FDA Benchmark Medical Device Flow Models for CFD Validation.
01 Mar 2017-Asaio Journal (ASAIO journal (American Society for Artificial Internal Organs : 1992))-Vol. 63, Iss: 2, pp 150-160
TL;DR: The primary goal of this article is to summarize the FDA initiative and to report recent findings from the benchmark blood pump model study, which aided the development of an FDA Guidance Document on factors to consider when reporting computational studies in medical device regulatory submissions.
Abstract: Computational fluid dynamics (CFD) is increasingly being used to develop blood-contacting medical devices. However, the lack of standardized methods for validating CFD simulations and blood damage predictions limits its use in the safety evaluation of devices. Through a U.S. Food and Drug Administration (FDA) initiative, two benchmark models of typical device flow geometries (nozzle and centrifugal blood pump) were tested in multiple laboratories to provide experimental velocities, pressures, and hemolysis data to support CFD validation. In addition, computational simulations were performed by more than 20 independent groups to assess current CFD techniques. The primary goal of this article is to summarize the FDA initiative and to report recent findings from the benchmark blood pump model study. Discrepancies between CFD predicted velocities and those measured using particle image velocimetry most often occurred in regions of flow separation (e.g., downstream of the nozzle throat, and in the pump exit diffuser). For the six pump test conditions, 57% of the CFD predictions of pressure head were within one standard deviation of the mean measured values. Notably, only 37% of all CFD submissions contained hemolysis predictions. This project aided in the development of an FDA Guidance Document on factors to consider when reporting computational studies in medical device regulatory submissions. There is an accompanying podcast available for this article. Please visit the journal's Web site (www.asaiojournal.com) to listen.
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TL;DR: The role of computational modeling for medical devices is introduced, OSEL's ongoing research is described, and how evidence from computational modeling has been used in regulatory submissions by industry to CDRH in recent years is overviewed.
Abstract: Protecting and promoting public health is the mission of the U.S. Food and Drug Administration (FDA). FDA's Center for Devices and Radiological Health (CDRH), which regulates medical devices marketed in the U.S., envisions itself as the world's leader in medical device innovation and regulatory science-the development of new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical devices. Traditionally, bench testing, animal studies, and clinical trials have been the main sources of evidence for getting medical devices on the market in the U.S. In recent years, however, computational modeling has become an increasingly powerful tool for evaluating medical devices, complementing bench, animal and clinical methods. Moreover, computational modeling methods are increasingly being used within software platforms, serving as clinical decision support tools, and are being embedded in medical devices. Because of its reach and huge potential, computational modeling has been identified as a priority by CDRH, and indeed by FDA's leadership. Therefore, the Office of Science and Engineering Laboratories (OSEL)-the research arm of CDRH-has committed significant resources to transforming computational modeling from a valuable scientific tool to a valuable regulatory tool, and developing mechanisms to rely more on digital evidence in place of other evidence. This article introduces the role of computational modeling for medical devices, describes OSEL's ongoing research, and overviews how evidence from computational modeling (i.e., digital evidence) has been used in regulatory submissions by industry to CDRH in recent years. It concludes by discussing the potential future role for computational modeling and digital evidence in medical devices.
90 citations
Cites background or methods from "FDA Benchmark Medical Device Flow M..."
...Other computational tools to assess specific aspects of device performance or safety that industry can employ include a simulator for high-intensity focused ultrasound (HIFU) beams and heating effects (51, 52), benchmarks models for computational fluid dynamics (19), patient-specific workflows for assessing clot trapping efficiency in IVC filters (53), surrogate models for predicting device-specific and speciesspecific hemolysis (Craven et al....
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...These models include implantable cardiovascular stents for assessing different methods to calculate fatigue safety factor (12); heart valves implanted with non-circular configurations (13) to assess the impact on stresses and strains; inferior vena cava filters to demonstrate a new method for computing embolus transport (14); hip implants for evaluating the impact of the design on contact mechanics (15); radiofrequency coils for MRI systems (16, 17) to investigate the design parameters on the electromagnetic field; surgical facemasks (18) for evaluating aerosol leakage of different designs; blood pump (19) for assessing the ability to predict hemolysis using computational fluid dynamics; and electrical stimulation of implanted lead wires (20) to investigate local heating....
