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Sex- and flow-related aortic valve calcification (AVC) studies are still limited in number, and data on the exact calcium quantity and distribution are scarce. Therefore, we aimed to (1) re-define the best threshold of AVC load to distinguish severe from moderate aortic stenosis (AS) in common AS entities and to (2) evaluate differences in the aortic annulus and left ventricular outflow tract (LVOT) calcium load. Nine hundred and thirty-eight patients with contrast-enhanced cardiac MSCT and moderate-to-severe aortic stenosis (AS) were retrospectively enrolled. Patients with severe AS ≤ 1.0 cm2 (n = 841) were further separated into three AS entities: high gradient (HGAS, n = 370, 44.0%), paradoxical low gradient (pLGAS, n = 333, 39.6%), and classical low gradient (LGAS, n = 138, 16.4%). AVC, leaflet, and LVOT calcification were quantified. Aortic valve calcification scores were highest in severe HGAS, and lower in severe pLGAS and classical LGAS. In all severity and AS entities, the non-coronary cusp (NCC) was the most calcified one. LVOT calcification was consistently comparable between gender and AS entities. Accuracy of logistic regression was the highest in HGAS (male vs. female: AVC > 2156 Agatston units (AU), c-index 0.76; vs. AVC > 1292 AU, c-index 0.85; or AVC density > 406 AU/cm2, c-index 0.82; vs. > 259 AU/cm2, c-index 0.86; each p < 0.0001*) to diagnose severe AS. AVC could only be used in men to differentiate between severe LGAS and moderate AS. Data from this retrospective analysis indicate that the NCC is subject to pre-dominant degeneration throughout gender, AS severity, and several AS entities. AVC was consistently comparable in severe pLGAS and classical LGAS, but only AVC in severe LGAS could sufficiently distinguish from moderate AS in men. LVOT calcification failed to be a reliable indicator of accelerating AS.

Aims The medical need for screening of aortic valve stenosis (AS), which leads to timely and appropriate medical intervention, is rapidly increasing because of the high prevalence of AS in elderly population. This study aimed to establish a screening method using understandable artificial intelligence (AI) to detect severe AS based on heart sounds and to package the built AI into a smartphone-application. Methods and Results In this diagnostic accuracy study, we developed multiple convolutional neural networks (CNNs) using a modified stratified 5-fold cross-validation to detect severe AS in electronic heart sound data recorded at three auscultation locations. Clinical validation was performed with the developed smartphone application in an independent cohort (model establishment: n = 556, clinical validation: n = 132). Our ensemble technique integrating the heart sounds from multiple auscultation locations increased the detection accuracy of CNN model by compensating detection errors. The established smartphone application achieved a sensitivity, specificity, accuracy, and F1 value of 97.6% (41/42), 94.4% (85/90), 95.7% (126/132), and 0.93, respectively, which were higher compared to the consensus of cardiologists (81.0%, 93.3%, 89.4%, and 0.829, respectively), implying a good utility for severe AS screening. The Grad-CAM++ demonstrated that the built AIs could focus on specific heart sounds to differentiate the severity of AS. Conclusions Our CNN model combining multiple auscultation locations and exported on smartphone application could efficiently identify severe AS based on heart sounds. The visual explanation of AI decisions for heart sounds was interpretable. These technologies may support medical training and remote consultations.

BackgroundThe implantation depth (ID) is a critical condition for optimal hemodynamic and clinical outcomes in transcatheter aortic valve replacement (TAVR). The recently recommended cusp-overlap technique (COT) offers optimized fluoroscopic projections facilitating a precise ID. This single-center observational study aimed to investigate short-term clinical performance, safety, and efficacy outcomes in patients undergoing TAVR with self-expandable prostheses and application of COT in a real-world setting.Materials and methodsFrom September 2020 to April 2021, a total of 170 patients underwent TAVR with self-expandable devices and the application of COT, while 589 patients were treated from January 2016 to August 2020 with a conventional three-cusp coplanar view approach. The final ID and 30-day outcomes were compared after 1:1 propensity score matching, resulting in 150 patients in both cohorts.ResultsThe mean ID was significantly reduced in the COT cohort (−4.2 ± 2.7 vs. −4.9 ± 2.3 mm; p = 0.007) with an improvement of ID symmetry of less than 2 mm difference below the annular plane (47.3 vs. 57.3%; p = 0.083). The rate of new permanent pacemaker implantation (PPI) following TAVR was effectively reduced (8.0 vs. 16.8%; p = 0.028). While the fluoroscopy time decreased (18.4 ± 7.6 vs. 19.8 ± 7.6 min; p = 0.023), the dose area product increased in the COT group (4951 ± 3662 vs. 3875 ± 2775 Gy × cm2; p = 0.005). Patients implanted with COT had a shorter length of in-hospital stay (8.4 ± 4.0 vs. 10.3 ± 6.7 days; p = 0.007).ConclusionTranscatheter aortic valve replacement using the cusp-overlap deployment technique is associated with an optimized implantation depth, leading to fewer permanent conduction disturbances. However, our in-depth analysis showed for the first time an increase of radiation dose due to extreme angulations of the gantry to obtain the cusp-overlap view.

Background Surgical risk prediction models are routinely used to guide decision-making for transcatheter aortic valve replacement (TAVR). New and updated TAVR-specific models have been developed to improve risk stratification; however, the best option remains unknown. Objective To perform a comparative validation study of six risk models for the prediction of 30-day mortality in TAVR Methods and results A total of 2946 patients undergoing transfemoral (TF, n = 2625) or transapical (TA, n = 321) TAVR from 2008 to 2018 from the German Rhine Transregio Aortic Diseases cohort were included. Six surgical and TAVR-specific risk scoring models (LogES I, ES II, STS PROM, FRANCE-2, OBSERVANT, GAVS-II) were evaluated for the prediction of 30-day mortality. Observed 30-day mortality was 3.7% (TF 3.2%; TA 7.5%), mean 30-day mortality risk prediction varied from 5.8 ± 5.0% (OBSERVANT) to 23.4 ± 15.9% (LogES I). Discrimination performance (ROC analysis, c-indices) ranged from 0.60 (OBSERVANT) to 0.67 (STS PROM), without significant differences between models, between TF or TA approach or over time. STS PROM discriminated numerically best in TF TAVR (c-index 0.66; range of c-indices 0.60 to 0.66); performance was very similar in TA TAVR (LogES I, ES II, FRANCE-2 and GAVS-II all with c-index 0.67). Regarding calibration, all risk scoring models-especially LogES I-overestimated mortality risk, especially in high-risk patients. Conclusions Surgical as well as TAVR-specific risk scoring models showed mediocre performance in prediction of 30-day mortality risk for TAVR in the German Rhine Transregio Aortic Diseases cohort. Development of new or updated risk models is necessary to improve risk stratification.

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