Performance of Echocardiographic Algorithms for Assessment of High Aortic Bioprosthetic Valve Gradients

Background: Bioprosthetic aortic valve dysfunction (BAVD) is a challenging diagnosis. Commonly used algorithms to classify high-gradient BAVD are the 2009 American Society of Echocardiography, 2014 Blauwet-Miller, and 2016 European Association of Cardiovascular Imaging algorithms. We sought (1) to evaluate the accuracy of existing algorithms against objectively proven BAVD and (2) to propose an improved algorithm.

Methods: This was a retrospective study of 266 patients with objectively proven BAVD (pathology of explanted valves, four-dimensional computed tomography prior to transcatheter valve-in-valve replacement, or therapeutically confirmed bioprosthetic thrombosis) who were treated. Of those, 191 had obstruction, 48 had regurgitation, 15 had mixed stenosis and regurgitation, and 12 had patient-prosthesis mismatch (PPM). Normal controls were matched 1:1 (age, prosthesis size, and type), of which 43 had high gradients (PPM in 30, high flow in nine, and normal prosthesis in nine). Algorithm assignment was based on the echocardiogram leading to BAVD diagnosis and the predischarge "fingerprint" echocardiogram after surgical or transcatheter aortic valve replacement. A novel algorithm (Mayo Clinic algorithm) incorporating valve appearance in addition to Doppler parameters was developed to improve observed deficiencies.

Results: The accuracy of existing algorithms was suboptimal (2009 American Society of Echocardiography, 62%; 2014 Blauwet-Miller, 62%; 2016 European Association of Cardiovascular Imaging, 57%). The most common overdiagnosis was PPM (22%-29% of patients and controls with high gradients). The novel Mayo Clinic algorithm correctly identified the mechanism in 256 of 307 patients and controls (83%). Recognition of regurgitation was substantially improved (42 of 47 patients, 89%), and the number of PPM misdiagnoses was significantly reduced (five patients).

Conclusion: Currently recommended algorithms misclassify a significant number of BAVD patients. The accuracy was improved by a newly proposed algorithm.