Estimating central blood pressure from aortic flow: development and assessment of algorithms
Mariscal-Harana, Jorge; Charlton, Peter H; Vennin, Samuel; Aramburu, Jorge; Florkow, Mateusz Cezary; van Engelen, Arna; Schneider, Torben; de Bliek, Hubrecht; Ruijsink, Bram; Valverde, Israel; Beerbaum, Philipp; Grotenhuis, Heynric; Charakida, Marietta; Chowienczyk, Phil; Sherwin, Spencer J; Alastruey, Jordi
(2021) American Journal of Physiology-Heart and Circulatory Physiology, volume 320, issue 2, pp. H494 - H510
(Article)
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
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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.
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Keywords: Blood flow models, Central blood pressure, Magnetic resonance imaging, Ultrasound, Virtual subjects, Physiology, Cardiology and Cardiovascular Medicine, Physiology (medical)
ISSN: 0363-6135
Publisher: American Physiological Society
Note: Funding Information: This work was supported by a PhD Fellowship awarded by the King’s College London and Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging [EP/L015226/1], the British Heart Foundation (BHF) [PG/15/104/31913], and the Wellcome EPSRC Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z]. The authors acknowledge financial support from the Department of Health through the National Institute for Health Research (NIHR) Cardiovascular MedTech Co-operative at Guy’s and St Thomas’ NHS Foundation Trust (GSTT). The views expressed are those of the authors and not necessarily those of the EPSRC, BHF, Wellcome Trust, NIHR, or GSTT. Publisher Copyright: Copyright © 2021 the American Physiological Society
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