Background: Artificial intelligence (AI)-assisted assessment of left ventricular ejection fraction (LVEF) has been increasingly adopted in clinical practice. In this study we aimed to assess the reliability and reproducibility of AI-assisted LVEF assessment in a diverse, real-world, multicentre setting. Methods: We conducted a retrospective multicentre study involving 354 cardiac magnetic resonance examinations. A standardized LVEF reassessment (M-LVEF) was performed using QMass 8.1 (Medis Medical Imaging, Leiden, Netherlands) and systematically compared with original report-derived LVEF values (R-LVEF) generated using vendor-specific AI tools. For interobserver reproducibility assessment, 3 operators independently analyzed 30 randomly selected cases using fully manual and AI-assisted modes, the latter also performed with QMass 8.1 software. Operator A performed 2 measurements in both modes for intraobserver analysis. Results: In overall and single-centre analyses, the consistency between R-LVEF and M-LVEF was good or excellent (intraclass correlation coefficient >= 0.86), but the 95% limits of agreement all exceeded +/- 5%. In 44.3% of cases > 5% differences between R-LVEF and M-LVEF were observed, and 17.5% showed > 10% differences. The AI-assisted method not only significantly reduced LVEF assessment time compared with manual analysis, but also showed superior reproducibility. This was evidenced by greater interobserver agreement among 3 operators (intraclass correlation coefficients, 0.968, 0.974, 0.984 for AI vs 0.913, 0.924, 0.971 for manual, respectively) and greater intraobserver reproducibility, all with significantly lower coefficients of variation (5.6%, 8.8%, 9.3% vs 2.5%, 3.8%, 4.4%, respectively; 3.5% vs 1.0% [P < 0.05). Conclusions: There are individual differences in AI-assisted LVEF assessment, but it has significant advantages in efficiency and reproducibility, making AI a worthwhile tool to facilitate cardiac magnetic resonance quantification.
This primer outlines alternative access strategies for transcatheter aortic valve replacement (TAVR) as its use extends to younger, lower-risk patients. Although transfemoral (TF) TAVR remains the standard, 5% to 10% of patients require alternative approaches. Such strategies include valve deployment via the carotid arteries, cavae, axillary/subclavian arteries, cardiac apex, and directly through the aorta. Careful computed tomography (CT)based planning, multidisciplinary heart-team discussion, and institutional expertise are essential. Durability data extend to 5 years with contemporary devices, whereas 10year results exist only for early-generation valves.
Background: In this we study aimed to develop and validate a dynamic prediction model for acute kidney injury (AKI) in heart failure (HF) patients. Methods: Using data from 7636 HF patients in the Medical Infor-mation Mart for Intensive Care IV (MIMIC-IV) version 3.1 database, we constructed a long short-term memory model with dynamic focal loss to handle class imbalance. We designed 2 prediction perspectives: for short-term prediction w used different data collection windows (6-72 hours) to dynamically predict the risk of AKI occurrence within subsequent specific time windows (12-72 hours); for long-term prediction we used data from specific time points after admission (12-72 hours) to predict the occurrence of AKI during the entire hospitalization. Results: The model showed robust performance across all prediction tasks (area under the receiver operating characteristic curve range, 0.80-0.94). Analysis of prediction lead time showed that the model could provide early warnings: the median lead times for predicting AKI occurrence within 12, 24, 48, and 72 hours were 9.57, 12.89, 18.77, and 27.25 hours, respectively. Feature importance analysis revealed that urine output, Sequential Organ Failure Assessment (SOFA) score, and systolic blood pressure played dominant roles in short-term prediction, whereas troponin T and history of cardiovascular surgery were more important in long-term prediction. Conclusions: The long short-term memory-based model proposed in this study captures dynamic physiological changes in HF patients and provides dynamic risk assessments for AKI with sufficient lead time.
Cardiology has grown exponentially in breadth and complexity, as reflected in the growth of adult cardiology specialty training programs have increased in number and variety. We created an online survey for final-year Canadian cardiology residents, to both understand how trainees are navigating their career choices and characterize the drivers behind these decisions. A total of 85 participants from across Canada responded to the survey from 2023-2025. A total of 94% of respondents are pursuing fellowship training, with 26% of participants planning to complete 2 fellowships. Despite significant subspecialization, almost all respondents are planning to incorporate general cardiology into their future practice.
Cardiovascular complications during pregnancy are increasing. However, many are preventable with increased provider education. To address these gaps, we developed 4 virtual asynchronous Cardio-Obstetrics modules. This proof-of-concept study evaluated trainee satisfaction, content knowledge, collaborative skills, and perceived interactivity. Thirty-four trainees from 3 Canadian programs participated. Trainees showed improvement in content knowledge and collaborative skills, rated simulated dialogue as the design element with highest interactivity, and indicated preferences for collaboration prompts. These findings support the use of asynchronous virtual modules in cardiology, with potential to enhance provider knowledge in caring for pregnant patients with heart disease.
