A multimodal approach to predict prosthesis-patient mismatch in patients undergoing valve-in-valve trans-catheter aortic valve implantation

Abstract

AimsThe valve-in-valve transcatheter-aortic-valve-implantation (VIV-TAVI) represents an emerging procedure for the treatment of degenerated aortic bio-prostheses, and the occurrence of patient-prosthesis mismatch (PPM) after VIV-TAVI might affect its clinical efficacy. This study aimed to test a multimodal imaging approach to predict PPM risk during the TAVI planning phase and assess its clinical predictivity in VIV-TAVI procedures. MethodsConsecutive patients undergoing VIV-TAVI procedures at our Institution over 6 years were screened and those treated by self-expandable supra-annular valves were selected. The effective orifice area (EOA) was calculated with a hybrid Gorlin equation combining echocardiographic data with invasive hemodynamic assessment. Severe PPM was defined according to such original multimodality assessment as EOAi≤0.65 cm2/m2 (if BMI < 30 kg/m2) or < 0.55 cm2/m2 (if BMI ≥ 30 kg/m2). The primary endpoint was a composite of all-cause mortality and valve-related re-hospitalization during the clinical follow-up. ResultsA total of 40 VIV-TAVI was included in the analysis. According to the pre-specified multimodal imaging modality assessment, 18 patients (45.0 %) had severe PPM. Among all baseline clinical and anatomical characteristics, estimated glomerular filtration rate before VIV-TAVI (OR 0.872, 95%CI[0.765–0.994],p = 0.040), the echocardiographic pre-procedural ≥moderate AR (OR 0.023, 95%CI[0.001–0.964],p = 0.048), the MSCT-derived effective internal area (OR 0.958, 95%CI[0.919–0.999],p = 0.046) and the implantation depth (OR 2.050, 95%CI[1.028–4.086],p = 0.041) resulted as independent predictors of severe PPM at multivariable logistic analysis. At a mean follow-up of 630 days, patients with severe PPM showed a higher incidence of the primary endpoint (9.1%vs.44.4 %;p = 0.023). ConclusionIn VIV-TAVI using self-expandable supra-annular valves, a multimodal imaging approach might improve clinical outcome predicting severe PPM occurrence.