OBJECTIVE: Blood flow waveforms-essential data for hemodynamic modeling-are often in practice unavailable to researchers. The objectives of this work were to assess the variability among the waveforms for a clinically relevant older population, and develop data-based methods for addressing the missing waveform data for hemodynamic studies. APPROACH: We analyzed 272 flow waveforms from the internal carotid arteries of older patients (73 ± 13 yr) with moderate cardiovascular disease, and used these data to develop methods to guide new approaches for hemodynamic studies. MAIN RESULTS: Profound variations in waveform parameters were found within the aged population that were not seen in published data for young subjects. Common features in the aged population relative to the young included a larger systole-to-diastole flow rate ratio, increased flow during late systole, and absence of a dicrotic notch. Eight waveforms were identified that collectively represent the range of waveforms in the older population. A relationship between waveform shape and flow rate was obtained that, in conjunction with equations relating flow rate to diameter, can be used to provide individualized waveforms for patient-specific geometries. The dependence of flow rate on diameter was statistically different between male and female patients. SIGNIFICANCE: It was shown that a single archetypal waveform cannot well-represent the diverse waveforms found within an aged population, although this approach is frequently used in studies of flow in the cerebral vasculature. Motivated by these results, we provided a set of eight waveforms that can be used to assess the hemodynamic uncertainty associated with the lack of patient-specific waveform data. We also provided a methodology for generating individualized waveforms when patient gender, age, and cardiovascular disease state are known. These data-driven approaches can be used to devise more relevant in vitro or in silico intra-cranial hemodynamic studies for older patients.
OBJECTIVE: Blood flow waveforms-essential data for hemodynamic modeling-are often in practice unavailable to researchers. The objectives of this work were to assess the variability among the waveforms for a clinically relevant older population, and develop data-based methods for addressing the missing waveform data for hemodynamic studies. APPROACH: We analyzed 272 flow waveforms from the internal carotid arteries of older patients (73 ± 13 yr) with moderate cardiovascular disease, and used these data to develop methods to guide new approaches for hemodynamic studies. MAIN RESULTS: Profound variations in waveform parameters were found within the aged population that were not seen in published data for young subjects. Common features in the aged population relative to the young included a larger systole-to-diastole flow rate ratio, increased flow during late systole, and absence of a dicrotic notch. Eight waveforms were identified that collectively represent the range of waveforms in the older population. A relationship between waveform shape and flow rate was obtained that, in conjunction with equations relating flow rate to diameter, can be used to provide individualized waveforms for patient-specific geometries. The dependence of flow rate on diameter was statistically different between male and female patients. SIGNIFICANCE: It was shown that a single archetypal waveform cannot well-represent the diverse waveforms found within an aged population, although this approach is frequently used in studies of flow in the cerebral vasculature. Motivated by these results, we provided a set of eight waveforms that can be used to assess the hemodynamic uncertainty associated with the lack of patient-specific waveform data. We also provided a methodology for generating individualized waveforms when patient gender, age, and cardiovascular disease state are known. These data-driven approaches can be used to devise more relevant in vitro or in silico intra-cranial hemodynamic studies for older patients.
Authors: Yiemeng Hoi; Bruce A Wasserman; Yuanyuan J Xie; Samer S Najjar; Luigi Ferruci; Edward G Lakatta; Gary Gerstenblith; David A Steinman Journal: Physiol Meas Date: 2010-01-20 Impact factor: 2.833
Authors: S Hadad; F Mut; B J Chung; J A Roa; A M Robertson; D M Hasan; E A Samaniego; J R Cebral Journal: AJNR Am J Neuroradiol Date: 2020-12-24 Impact factor: 3.825