Complementary Role of the Computed Biomodelling through Finite Element Analysis and Computed Tomography for Diagnosis of Transcatheter Heart Valve Thrombosis. (22nd October 2018)

• Record Type: Journal Article
• Authors: Nappi, Francesco; Mazzocchi, Laura; Avtaar Singh, Sanjeet Singh; Morganti, Simone; Sablayrolles, Jean-Louis; Acar, Christophe; Auricchio, Ferdinando
• Other Names: Del Gaudio Costantino Academic Editor.
• Is Part Of: BioMed research international. Volume 2018(2018)
• Publication Date: 2018-10-22
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2018/1346308
• Languages: English
• ISSNs: 2314-6133

Abstract

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.