Complementary Role of the Computed Biomodelling through Finite Element Analysis and Computed Tomography for Diagnosis of Transcatheter Heart Valve Thrombosis

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Abstract

Introduction. The TAVR procedure is associated with a substantial risk of thrombosis. Current guidelines recommend catheter-based aortic valve implantation for prohibitive-high-risk patients with severe aortic valve stenosis but acknowledge that the aetiology and mechanism of thrombosis are unclear. Methods. From 2015 to 2018, 607 patients with severe aortic valve stenosis underwent either self-expandable or balloon-expandable catheter-based aortic valve implantation at our institute. A complementary study was designed to support computed tomography as a predictor of complications using an advanced biomodelling process through finite element analysis (FEA). The primary evaluation of study was the thrombosis of the valve at 12 months. Results. At 12 months, 546 patients had normal valvular function. 61 patients had THVT while 6 showed thrombosis and dislodgement with deterioration to NYHA Class IV requiring rehospitalization. The FEA biomodelling revealed a strong link between solid uncrushed calcifications, delayed dislodgement of TAVR and late thrombosis. We observed an interesting phenomenon of fibrosis/calcification originating at the level of the misplaced valve, which was the primary cause of coronary obstruction. Conclusion. The use of cardiac CT and predictive biomodelling should be integrated into routine practice for the selection of TAVR candidates and as a predictor of negative outcomes given the lack of accurate investigations available. This would assist in effective decision-making and diagnosis especially in a high-risk cohort of patients.

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Nappi, F., Mazzocchi, L., Avtaar Singh, S. S., Morganti, S., Sablayrolles, J. L., Acar, C., & Auricchio, F. (2018). Complementary Role of the Computed Biomodelling through Finite Element Analysis and Computed Tomography for Diagnosis of Transcatheter Heart Valve Thrombosis. BioMed Research International, 2018. https://doi.org/10.1155/2018/1346308

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