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TL;DR: Key components of patient-specific CFD are covered briefly which include image segmentation, geometry reconstruction, mesh generation, fluid-structure interaction, and solver techniques.
Abstract: The emergence of new cardiac diagnostics and therapeutics of the heart has given rise to the challenging field of virtual design and testing of technologies in a patient-specific environment. Given the recent advances in medical imaging, computational power and mathematical algorithms, patient-specific cardiac models can be produced from cardiac images faster, and more efficiently than ever before. The emergence of patient-specific computational fluid dynamics (CFD) has paved the way for the new field of computer-aided diagnostics. This article provides a review of CFD methods, challenges and opportunities in coronary and intra-cardiac flow simulations. It includes a review of market products and clinical trials. Key components of patient-specific CFD are covered briefly which include image segmentation, geometry reconstruction, mesh generation, fluid-structure interaction, and solver techniques.
66 citations
Additional excerpts
...in the regulatory approval process (Malinauskas et al., 2017)....
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TL;DR: An overview of the ASME V&V 40 standard and an example of the framework applied to a generic centrifugal blood pump are presented, emphasizing how experimental evidence from in vitro testing can support computational modeling for device evaluation.
Abstract: Medical device manufacturers using computational modeling to support their device designs have traditionally been guided by internally developed modeling best practices. A lack of consensus on the evidentiary bar for model validation has hindered broader acceptance, particularly in regulatory areas. This has motivated the US Food and Drug Administration and the American Society of Mechanical Engineers (ASME), in partnership with medical device companies and software providers, to develop a structured approach for establishing the credibility of computational models for a specific use. Charged with this mission, the ASME VV the main tenet of the framework is that the credibility requirements of a computational model should be commensurate with the risk associated with model use. This article provides an overview of the ASME V&V 40 standard and an example of the framework applied to a generic centrifugal blood pump, emphasizing how experimental evidence from in vitro testing can support computational modeling for device evaluation. Two different contexts of use for the same model are presented, which illustrate how model risk impacts the requirements on the V&V activities and outcomes.
34 citations
TL;DR: Experimental datasets from the inter-laboratory characterization of benchmark flow models, including the blood pump model presented herein and the previous nozzle model, can be used for validating future CFD studies and to collaboratively develop guidelines on best practices for verification, validation, uncertainty quantification, and credibility assessment of CFD simulations in the evaluation of medical devices.
Abstract: A credible computational fluid dynamics (CFD) model can play a meaningful role in evaluating the safety and performance of medical devices. A key step towards establishing model credibility is to first validate CFD models with benchmark experimental datasets to minimize model-form errors before applying the credibility assessment process to more complex medical devices. However, validation studies to establish benchmark datasets can be cost prohibitive and difficult to perform. The goal of this initiative sponsored by the U.S. Food and Drug Administration is to generate validation data for a simplified centrifugal pump that mimics blood flow characteristics commonly observed in ventricular assist devices. The centrifugal blood pump model was made from clear acrylic and included an impeller, with four equally spaced, straight blades, supported by mechanical bearings. Particle Image Velocimetry (PIV) measurements were performed at several locations throughout the pump by three independent laboratories. A standard protocol was developed for the experiments to ensure that the flow conditions were comparable and to minimize systematic errors during PIV image acquisition and processing. Velocity fields were extracted at the pump entrance, blade passage area, back gap region, and at the outlet diffuser regions. A Newtonian blood analog fluid composed of sodium iodide, glycerin, and water was used as the working fluid. Velocity measurements were made for six different pump flow conditions, with the blood-equivalent flow rate ranging between 2.5 and 7 L/min for pump speeds of 2500 and 3500 rpm. Mean intra- and inter-laboratory variabilities in velocity