Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes

Abstract

The arterial pulse wave (PW) is a rich source of information on cardiovascular (CV) health. It is widely measured by both consumer and clinical devices. However, the physical determinants of the PW are not yet fully understood, and the development of PW analysis algorithms is limited by a lack of PW data sets containing reference CV measurements. Our aim was to create a database of PWs simulated by a computer to span a range of CV conditions, representative of a sample of healthy adults. The typical CV properties of 25–75 yr olds were identified through a literature review. These were used as inputs to a computational model to simulate PWs for subjects of each age decade. Pressure, flow velocity, luminal area, and photoplethysmographic PWs were simulated at common measurement sites, and PW indexes were extracted. The database, containing PWs from 4,374 virtual subjects, was verified by comparing the simulated PWs and derived indexes with corresponding in vivo data. Good agreement was observed, with well-reproduced age-related changes in hemodynamic parameters and PW morphology. The utility of the database was demonstrated through case studies providing novel hemodynamic insights, in silico assessment of PW algorithms, and pilot data to inform the design of clinical PW algorithm assessments. In conclusion, the publicly available PW database is a valuable resource for understanding CV determinants of PWs and for the development and preclinical assessment of PW analysis algorithms. It is particularly useful because the exact CV properties that generated each PW are known.NEW & NOTEWORTHY First, a comprehensive literature review of changes in cardiovascular properties with age was performed. Second, an approach for simulating pulse waves (PWs) at different ages was designed and verified against in vivo data. Third, a PW database was created, and its utility was illustrated through three case studies investigating the determinants of PW indexes. Fourth, the database and tools for creating the database, analyzing PWs, and replicating the case studies are freely available.

Estimating Central Pulse Pressure From Blood Flow by Identifying the Main Physical Determinants of Pulse Pressure Amplification

“…Pulse waves were simulated for subjects of each age decade, from 25 to 75 years old, using a 116-artery, one-dimensional (1-D) model of blood flow in the larger systemic arteries of the thorax, limbs, and head ( Figure 2A ). The model cardiovascular properties were identified through a comprehensive literature review and simulated pulse waves were verified by comparison against clinical data [see Charlton et al ( 2019 ) for full details].…”

“… Schematic representation of the three models used in the study. (A) Full 116-artery model with cardiovascular properties taken from Charlton et al ( 2019 ). (B) Reduced 15-artery model containing the aortic-brachial arterial path of the full model.…”

“…This model hereafter referred to as the “reduced model”—has the same inflow waveform prescribed at the aortic root as the full model. Such inflow waveform was obtained by the AorticFlowWave script described in Charlton et al ( 2019 ) for the desired inflow characteristics (heart rate, stroke volume and left ventricular ejection time). Figure 3A shows the inflow waveform for the 25 year-old baseline subject.…”

“…Large ΔPP values have been associated with male sex (Segers et al, 2009 ; Herbert et al, 2014 ), higher heart rate (Wilkinson et al, 2002 ), height (Asmar et al, 1997 ; Camacho et al, 2004 ), mass index (Pichler et al, 2016 ), pulse transit time (Gao et al, 2016 ; Natarajan et al, 2017 ), and wave reflection coefficient (Gao et al, 2016 ), and lower age (Wilkinson et al, 2001 ; Herbert et al, 2014 ) and pulse wave velocity (Hashimoto and Ito, 2010 ; Pierce et al, 2012 ), and is significantly influenced by cardiovascular risk factors, such as hypertension and obesity (Herbert et al, 2014 ). Experimental and computational models have been used to study the effect on ΔPP of cardiovascular properties (Karamanoglu et al, 1995 ; Figueroa and Humphrey, 2014 ; Mynard and Smolich, 2015 ; Gaddum et al, 2017 ) and age (Charlton et al, 2019 ), showing that ΔPP raises with increasing ventricular inotropy (contractile state of the ventricle), tapering, peripheral load and vessel length; and decreasing wall thickness and age. However, there are currently no methods based on the physics of blood flow in the systemic arterial tree that enable explicit analytical identification of the main cardiovascular determinants of ΔPP—and hence estimation of cPP from peripheral PP (pPP)—from data that can be acquired non-invasively for a specific subject.…”

Evaluación No-Invasiva de la Dinámica Cardiovascular a Utilizando Herramientas de Aprendizaje Estadístico

“…Virtual DB'S Se accedió a dos BD simuladas públicas de sujetos sanos las cuales constan de señales de presión, flujo y área de distintos segmentos arteriales. En la primera (Willemet et al, 2015) se encuentran 3325 muestras sintéticas, mientas que en la segunda (Charlton et al, 2019) hay 3837. El objetivo del uso de estas BD es el de poder testear en un marco controlado la respuesta de los modelos de aprendizaje profundo.…”