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Mariscal-Harana J, Charlton PH, Vennin S, Aramburu J, Florkow MC, van Engelen A, Schneider T, de Bliek H, Ruijsink B, Valverde I, Beerbaum P, Grotenhuis H, Charakida M, Chowienczyk P, Sherwin SJ, Alastruey J. Estimating central blood pressure from aortic flow: development and assessment of algorithms. Am J Physiol Heart Circ Physiol 2020; 320:H494-H510. [PMID: 33064563 PMCID: PMC7612539 DOI: 10.1152/ajpheart.00241.2020] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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.
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Affiliation(s)
- Jorge Mariscal-Harana
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Peter H Charlton
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Samuel Vennin
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.,Department of Clinical Pharmacology, King's College London, King's Health Partners, London , United Kingdom
| | - Jorge Aramburu
- TECNUN Escuela de Ingenieros, Universidad de Navarra, Donostia-San Sebastián, Spain
| | - Mateusz Cezary Florkow
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.,Philips Research, Cambridge, United Kingdom
| | - Arna van Engelen
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Torben Schneider
- Philips Healthcare UK, Philips Centre, Guildford Business Park, Guildford, Surrey, United Kingdom
| | - Hubrecht de Bliek
- HSDP Clinical Platforms, Philips Healthcare, Eindhoven, The Netherlands
| | - Bram Ruijsink
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.,Department of Cardiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Israel Valverde
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.,Cardiovascular Pathophysiology, Institute of Biomedicine of Seville, University Hospital of Virgen del Rocío, University of Seville, CIBERCV, CSIC, Seville, Spain
| | - Philipp Beerbaum
- Department of Pediatric Cardiology and Intensive Care, Hannover Medical School, Hannover, Germany
| | - Heynric Grotenhuis
- Department of Pediatric Cardiology, University Medical Center Utrecht/Wilhelmina Children's Hospital, Utrecht, The Netherlands
| | - Marietta Charakida
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom
| | - Phil Chowienczyk
- Department of Clinical Pharmacology, King's College London, King's Health Partners, London , United Kingdom
| | - Spencer J Sherwin
- Department of Aeronautics, South Kensington Campus, Imperial College London, London, United Kingdom
| | - Jordi Alastruey
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.,Institute of Personalized Medicine, Sechenov University, Moscow, Russia
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Abstract
Aortic disease is routinely monitored with anatomic imaging, but until the recent advent of 3-directional phase contrast MRI (4D) flow, blood flow abnormalities have gone undetected. 4D flow measures aortic hemodynamic markers quickly. Qualitative flow visualization has spurred the investigation of new quantitative markers. Flow displacement and wall shear stress can quantify the effects of valve-related aortic flow abnormalities. Markers of turbulent and viscous energy loss approximate the increased energetic burden on the ventricle in disease states. This article discusses magnetic resonance flow imaging and highlights new flow-related markers in the context of aortic valve disease, valve-related aortic disease, and aortic wall disease.
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Affiliation(s)
- Nicholas S Burris
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Avenue, Box 0628, San Francisco, CA 94143-0628, USA
| | - Michael D Hope
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Avenue, Box 0628, San Francisco, CA 94143-0628, USA.
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