Abstract 10028: A Predictive Model of Iliofemoral Vascular Complications Following Transfemoral Transcatheter Aortic Valve Replacement

Abstract

Objective: Design and validate a predictive model for iliofemoral vascular complications (IVC) following transfemoral-transcatheter aortic valve replacement (TF-TAVR). Background: IVC following TF-TAVR are common and associated with higher adverse events. However, there is no acceptable method to predict IVC. Methods: We used data from 3,706 TF-TAVR patients treated at Cedars-Sinai medical center between 2013-2021. We analyzed CT images of 516 matched patients to formulate a novel score index (Cedars-Sinai Index, CSI) and a predictive model and validated it on data of 609 consecutive patients. Patients with pre-procedural alternative access were excluded. Results: IVC occurred in 352 (9.1%) patients. The model design cohort includes 516 matched patients (by age, sex, and year of procedure) in a 2:1 ratio of control and IVC group. Sheath size, the sum of angles, number of curves, minimal lumen diameter (MLD), and sheath-to-femoral artery diameter ratio (SFAR) were significant predictors for IVC. The CSI formula consisting of the multiplication of the sum of angles and the number of curves divided by the MLD had an 84.3% sensitivity and 96.8% specificity (C-stat 0.936, 95% CI 0.911-0.959, p<0.001) for predicting IVC. Setting CSI score > 100 and SFAR >1.0 increased overall accuracy and correctly predicted 97.7% of the complications. The validated model showed a 89.5% sensitivity, 98.9% specificity and 94.2% accuracy (C-stat 0.942, 95% CI 0.904-0.980, p<.0001). Conclusion: Our proposed model is simple-to-use and accurately identifies patients at high risk for IVC. The model may be useful to implement in the pre-procedural planning of TAVR.