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Background: Surgery remains the only recommended intervention for patients with native aortic regurgitation. A transcatheter therapy to treat patients at high risk for mortality and complications with surgical aortic valve replacement represents an unmet need. Commercial transcatheter heart valves in pure aortic regurgitation are hampered by unacceptable rates of embolisation and paravalvular regurgitation. The Trilogy transcatheter heart valve (JenaValve Technology, Irvine, CA, USA) provides a treatment option for these patients. We report outcomes with transfemoral transcatheter aortic valve implantation (TAVI) in patients with pure aortic regurgitation using this dedicated transcatheter heart valve., Methods: The ALIGN-AR trial is a prospective, multicentre, single-arm study. We recruited symptomatic patients (aged ≥18 years) with moderate-to-severe or severe aortic regurgitation at high risk for mortality and complications after surgical aortic valve replacement at 20 US sites for treatment with the Trilogy transcatheter heart valve. The 30-day composite primary safety endpoint was compared for non-inferiority with a prespecified performance goal of 40·5%. The primary efficacy endpoint was 1-year all-cause mortality compared for non-inferiority with a performance goal of 25%. This trial is registered with ClinicalTrials.gov, NCT04415047, and is ongoing., Findings: Between June 8, 2018, and Aug 29, 2022, we screened 346 patients. We excluded 166 (48%) patients and enrolled 180 (52%) patients with symptomatic aortic regurgitation deemed high risk by the heart team and independent screening committee assessments. The mean age of the study population was 75·5 years (SD 10·8), and 85 (47%) were female, 95 (53%) were male, and 131 (73%) were White. Technical success was achieved in 171 (95%) patients. At 30 days, four (2%) deaths, two (1%) disabling strokes, and two (1%) non-disabling strokes occurred. Using standard Valve Academic Research Consortium-2 definitions, the primary safety endpoint was achieved, with events occurring in 48 (27% [97·5% CI 19·2-34·0]) patients (p non-inferiority <0·0001), with new pacemaker implantation in 36 (24%) patients. The primary efficacy endpoint was achieved, with mortality in 14 (7·8% [3·3-12·3]) patients at 1 year (p non-inferiority <0·0001)., Interpretation: This study shows the safety and effectiveness of treating native aortic regurgitation using a dedicated transcatheter heart valve to treat patients with symptomatic moderate-to-severe or severe aortic regurgitation who are at high risk for mortality or complications after surgical aortic valve replacement. The observed short-term clinical and haemodynamic outcomes are promising as are signs of left ventricular remodelling, but long-term follow-up is necessary., Funding: JenaValve Technology., Competing Interests: Declaration of interests TPV reports institutional funding to Columbia University Irving Medical Center from JenaValve Technology, Abbott Vascular, Boston Scientific, Edwards Lifesciences, and Medtronic; and he personally received consulting fees from 4C Medical and Philips. OKK reports that he is part of a core laboratory contracting with JenaValve Technology but he has not received any direct compensation; he further reports consulting fees from Edwards Lifesciences, VDyne, Siemens, Philips, Laralab, and Restore Medical. NH reports that she is part of a core laboratory contracting with JenaValve Technology but she has not received any direct compensation. JMM reports consulting fees and honoraria from Edwards Lifesciences, Medtronic, Abbott, Shockwave, and Equity in Excision Medical, and Conkay Medical. IG reports consulting fees from Edwards Lifesciences, Medtronic, Boston Scientific, Abbott SJM, and Atricure. SKK reports consultant fees (honoraria) from Admedus, Meril Lifesciences, JenaValve Technology, and Abbott Vascular; scientific advisory board participation (equity) with Dura Biotech, MicroInterventional Devices, Thubrikar Aortic Valve, Supira, and Admedus; and institutional funding to Columbia University and the Cardiovascular Research Foundation from Edwards Lifesciences, Medtronic, Abbott Vascular, Boston Scientific, and JenaValve Technology. NA reports medical advisory board fees and consultant and proctor fees from Nininger Medical, Edwards Lifesciences, V-wave, JenaValve Technology, Abbott, Vdyne, and Boston Scientific, and participation in industry-sponsored trials and research funding from Edwards Lifesciences, JenaValve Technology, Boston Scientific, V-wave, AccuCinch, Highlife, Tendyne, and preCardia. DSP is the Chief Medical officer of JenaValve Technology and a consultant for Abiomed, Abbott Vascular, Magenta, NuPulseCV, and Terumo. MBL reports institutional clinical research grants from Abbott, Boston Scientific, Edwards Lifesciences, Medtronic, and JenaValve Technology. HG reports consulting fees from Medtronic and Boston Scientific. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR)., Objectives: This study aimed to develop ECG deep learning algorithms to identify moderate or severe AS, AR, and MR alone and in combination., Methods: A total of 77,163 patients undergoing ECG within 1 year before echocardiography from 2005-2021 were identified and split into train (n = 43,165), validation (n = 12,950), and test sets (n = 21,048; 7.8% with any of AS, AR, or MR). Model performance was assessed using area under the receiver-operating characteristic (AU-ROC) and precision-recall curves. Outside validation was conducted on an independent data set. Test accuracy was modeled using different disease prevalence levels to simulate screening efficacy using the deep learning model., Results: The deep learning algorithm model accuracy was as follows: AS (AU-ROC: 0.88), AR (AU-ROC: 0.77), MR (AU-ROC: 0.83), and any of AS, AR, or MR (AU-ROC: 0.84; sensitivity 78%, specificity 73%) with similar accuracy in external validation. In screening program modeling, test characteristics were dependent on underlying prevalence and selected sensitivity levels. At a prevalence of 7.8%, the positive and negative predictive values were 