were ~ 10% at the majority of the measurement locations inside the pump. However, the inter-laboratory variability increased to more than ~ 30% in the exit diffuser region. The variability between the three laboratories for the peak velocity magnitude in the diffuser region ranged from 5 to 25%. The bulk velocity field near the impeller changed proportionally with the rotational speed but was relatively unaffected by the pump flow rate. In contrast, flow in the exit diffuser region was sensitive to both the flow rate and the rotational speed. Specifically, at 3500 rpm, the exit jet tilted toward the inner wall of the diffuser at a flow rate of 2.5 L/min, but the jet tilted towards the outer wall when the flow rate was 7 L/min. Inter-laboratory experimental mean velocity data (and the corresponding variance) were obtained for the FDA pump model and are available for download at https://nciphub.org/wiki/FDA_CFD
. Experimental datasets from the inter-laboratory characterization of benchmark flow models, including the blood pump model presented herein and our previous nozzle model, can be used for validating future CFD studies and to collaboratively develop guidelines on best practices for verification, validation, uncertainty quantification, and credibility assessment of CFD simulations in the evaluation of medical devices (e.g. ASME V&V 40 standards working group).
33 citations
TL;DR: This work demonstrates that, in a well‐controlled environment, both PC‐MRI and CFD might bring reliable and correlated flow quantities when a proper methodology to reduce the errors is followed.
Abstract: Several well-resolved 4D Flow MRI acquisitions of an idealized rigid flow phantom featuring an aneurysm, a curved channel as well as a bifurcation were performed under pulsatile regime. The resulting hemodynamics were processed to remove MRI artifacts. Subsequently, they were compared with CFD predictions computed on the same flow domain, using an in-house high-order low dissipative flow solver. Results show that reaching a good agreement is not straightforward but requires proper treatments of both techniques. Several sources of discrepancies are highlighted and their impact on the final correlation evaluated. While a very poor correlation ($r^2$ = 0.63) is found in the entire domain between raw MRI and CFD data, correlation as high as $r^2$ = 0.97 is found when artifacts are removed by post-processing the MR data and down sampling the CFD results to match the MRI spatial and temporal resolutions. This work demonstrates that, in a well-controlled environment, both PC-MRI and CFD might bring reliable and correlated flow quantities when a proper methodology to reduce the errors is followed.
28 citations
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TL;DR: Improved understanding of the relationships between shear stress, exposure time, and blood damage and the development of numerical models for the different types of blood damage are proposed to enable the design of improved VADs.
Abstract: Ventricular assist devices (VADs) have already helped many patients with heart failure but have the potential to assist more patients if current problems with blood damage (hemolysis, platelet activation, thrombosis and emboli, and destruction of the von Willebrand factor (vWf)) can be eliminated. A step towards this goal is better understanding of the relationships between shear stress, exposure time, and blood damage and, from there, the development of numerical models for the different types of blood damage to enable the design of improved VADs. In this study, computational fluid dynamics (CFD) was used to calculate the hemodynamics in three clinical VADs and two investigational VADs and the shear stress, residence time, and hemolysis were investigated. A new scalar transport model for hemolysis was developed. The results were compared with in vitro measurements of the pressure head in each VAD and the hemolysis index in two VADs. A comparative analysis of the blood damage related fluid dynamic parameters and hemolysis index was performed among the VADs. Compared to the centrifugal VADs, the axial VADs had: higher mean scalar shear stress (sss); a wider range of sss, with larger maxima and larger percentage volumes at both low and high sss; and longer residence times at very high sss. The hemolysis predictions were in agreement with the experiments and showed that the axial VADs had a higher hemolysis index. The increased hemolysis in axial VADs compared to centrifugal VADs is a direct result of their higher shear stresses and longer residence times. Since platelet activation and destruction of the vWf also require high shear stresses, the flow conditions inside axial VADs are likely to result in more of these types of blood damage compared with centrifugal VADs.
272 citations
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238 citations