Daily Cardiology Research Analysis

BACKGROUND AND AIMS: Accurate near-term prediction of life-threatening ventricular arrhythmias would enable pre-emptive actions to prevent sudden cardiac arrest/death. A deep learning-enabled single-lead ambulatory electrocardiogram (ECG) may identify an ECG profile of individuals at imminent risk of sustained ventricular tachycardia (VT). METHODS: This retrospective study included 247 254, 14 day ambulatory ECG recordings from six countries. The first 24 h were used to identify patients likely to experience sustained VT occurrence (primary outcome) in the subsequent 13 days using a deep learning-based model. The development set consisted of 183 177 recordings. Performance was evaluated using internal (n = 43 580) and external (n = 20 497) validation data sets. Saliency mapping visualized features influencing the model's risk predictions. RESULTS: Among all recordings, 1104 (.5%) had sustained ventricular arrhythmias. In both the internal and external validation sets, the model achieved an area under the receiver operating characteristic curve of .957 [95% confidence interval (CI) .943-.971] and .948 (95% CI .926-.967). For a specificity fixed at 97.0%, the sensitivity reached 70.6% and 66.1% in the internal and external validation sets, respectively. The model accurately predicted future VT occurrence of recordings with rapid sustained VT (≥180 b.p.m.) in 80.7% and 81.1%, respectively, and 90.0% of VT that degenerated into ventricular fibrillation. Saliency maps suggested the role of premature ventricular complex burden and early depolarization time as predictors for VT. CONCLUSIONS: A novel deep learning model utilizing dynamic single-lead ambulatory ECGs accurately identifies patients at near-term risk of ventricular arrhythmias. It also uncovers an early depolarization pattern as a potential determinant of ventricular arrhythmias events.

BACKGROUND: Glucagon-like peptide 1 receptor agonists (GLP-1RA) reduce the incidence of major adverse cardiovascular events (MACE) in type 2 diabetes (T2D), although whether benefits extend to both subcutaneous and oral formulations remains unclear. PURPOSE: In these meta-analyses, including new data from the Semaglutide cardiOvascular oUtcomes triaL (SOUL) (oral semaglutide) and Evaluate Renal Function with Semaglutide Once Weekly (FLOW) trial, we examined cardiovascular (CV) and kidney benefits and risks of long-acting (defined as having pharmacokinetics sufficient to provide 24-h activity) GLP-1RA in T2D. DATA SOURCES: A systematic review of PubMed was conducted (to 7 February 2025). STUDY SELECTION: Randomized placebo-controlled CV and kidney outcomes trials of GLP-1RA with ≥500 individuals with T2D were included. DATA EXTRACTION: A random-effects model was used to estimate hazard ratios (HRs) for MACE, its components, all-cause mortality, hospitalization for heart failure (HHF), a composite kidney outcome (kidney failure [kidney replacement therapy or persistent estimated glomerular filtration rate [eGFR] <15 mL/min/1.73 m2], sustained ≥50% eGFR decline or nearest equivalent, or kidney-related death), worsening kidney function, and safety outcomes. DATA SYNTHESIS: Across 10 trials (n = 71,351), long-acting GLP-1RA reduced incidence rate of MACE by 14% (HR 0.86 [95% CI 0.81, 0.90]; I2 = 27.6%), HHF by 14% (0.86 [0.79, 0.93]; I2 = 2.1%), and the composite kidney outcome by 17% (0.83 [0.75, 0.92]; I2 = 20.4%) and all-cause mortality by 12% (0.88 [0.82, 0.93]; I2 = 17.5%). A consistent 14% reduction was seen for all MACE components. There was no significant heterogeneity by GLP-1RA administration route (subcutaneous vs. oral). There were no increased risks of severe hypoglycemia, retinopathy, or pancreatic events. LIMITATIONS: Trial-level meta-analyses preclude detailed subgroup analyses and may introduce ecological bias. CONCLUSIONS: As a group, long-acting GLP-1RA, including both injectable and oral formulations, reduce incidence of MACE, HHF, and kidney events and all-cause mortality in T2D.

BACKGROUND AND AIMS: Classification and risk stratification in aortic (AR), mitral (MR), and tricuspid regurgitation (TR) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) system to assess valvular regurgitation and stratify MR-progression risk. METHODS: Using transthoracic echocardiograms (TTEs) at two sites (internal development/test, external test), the DELINEATE-Regurgitation system was developed to classify AR, MR, and TR severity using colour Doppler videos. Methods of summating video-level classifications into study-level predictions were tested, comparing single-view with multiview approaches integrating predictions across multiple videos. Model agreement with cardiologists was assessed by weighted kappa. A separate AI system (DELINEATE-MR-Progression) analysing colour Doppler videos was developed to predict which patients with mild, mild-moderate, and moderate MR were most likely to progress to moderate-severe or severe MR with analysis by Kaplan-Meier and Cox proportional hazards models. RESULTS: A total of 71 660 TTEs with 1 203 980 colour Doppler videos were included. The weighted kappa in internal/external test sets for regurgitation classification was 0.81/0.76 for AR, 0.76/0.72 for MR, and 0.73/0.64 for TR using a multiview approach taking all colour Doppler videos in a study, demonstrating substantial agreement with cardiologist interpretation with superiority of multiview over single view approaches. In the progression analysis, the AI score stratified MR-progression risk even when controlled for clinical factors known to be associated with MR progression [internal test set hazard ratio 4.1 (95% confidence interval 2.5-6.6)]. CONCLUSIONS: An AI system can accurately classify AR, MR, and TR and predict MR progression beyond currently known risk factors.