METHODS: Retrospective study of 266 patients with objectively proven BAVD (pathology of explanted valves, 4D-CT prior to transcatheter valve-in-valve, 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 9 and normal prosthesis in 9). Algorithm assignment was based on the echocardiogram leading to BAVD diagnosis and the pre-discharge "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 ASE: 62%; 2014 Blauwet-Miller: 62%; 2016 EACVI: 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 significantly reduced (5 patients).
CONCLUSION: Currently recommended algorithms misclassify a significant number of BAVD patients. The accuracy was improved by a newly proposed algorithm.
METHODS AND RESULTS: A contrastive trajectories inference approach was applied to data collected from three UK studies of young adults. Low-dimensional variance was identified in 66 echocardiography variables from participants with hypertension (systolic ≥160 mmHg) relative to a normotensive group (systolic < 120 mmHg) using a contrasted principal component analysis. A minimum spanning tree was constructed to derive a normalized score for each individual reflecting extent of cardiac remodelling between zero (health) and one (disease). Model stability and clinical interpretability were evaluated as well as modifiability in response to a 16-week exercise intervention. A total of 411 young adults (29 ± 6 years) were included in the analysis, and, after contrastive dimensionality reduction, 21 variables characterized >80% of data variance. Repeated scores for an individual in cross-validation were stable (root mean squared deviation = 0.1 ± 0.002) with good differentiation of normotensive and hypertensive individuals (area under the receiver operating characteristics 0.98). The derived score followed expected hypertension-related patterns in individual cardiac parameters at baseline and reduced after exercise, proportional to intervention compliance (P = 0.04) and improvement in ventilatory threshold (P = 0.01).
CONCLUSION: A quantitative score that summarizes hypertension-related cardiac remodelling in young adults can be generated from a computational model. This score might allow more personalized early prevention advice, but further evaluation of clinical applicability is required.