158 papers
Using a large national dataset, this study demonstrates that higher hospital cardiac capability is independently associated with reduced cardiogenic shock mortality and mitigates the increased risk typically seen in transferred patients, thereby supporting the implementation of regionalized hub-and-spoke referral systems.
In a large multicenter study of Fontan patients, exercise capacity was primarily determined by sex, age, and body size rather than by specific anatomical or surgical factors such as ventricular morphology, Fontan subtype, or conduit size.
This longitudinal study of Finnish municipal employees found that while health behaviors such as smoking, diet, and physical activity explain approximately 40% of the excess cardiovascular disease risk associated with lower occupational class, the majority of this inequality remains unexplained by these factors, pointing to the significant role of broader social determinants.
Using causal machine learning on real-world electronic health records, this study found that GLP-1 receptor agonists are associated with a lower risk of all-cause mortality or heart failure hospitalization compared to SGLT2 inhibitors in heart failure patients, though evidence for individualized treatment selection remains limited.
A systematic needs assessment of the DCM Consortium identified four distinct, highly variable care models for genetic evaluation of dilated cardiomyopathy, revealing that while most sites utilize genetic testing for treatment decisions, the implementation of comprehensive services is hindered by operational and staffing challenges despite a consensus on necessary facilitators like genetic counselors and standardized guidelines.
This study demonstrates that in patients with advanced renal disease, cardiac MRI parametric mapping (specifically T1 time and ECV) exhibits high negative predictive value for diagnosing cardiac amyloidosis, while the overall reader impression shows high negative but low positive predictive value.
This study demonstrates that while fully automated AI-assisted echocardiographic measurements show moderate agreement with expert cardiologists due to systematic biases, their repeatability is comparable to experienced readers, supporting their utility for longitudinal monitoring of ATTR cardiomyopathy.
In the tri-ethnic SABRE study, poor sleep quality was significantly associated with increased left ventricular mass in South Asian and African/African-Caribbean participants, but not in Europeans, suggesting ethnic-specific links between sleep and cardiovascular structural changes.
In a 10-year national registry study of over 14,000 patients, neither the right internal thoracic artery nor the radial artery as a second graft significantly impacted long-term survival in either sex, though the radial artery was associated with higher repeat revascularization in men and more cerebrovascular accidents in women.
In a population-based target trial emulation using TriNetX data, the initiation of PCSK9 inhibitor therapy was associated with a significantly lower risk of incident depression compared to statin therapy among adults with hyperlipidemia.
This exploratory Granger causality study demonstrates that beat-to-beat functional mitral regurgitation is driven by heterogeneous, subtype-specific temporal dynamics, where ventricular loading dominates short-term prediction in ventricular cases while atrial volume and papillary muscle interactions govern longer-term patterns in atrial cases.
This study demonstrates that while removing race from cardiovascular risk prediction models improves statistical parity metrics, it inadvertently redistributes clinical harms by causing systematic underprediction and concentrated overtreatment or undertreatment among Black individuals, highlighting the need for comprehensive empirical evaluation beyond population-average performance before adopting race-neutral tools.
CorSeg-CineSAX is an open-source deep learning framework that utilizes a large-scale, multi-center dataset and an anatomically constrained post-processing pipeline to achieve robust, fully automatic segmentation of cardiac MRI across diverse and previously unseen disease categories with high clinical agreement.
This study presents an automated algorithm using LAT-derived directed graphs that accurately identifies and classifies single- and dual-loop macro-reentrant atrial tachycardia circuits, achieving 88% anatomical localization accuracy and 93% classification agreement with expert electrophysiologists.
In a randomized trial, instantaneous wave-free ratio (iFR)-guided coronary artery bypass grafting significantly improved 36-month graft patency for both left internal mammary artery and saphenous vein grafts compared to conventional angiography-guided CABG by optimizing target selection and reducing competitive flow, although major adverse cardiac and cerebrovascular events remained similar between the two groups.
The BECAME-HF project utilized plasma lipidomics to identify distinct, clinically relevant HFpEF phenogroups across Belgian and Canadian cohorts, revealing a specific high-risk metabolic signature characterized by severe organ dysfunction and reduced survival.
This study demonstrates that fully automated, AI-derived echocardiographic measurements of left ventricular strain and right ventricular function provide independent and incremental prognostic value for risk stratification in transthyretin cardiomyopathy, performing comparably to human measurements and significantly enhancing existing biomarker-based staging systems.
This retrospective study demonstrates that the Kurume-HEARTS program, which utilizes planned hospitalizations for structured education and management, significantly reduces total hospitalization costs and length of stay per person-year compared to unplanned admissions for patients with recurrent heart failure.
This study utilizing data from the German National Cohort reveals that while postmenopausal women exhibit more adverse cardiac remodeling, this is primarily driven by a higher burden of cardiometabolic risk factors rather than stronger risk-factor associations, with distinct sex- and age-specific patterns identified for BMI, triglycerides, and sleep problems.
This study demonstrates that a deep learning model can accurately diagnose atrial cardiomyopathy and predict left atrial imaging indices from routine 12-lead electrocardiograms, thereby significantly improving risk stratification for atrial fibrillation and heart failure beyond existing clinical scores.