Towards the Prediction of Successful Outcome of Transcatheter Aortic-Valve replacement (TAVR)
By Angelica Chen
I began to appreciate such simplicity, and to redefine my understanding of mathematics. I came to see it as being much more than just its constituent symbols and equations, but a beautiful language capable of describing the logical foundations of all the natural sciences. Over time, that same beauty began to appear everywhere I looked … Aortic stenosis (AS) is a lethal disease that can lead to severe cardiac complications if left untreated. A new type of non-invasive treatment for AS, transcatheter aortic-valve replacement (TAVR), exhibits comparable success rates in comparison with conventional surgical aortic valve replacement. Nevertheless, it also demonstrates significantly greater rates of paravalvular regurgitation, a serious complication associated with increased rates of later mortality. In this study, we achieve three main objectives. First, we design a computer program for automatic 2-dimensional measurement of the aortic annulus that is statistically non-inferior to radiologists’ manual measurements. Secondly, we use these measurements in addition to the Agatston calcium score to identify significant predictor variables of paravalvular regurgitation. At a significance level of 0.05, the predictor variables were identified to be aortic valve calcification and prosthesis mis-sizing. Lastly, we use these predictor variables to construct a multivariate Bayesian model that predicts the incidence of moderate post-TAVR paravalvular aortic regurgitation with 70% accuracy, highlighting its potential for clinical use in recommending patients to the appropriate AS treatment. In light of the fact that 50% of medically treated AS patients die within two years of onset of symptoms and as many as 30% of these patients cannot undergo surgery, TAVR is a life-saving procedure that has the potential to positively impact many patients’ lives. Since TAVR cannot be conducted safely without prior assessment of risk, the proposed risk-stratification model reflects a significant advancement in AS patient care.