Computed tomography angiography as an adjunct to computational fluid dynamics for prediction of oxygenator thrombus formation

Abstract

Introduction: Extracorporeal membrane oxygenation circuit performance can be compromised by oxygenator thrombosis. Stagnant blood flow in the oxygenator can increase the risk of thrombus formation. To minimize thrombogenic potential, computational fluid dynamics is frequently applied for identification of stagnant flow conditions. We investigate the use of computed tomography angiography to identify flow patterns associated with thrombus formation. Methods: A computed tomography angiography was performed on a Quadrox D oxygenator, and video densitometric parameters associated with flow stagnation were measured from the acquired videos. Computational fluid dynamics analysis of the same oxygenator was performed to establish computational fluid dynamics–based flow characteristics. Forty-one Quadrox D oxygenators were sectioned following completion of clinical use. Section images were analyzed with software to determine oxygenator clot burden. Linear regression was used to correlate clot burden to computed tomography angiography and computational fluid dynamics–based flow characteristics. Results: Clot burden from the explanted oxygenators demonstrated a well-defined pattern, with the largest clot burden at the corner opposite the blood inlet and outlet. The regression model predicted clot burden by region of interest as a function of time to first opacification on computed tomography angiography (R2 = 0.55). The explanted oxygenator clot burden map agreed well with the computed tomography angiography predicted clot burden map. The computational fluid dynamics parameter of residence time, when summed in the Z-direction, was partially predictive of clot burden (R2 = 0.35). Conclusion: In the studied oxygenator, clot burden follows a pattern consistent with clinical observations. Computed tomography angiography–based flow analysis provides a useful adjunct to computational fluid dynamics–based flow analysis in understanding oxygenator thrombus formation.

A fibrin enhanced thrombosis model for medical devices operating at low shear regimes or large surface areas

“…Computational fluid dynamics (CFD) simulations of blood oxygenators have been used to identify regions prone to thrombogenesis [ 57 , 58 ]. For example, Conway et al [ 58 ] reported that a cumulative residence time along the flow axis computed from CFD simulations was partially predictive of clot burden.…”

“…Computational fluid dynamics (CFD) simulations of blood oxygenators have been used to identify regions prone to thrombogenesis [ 57 , 58 ]. For example, Conway et al [ 58 ] reported that a cumulative residence time along the flow axis computed from CFD simulations was partially predictive of clot burden. In a similar manner, Gartner et al [ 57 ] used 2D CFD simulations within blood oxygenators to minimize regions of low blood velocity regions suspected of producing potential thrombosis.…”

Design of artificial vascular devices: Hemodynamic evaluation of shear-induced thrombogenicity

“…Current medical devices have diverse blood flow paths, and operate scale reduction and constriction to facilitate their operations. Classical ECMO circuits commonly show a set of very packed fibres where blood flow is repeatedly constricted and expanded from the inlet to the outlet to enhance the oxygenation rate (Fukuda et al, 2020;Conway et al, 2021). Similarly, centrifugal pumps integrate sharp impellers and blades in order to rapidly accelerate the blood (Figure 3) (Fox et al, 2022).…”

Introducing 3D-potting: a novel production process for artificial membrane lungs with superior blood flow design

“…Thrombus formation in combination with protein adsorption is the most frequent clinical complication and can lead to decreased gas transfer performance of the ML, increased embolic risk to the patient, or even mechanical failure of the device that is essential to the patient's survival [2,3]. Thrombosis in MLs is typically associated with flow stagnation [4][5][6], unsteady cross-sectional changes [7], nonhomogeneous flow distribution and, especially, shut-off flow regimes such as corners [8]. These non-ideal flow scenarios, which lead to abnormal physiological blood reactions and clotting of the ALs, are the result of design limitations created by the manufacturing process-the so-called potting process.…”

“…Clots are often found at the cross-sectional expansion in the inlet region [11][12][13]. Additionally, the corners have proven to be particularly prone to clotting due to an uneven flow distribution resulting in low flow regimes in the corners [8,11]. The corners can be eliminated, for (Eurosets, S.r.I., Medolla, Italy)] and a stacked, cuboidal-shaped ML (right) [e.g., iLA membrane ventilator (Xenios AG, Heilbronn, Germany) or Quadrox-i (Maquet GmbH, Rastatt, Germany)].…”