20% and 97.6%, respectively., Conclusions: Deep learning analysis of the ECG can accurately detect AS, AR, and MR in this multicenter cohort and may serve as the basis for the development of a valvular heart disease screening program., Competing Interests: Funding Support and Author Disclosures This study was supported by a National Institutes of Health Institutional Training Grant (2T32HL007854-21) and National Institutes of Health/National Heart, Lung, and Blood Institute Award (R01HL148248) (to Dr Elias and Dr Perotte’s institution). Dr Elias has received research grant support provided to his institution from Pfizer, Eidos Therapeutics, Google, and Edwards LifeSciences; and is an inventor on a pending patent related to the ValveNet algorithm described in this paper. Dr Poterucha owns stock in Abbott Laboratories and Baxter International with research support provided to his institution from the Amyloidosis Foundation, Eidos Therapeutics, Pfizer, Edwards Lifesciences, and the Glorney-Raisbeck Fellowship Award from the New York Academy of Medicine; and is an inventor on a pending patent related to the ValveNet algorithm described in this paper. Dr Hahn has received speaker fees from Abbott Structural, Edwards Lifesciences, and Philips Healthcare; has institutional consulting contracts with Abbott Structural, Boston Scientific, Edwards Lifesciences, and Gore and Associates; has equity with Navigate; and is Chief Scientific Officer for the Echocardiography Core Laboratory at the Cardiovascular Research Foundation for multiple industry-sponsored trials, for which she receives no direct industry compensation. Dr Tison has received research grants from General Electric, Janssen Pharmaceuticals, and Myokardia; has received personal fees from Myokardia Digital Health as an advisory group member; and has served as an unpaid advisor for Cardiogram. Dr Kodali has served as a consultant for or received honoraria from Admedus, Meril Lifesciences, JenaValve, and Abbott Vascular; has served on the scientific advisory board for and owns equity in Dura Biotech, MicroInterventional Devices, Thubrikar Aortic Valve, Supira, and Admedus; and has received institutional funding to Columbia University and/or the Cardiovascular Research Foundation from Edwards Lifesciences, Medtronic, Abbott Vascular, Boston Scientific, and JenaValve. Dr Khalique has served as a consultant for Boston Scientific, Edwards Lifesciences, and Abbott Structural. Dr Cohen has received research grant support and consulting income from Edwards Lifesciences, Abbott, Boston Scientific, and Medtronic. Dr Einstein has received speaker fees from Ionetix; has received consulting fees from W. L. Gore and Associates; has received authorship fees from Wolters Kluwer Healthcare–UpToDate; and his institution has grants/grants pending from Attralus, Canon Medical Systems, Eidos Therapeutics, GE Healthcare, Pfizer, Roche Medical Systems, W. L. Gore and Associates, and XyloCor Therapeutics. Dr Nazif has served as a consultant for or received honoraria from Edwards Lifesciences, Medtronic, Venus Medtech, and Boston Scientific. Dr Leon has received institutional grants for clinical research from Abbott, Boston Scientific, Edwards, JenaValve, and Medtronic; has received stock options (equity) for advisory board participation in Valve Medical, Picardia, and Venus MedTech; and is an inventor on a pending patent related to the ValveNet algorithm described in this paper. Dr Perotte is Chief Scientific and Innovation Officer at Spiden AG; and is an inventor on a pending patent related to the ValveNet algorithm described in this paper. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The threshold for the optimal computed tomography (CT) number in Hounsfield Units (HU) to quantify aortic valvular calcium on contrast-enhanced scans has not been standardized. Our aim was to find the most accurate threshold to predict paravalvular regurgitation (PVR) after transcatheter aortic valve replacement (TAVR)., Methods: 104 patients who underwent TAVR with the CoreValve prosthesis were studied retrospectively. Luminal attenuation (LA) in HU was measured at the level of the aortic annulus. Calcium volume score for the aortic valvular complex was measured using 6 threshold cutoffs (650 HU, 850 HU, LA × 1.25, LA × 1.5, LA+50, LA+100). Receiver-operating characteristic (ROC) analysis was performed to assess the predictive value for > mild PVR (n = 16). Multivariable analysis was performed to determine the accuracy to predict > mild PVR after adjustment for depth and perimeter oversizing., Results: ROC analysis showed lower area under the curve (AUC) values for fixed threshold cutoffs (650 or 850 HU) compared to thresholds relative to LA. The LA+100 threshold had the highest AUC (0.81), and AUC was higher than all studied protocols, other than the LA x 1.25 and LA + 50 protocols, where the difference approached statistical significance (p = 0.05, and 0.068, respectively). Multivariable analysis showed calcium volume determined by the LAx1.25, LAx1.5, LA+50, and LA+ 100 HU protocols to independently predict PVR., Conclusions: Calcium volume scoring thresholds which are relative to LA are more predictive of PVR post-TAVR than those which use fixed cutoffs. A threshold of LA+100 HU had the highest predictive value., (Copyright © 2017 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

With the advent of percutaneous valve interventions, the landscape for management of high-risk valve replacement and repair has changed dramatically. Transcatheter valve repair/replacement techniques can be used in conjunction with open surgery to facilitate a hybrid approach in patients with multivalve disease. We present a case of staged hybrid valve repair followed by surgical replacement for a high-risk patient with mitral regurgitation and aortic regurgitation. This case illustrates the effectiveness of the staged hybrid approach for high-risk patients with incomplete transcatheter options. We expect these techniques to play an increasingly larger role in the treatment algorithm for high-risk multivalve disease., (Copyright © 2016 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)