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.
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging
- Safety Research
- Cardiology and Cardiovascular Medicine
- Advanced and Specialized Nursing