Sherwin, S. J. (Spencer J.)

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    Estimating central blood pressure from aortic flow: development and assessment of algorithms.
    (AMER. PHYSIOLOGICAL SOC., 2021) Mariscal-Harana, J. (Jorge); Charlton, P.H. (Peter H.); Vennin, S. (Samuel); Aramburu-Montenegro, J. (Jorge); Florkow, M.C. (Mateusz Cezary); Schneider, T. (Torben); De Bliek, H. (Hubrecht); Ruijsink, B. (Bram); Valverde, I. (Israel); Beerbaum, P. (Philipp); Grotenhuis, H. (Heynric); Charakida, M. (Marietta); Chowienczyk, P. (Phil); Sherwin, S. J. (Spencer J.); Alastruey, J. (Jordi)
    Central blood pressure (cBP) is a highly prognostic cardiovascular (CV) risk factor whose accurate, invasive assessment iscostly and carries risks to patients. We developed and assessed novel algorithms for estimating cBP from noninvasive aortichemodynamic data and a peripheral blood pressure measurement. These algorithms were created using three blood flowmodels: the two- and three-element Windkessel (0-D) models and a one-dimensional (1-D) model of the thoracic aorta. Wetested new and existing methods for estimating CV parameters (left ventricular ejection time, outflow BP, arterial resistanceand compliance, pulse wave velocity, and characteristic impedance) required for the cBP algorithms, using virtual (simu-lated) subjects (n = 19,646) for which reference CV parameters were known exactly. We then tested the cBP algorithmsusing virtual subjects (n = 4,064), for which reference cBP were available free of measurement error, and clinical datasetscontaining invasive (n = 10) and noninvasive (n = 171) reference cBP waves across a wide range of CV conditions. The 1-Dalgorithm outperformed the 0-D algorithms when the aortic vascular geometry was available, achieving central systolicblood pressure (cSBP) errors 2.1 ± 9.7 mmHg and root-mean-square errors (RMSEs) 6.4 ± 2.8 mmHg against invasive refer-ence cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBPerrors 6.0 ± 4.7 mmHg and RMSEs 5.9 ± 2.4 mmHg against noninvasive reference cBP waves (n = 171), outperforming thetwo-element 0-D algorithm. All CV parameters were estimated with mean percentage errors 8.2%, except for the aorticcharacteristic impedance ( 13.4%), which affected the three-element 0-D algorithm’s performance. The freely available algo-rithms 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.