk indicator than brachial blood pressure [1,2,3,4], since central (aortic) blood pressure (cBP) is more representative of the load exerted on major organs [1, 5]
These methods were used in the cBP estimation algorithms
We have presented freely available, fast, patient-specific algorithms to estimate clinically relevant CV parameters and reconstruct the cBP wave from the aortic flow wave, using noninvasive data and patient-specific models of aortic blood flow
Summary
Recent clinical studies have shown that central (aortic) blood pressure (cBP) is a better cardiovascular risk indicator than brachial blood pressure (bBP) [1,2,3,4], since cBP is more representative of the load exerted on major organs [1, 5]. A potential approach is to use a computational model of the circulation to estimate cBP from noninvasive measurements of aortic flow and peripheral blood pressure (BP) [8]. Computational models could be personalized to estimate cBP in cardiac MRI and US settings. These imaging modalities are the gold standard when assessing cardiac anatomy (cardiac magnetic resonance and echocardiography). Combining the information they provide with the knowledge of cBP could enable the noninvasive derivation of pressure-volume loops and myocardial wall stress, two major indicators of cardiac performance. Previous studies have used reducedorder models to estimate cBP noninvasively, they either did not use patient-specific MRI aortic geometry [10] or did not validate their cBP estimates against invasive cBP measurements or compare the performance of several algorithms [8, 11,12,13,14]
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