Global Sensitivity Analysis For Clinically Validated 1D Models of Fractional Flow Reserve

Tanade, Cyrus; Feiger, Bradley; Vardhan, Madhurima; Chen, S. James; Leopold, Jane A.; Randles, Amanda

doi:10.1109/embc46164.2021.9629890

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…We considered stenosis anatomy separately from coronary geometry. Explicit pressure drop terms were required to accurately evaluate ischemic burden, and these terms were parameterized via stenosis geometry: stenosis length and stenosis radius at the minimum luminal diameter ( 30 , 31 ). Cardiac output was used to parameterize the inlet flow rate waveform and peripheral resistance at the outlets.…”

Section: Methodsmentioning

confidence: 99%

“…To prevent diluting the parameter space with insignificant parameters, the raw inputs were narrowed to mean arterial pressure, cardiac output, stenosis degree, and distal location based on what has been shown to be significant from prior works ( 27 , 31 ). As inlet flow rate was found to be insignificant in the literature ( 27 , 31 , 52 ), we only evaluated cardiac output as it pertained to peripheral resistance and relegated cardiac output as it pertained to inlet flow rate as patient-generalized. The importance of distal location has been discussed in ( 53 ) and was also considered potentially significant.…”

Section: Methodsmentioning

confidence: 99%

“…One parameter that could be patient-generalized from the get-go was inlet flow rate waveform. As prior studies ( 27 , 31 , 52 ) demonstrated that the inlet flow rate waveform contributed minimally to FFR, two canonical hyperemic waveforms were created ( Figure 2 ), one for the left coronary artery (LCA) and one for the right coronary artery (RCA).…”

Section: Methodsmentioning

confidence: 99%

“…As a secondary endpoint to this work, we evaluated whether Sobol indices varied across patients. Previous works have conventionally aggregated the Sobol indices over all patients in the cohort, but the relative importance of each input parameter to FFR may not generalize across differing patient anatomy and physiology ( 11 , 27 , 31 ). To this end, we performed a global repeated measures analysis of variance (ANOVA), where anatomic and hemodynamic variations subdivided the Sobol indices.…”

Section: Methodsmentioning

confidence: 99%

“…When patient-tuned values do not drastically deviate from patient averages, patient-generalized inputs could result in the same FFR calculation. As the focus has been on identifying sensitive inputs, it is unknown which parameters are insensitive to FFR and could be relegated to patient averages, or patient-generalized parameters, without sacrificing diagnostic performance ( 31 ). We hypothesized that on top of accurately segmenting the overall coronary tree, an accurate model with minimized inputs would require prescribing flow distribution parameters and capturing the geometric severity of stenoses on a per-patient level.…”

Section: Introductionmentioning

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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics

Schäfer,

Schiavazzi,

Hellevik

et al. 2024

Numer Methods Biomed Eng

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Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample‐based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol’ sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three‐dimensional fluid–structure interaction simulations with affordable one‐ and zero‐dimensional reduced‐order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi‐ (1D/0D) and tri‐fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single‐fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol’ indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response.

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HarVI: Real-Time Intervention Planning for Coronary Artery Disease Using Machine Learning

Tanade,

Randles

2024

Lecture Notes in Computer Science

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Global Sensitivity Analysis For Clinically Validated 1D Models of Fractional Flow Reserve

Cited by 6 publications

References 16 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

Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics

HarVI: Real-Time Intervention Planning for Coronary Artery Disease Using Machine Learning

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