Predictors of Disease Progression and Adverse Clinical Outcomes in Patients With Moderate Aortic Stenosis Using an Artificial Intelligence-Based Software Platform.

Patients with moderate aortic stenosis (AS) have a greater risk of adverse clinical outcomes than that of the general population. How this risk compares with those with severe AS, along with factors associated with outcomes and disease progression, is less clear. We analyzed serial echoes (from 2017 to 2019) from a single healthcare system using Tempus Next (Chicago, Illinois) software. AS severity was defined according to American Heart Association/American College of Cardiology guidelines. Outcomes of interest included death or heart failure hospitalization. We used Cox proportional hazards models and logistic regression to identify predictors of clinical outcome and disease progression, respectively. From 82,805 echoes for 61,546 patients, 1,770; 914; 565; and 1,463 patients had no, mild, moderate, or severe AS, respectively. Both patients with moderate and those with severe AS experienced a similar prevalence of adverse clinical outcomes (p = 0.45) that was significantly greater than that of patients without AS (p <0.01). In patients with moderate AS, atrial fibrillation (hazard ratio 3.29, 95% confidence interval 1.79 to 6.02, p <0.001) and end-stage renal disease (hazard ratio 3.34, 95% confidence interval 1.87 to 5.95, p <0.001) were associated with adverse clinical outcomes. One-third of patients with moderate AS with a subsequent echo (139/434) progressed to severe AS within 1 year. In conclusion, patients with moderate AS can progress rapidly to severe AS and experience a similar risk of adverse clinical outcomes; predictors include atrial fibrillation and low left ventricular ejection fraction. Machine learning algorithms may help identify these patients. Whether these patients may warrant earlier intervention merits further study.

National Variability in Pacemaker Implantation Rate Following TAVR: Insights From the STS/ACC TVT Registry.

BACKGROUND: Although permanent pacemaker (PPM) implantation is a common complication of transcatheter aortic valve replacement (TAVR), hospital variation and change in PPM implantation rates are ill defined. OBJECTIVES: The aim of this study was to determine hospital-level variation and temporal trends in the rate of PPM implantation following TAVR. METHODS: Using the American College of Cardiology/Society of Thoracic Surgeons TVT (Transcatheter Valve Therapy) Registry, temporal changes in variation of in-hospital and 30-day PPM implantation were determined among 184,452 TAVR procedures across 653 sites performed from 2016 to 2020. The variation in PPM implantation adjusted for valve type by annualized TAVR volume was determined, and characteristics of sites below, within, and above the 95% boundary were identified. A series of stepwise multivariable hierarchical models were then fit, and the median OR was used to measure variation in pacemaker rates among sites. RESULTS: From 2016 to 2020, the overall rate of PPM implantation was 11.3%, with wide variation across sites (range: 0%-36.4%); rates trended lower over time. Adjusted for annualized volume, there were 34 sites with PPM implantation rates above the 95th percentile CI and 28 with rates below, with wide variation among the remaining sites. After adjusting for patient-level covariates, there was variation among sites in the probability of PPM implantation (median OR: 1.39; 95% CI: 1.35-1.43, P < 0.001); although some of the variation was explained by the addition of valve type, residual variation in PPM implantation rates persisted in additional models incorporating site-level covariates (annualized volume, region, teaching status, hospital beds, etc). CONCLUSIONS: Although PPM implantation rates have decreased over time, substantial site-level variation remains even after accounting for observed patient characteristics and site-level factors. As there are numerous outlier sites both above and below the 95% confidence limit, dissemination of best practices from high-performing sites to low-performing sites and guideline-based education may be important quality improvement initiatives to reduce rates of this common complication.

A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression.

Importance: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization. Objective: To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression. Design, Setting, and Participants: This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024. Exposure: DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures: Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results: A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors. Conclusions and Relevance: In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.

The Current Landscape of Transcatheter Tricuspid Valve Intervention.

Tricuspid regurgitation (TR) is common, and its prevalence increases with age. It was previously estimated that there are 1.6 million patients in the United States with moderate or worse TR, and more contemporary data suggest the age-adjusted prevalence of TR is 0.55%. Increasing TR severity is associated with an adverse prognosis independent of the pulmonary artery pressure and the degree of right heart failure. In heart failure with reduced ejection fraction, survival is significantly worsened when moderate or severe TR is present. The mainstay of therapy has traditionally been surgery, but outcomes are poor. There has been increasing attention on the potential role of transcatheter interventions for TR. Numerous platforms are in developmental evolution, which broadly fall into 3 categories: valve replacement, valve repair (subdivided into annular, leaflet, and chordal platforms), and caval valve implantation. In this review, we examine all these strategies and devices, including guidance on how to appropriately select patients who can benefit from intervention.