Estimating central blood pressure from aortic flow: development and assessment of algorithms

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 ± 9.7 mmHg and root-mean-square errors (RMSEs) ≤ 6.4 ± 2.8 mmHg against invasive reference cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBP errors ≤ 6.0 ± 4.7 mmHg and RMSEs ≤ 5.9 ± 2.4 mmHg 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 & NOTEWORTHY First, 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.

Keywords: blood flow models; central blood pressure; magnetic resonance imaging; ultrasound; virtual subjects.

Figure 1

Study methodology. 1) Central blood pressure (cBP) estimation algorithms consisted of three steps. A: clinical data acquisition and preprocessing: blood flow measured at the ascending and descending [one-dimensional (1-D) algorithm only] aorta; peripheral blood pressure (BP) measurement; and aortic anatomy (1-D algorithm only). B: cardiovascular (CV) parameters estimated from clinical data. C: these parameters, along with the noninvasive clinical data, were used as inputs to one of three cBP models. 2) Algorithm performance was assessed by comparing cBP estimates provided by each model to reference values.

Figure 2

Clinical central blood pressure (cBP) wave morphologies: (left) aortic coarctation dataset (obtained invasively), (middle) normotensive (noninvasive) dataset, and (right) hypertensive (noninvasive) dataset. Black lines illustrate a random patient’s cBP waveform. Shaded regions represent the range of cBP waves within each dataset.

Figure 3. Generating datasets of virtual subjects.

A, top: A range of values for each cardiovascular (CV) parameter was obtained from the clinical literature for healthy individuals (see Table A1). A, bottom: the thick line illustrates the flow wave corresponding to the baseline values of stroke volume (SV) and heart rate (HR), and the shaded region represents the range of flow waves corresponding to all SV and HR variations. B: two reduced-order models were used to generate central blood pressure (cBP) waves. C: cBP waves generated by each model: black lines illustrate the cBP wave corresponding to the baseline set of parameter variations, and shaded regions represent the range of cBP waves within each dataset.

Figure 4

Bland–Altman plots for the optimal cardiovascular (CV) parameter estimation methods. They were obtained from all one-dimensional (1-D) dataset waves using the clinical scenarios carotid + (top) and carotid– (bottom).