Multiscale modeling of blood flow to assess neurological complications in patients supported by venoarterial extracorporeal membrane oxygenation

Feiger, Bradley; Adebiyi, Adebayo; Randles, Amanda

doi:10.1016/j.compbiomed.2020.104155

Cited by 15 publications

(11 citation statements)

References 49 publications

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“…We used a 1D blood flow model described in ( 36 , 37 ). The 1D blood flow simulator was based on the following governing equations:

0

where

is vessel cross-sectional area,

is flow rate,

is pressure,

is density of blood,

describes the velocity profile, and

22

is a frictional term.…”

Section: Methodsmentioning

confidence: 99%

“…We used a 1D blood flow model described in (36,37). The 1D blood flow simulator was based on the following governing equations:…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…where A is vessel cross-sectional area, Q is flow rate, P is pressure, r is density of blood, a describes the velocity profile, and C f ¼ 22pm is a frictional term. a and C f were estimated from experimental data that have been used in 1D models (36)(37)(38)(39). P and A are related by the following constitutive equation:…”

Section: Mathematical Formulationmentioning

confidence: 99%

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Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve

Tanade

Chen²,

Leopold³

et al. 2022

Front. Med. Technol.

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BackgroundPersonalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFRbaseline), but obtaining patient-specific data could be challenging and sometimes not feasible. Understanding which measurements need to be patient-tuned vs. patient-generalized would inform models with minimal inputs that could expedite data collection and simulation pipelines.AimsTo determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFRinvasive) as gold standard.Materials and MethodsPersonalized coronary geometries (N=50) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFRbaseline, was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFRbaseline was validated against FFRinvasive and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFRstreamlined was created by only incorporating the most sensitive inputs and FFRsemi-streamlined additionally included patient-specific distal location.ResultsFFRbaseline was validated against FFRinvasive via correlation (r=0.714, p<0.001), agreement (mean difference: 0.01±0.09), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFRsemi-streamlined provided identical diagnostic performance with FFRbaseline. Compared to FFRbaseline vs. FFRinvasive, FFRstreamlined vs. FFRinvasive had decreased correlation (r=0.64, p<0.001), improved agreement (mean difference: 0.01±0.08), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90).ConclusionStreamlined models could match the diagnostic performance of the baseline with a full gamut of patient-specific measurements. Capturing coronary hemodynamics depended most on accurate geometry reconstruction and cardiac output measurement.

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“…We used a 1D blood flow model described in ( 36 , 37 ). The 1D blood flow simulator was based on the following governing equations:

0

where

is vessel cross-sectional area,

is flow rate,

is pressure,

is density of blood,

describes the velocity profile, and

22

is a frictional term.…”

Section: Methodsmentioning

confidence: 99%

“…We used a 1D blood flow model described in (36,37). The 1D blood flow simulator was based on the following governing equations:…”

Section: Mathematical Formulationmentioning

confidence: 99%

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Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve

Tanade

Chen²,

Leopold³

et al. 2022

Front. Med. Technol.

Self Cite

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show abstract

“…Implementation of this can be time consuming in terms of both computational development and implementation as well as execution time. For this reason, a number of studies will fall back on a rigid wall assumption due to its simplicity [4,5,6]. In Table 1 we compare the average time taken to complete 1000 iterations for our elastic wall implementation and the same domain and core configuration using the rigid wall assumption.…”

Section: Model Verificationmentioning

confidence: 99%

“…Depending on the fluid solver used, the changing wall position may demand the fluid domain to be modified in response. Both of these procedures can be complex and computationally costly to conduct in 3D and may be a reason as to why a number of 3D models utilise a rigid wall assumption [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

An efficient, localised approach for the simulation of elastic blood vessels using the lattice Boltzmann method

McCullough¹,

Coveney²

2021

Preprint

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Many numerical studies of blood flow impose a rigid wall assumption due to the simplicity of its implementation compared to a full coupling to a solid mechanics model. In this paper, we present a localised method for incorporating the effects of elastic walls into blood flow simulations using the lattice Boltzmann method. We demonstrate that our approach is able to more accurately capture the flow behaviour expected in an elastic walled vessel than a rigid wall model and achieves this without a loss of computational performance. We also demonstrate that our approach can capture trends in wall shear stress distribution captured by fully coupled models in personalised vascular geometries.

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Velocity Temporal Shape Affects Simulated Flow in Left Coronary Arteries

Geddes,

Tanade,

Ladd

et al. 2024

Lecture Notes in Computer Science

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Multiscale modeling of blood flow to assess neurological complications in patients supported by venoarterial extracorporeal membrane oxygenation

Cited by 15 publications

References 49 publications

Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve

Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve

An efficient, localised approach for the simulation of elastic blood vessels using the lattice Boltzmann method

Velocity Temporal Shape Affects Simulated Flow in Left Coronary Arteries

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