Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleMuhs T, Ljubojevic-Holzer S, Sattler S, 2025,
Anti-inflammatory Therapies for Ischemic Heart Disease
, CURRENT CARDIOLOGY REPORTS, Vol: 27, ISSN: 1523-3782- Cite
- Citations: 1
-
Journal articleKeene D, Nielsen JC, Burri H, et al., 2025,
Cardiac implantable electronic device upgrades and downgrades: a Clinical Consensus Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Asia Pacific Heart Rhythm Association (APHRS), Canadian Heart Rhythm Society (CHRS), Heart Rhythm Society (HRS), and the Latin American Heart Rhythm Society (LAHRS)
, EUROPACE, Vol: 27, ISSN: 1099-5129 -
Journal articleKacar P, Prokselj K, Ghonim S, et al., 2025,
Advances in the imaging of pulmonary hypertension
, INTERNATIONAL JOURNAL OF CARDIOLOGY CONGENITAL HEART DISEASE, Vol: 22, ISSN: 2666-6685 -
Journal articleCiaccio EJ, Hsia HH, Yarmohammadi H, et al., 2025,
Atrial fibrillation substrate mapping with emphasis on voltage-based guidance
, WORLD JOURNAL OF CARDIOLOGY, Vol: 17, ISSN: 1949-8462 -
Journal articleReddy RK, Howard JP, Ahmad Y, 2025,
Frailty Assessment for Decision-Making in Older Patients With Acute Coronary Syndrome
, JAMA INTERNAL MEDICINE, ISSN: 2168-6106 -
Journal articleLiang Y, Sau A, Zeidaabadi B, et al., 2025,
Artificial intelligence-enhanced electrocardiography to predict regurgitant valvular heart diseases: an international study
, European Heart Journal, Vol: 46, Pages: 4823-4837, ISSN: 0195-668XBackground and AimsValvular heart disease (VHD) is a significant source of morbidity and mortality, though early intervention can improve outcomes. This study aims to develop artificial intelligence-enhanced electrocardiography (AI-ECG) models to diagnose and predict future moderate or severe regurgitant VHDs (rVHDs), including mitral regurgitation (MR), tricuspid regurgitation (TR), and aortic regurgitation (AR).MethodsThe AI-ECG models were developed in a data set of 988 618 ECG and transthoracic echocardiogram pairs from 400 882 patients from Zhongshan Hospital, Shanghai, China. The AI-ECG models used a residual convolutional neural network with a discrete-time survival loss function. External evaluation was performed in outpatients from a secondary care data set from Beth Israel Deaconess Medical Center, Boston, USA, consisting of 34 214 patients with linked echocardiography.ResultsIn the internal test set, the AI-ECG models accurately predicted future significant MR [C-index 0.774, 95% confidence interval (CI) 0.753–0.792], AR (0.691, 95% CI 0.657–0.720), and TR (0.793, 95% CI 0.777–0.808). In age- and sex-adjusted Cox models, the highest risk quartile had a hazard ratio (HR) of 7.6 (95% CI 5.8–9.9, P < .0001) for risk of future significant MR, compared with the lowest risk quartile. For future AR and TR, the equivalent HRs were 3.8 (95% CI 2.7–5.5) and 9.9 (95% CI 7.5–13.0), respectively. These findings were confirmed in the transnational external test set. Imaging association analyses demonstrated AI-ECG predictions were associated with subclinical chamber remodelling.ConclusionsThis study developed AI-ECG models to diagnose and predict rVHDs and validated the models in a transnational and ethnically distinct cohort. The AI-ECG models could be utilized to guide surveillance echocardiography in patients at risk of future rVHDs, to facilitate early detection and intervention.
-
Journal articleLiang Y, Sau A, Zeidaabadi B, et al., 2025,
Artificial intelligence-enhanced electrocardiography models for the diagnosis and prediction of future regurgitant valvular heart diseases: an international multi-center study
, European Heart Journal, Vol: 46, Pages: 4823-4837, ISSN: 0195-668XBackground and Aims Valvular heart disease (VHD) is a significant source of morbidity and mortality, though early intervention can improve outcomes. This study aims to develop artificial intelligence-enhanced electrocardiogram (AI-ECG) models to diagnose and predict future moderate or severe regurgitant VHDs (rVHDs), including mitral regurgitation (MR), tricuspid regurgitation (TR), and aortic regurgitation (AR). Methods:The AI-ECG models were developed in a dataset of 988,618 ECG and transthoracic echocardiogram pairs from 400,882 patients from Zhongshan Hospital, Shanghai, China. The AI-ECG models used a residual convolutional neural network with a discrete-time survival loss function. External evaluation was performed in outpatients from a secondary care dataset from Beth Israel Deaconess Medical Center, Boston, USA, consisting of 34,214 patients with linked echocardiography. Results:In the internal test set, the AI-ECG models accurately predicted future significant MR (C-index 0.774, 95%CI 0.753-0.792), AR (0.691, 95%CI 0.657-0.720) and TR (0.793, 95%CI 0.777-0.808). In age- and sex-adjusted Cox models, the highest risk quartile had a hazard ratio (HR) of 7.6 (95%CI 5.8-9.9, P < 0.0001) for risk of future significant MR, compared to the lowest risk quartile. For future AR and TR, the equivalent HRs were 3.8 (95%CI 2.7-5.5) and 9.9 (95%CI 7.5-13.0), respectively. These findings were confirmed in the transnational external test set. Imaging association analyses demonstrated AI-ECG predictions were associated with subclinical chamber remodeling. Conclusions: This study developed AI-ECG models to diagnose and predict future rVHDs and validated the models in a transnational and ethnically distinct cohort. AI-ECG could be utilized to guide surveillance echocardiography in patients at risk of future rVHDs, to facilitate early detection and intervention.REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06475157.
-
Journal articleDanylenko O, Babu-Narayan S, Brida M, et al., 2025,
Biventricular Interatrial Shunt With Desaturation After Atrial Septal Defect Repair.
, JACC Case Rep, Vol: 30BACKGROUND: Patients after atrial septal defect (ASD) repair with residual shunt, elevated pulmonary arterial pressure or arrhythmias, and those repaired at adult age should be followed on a regular basis. CASE SUMMARY: This case describes a 43-year-old female with previous ASD repair, who presented with breathlessness, tiredness, and evidence of desaturation on exercise with a mildly positive bubble echo. While local investigations were inconclusive, diagnostic tests performed at our center revealed an inferior sinus venosus ASD-type defect with bidirectional flow, even though neither right-heart dilatation, pulmonary hypertension nor hemodynamically significant shunt was identified. The patient was offered surgical repair of the residual interatrial communication to give the patient symptomatic benefit. DISCUSSION: This case demonstrates a rare presentation of a post-ASD repair patient with bidirectional shunt without right-heart overload and no pulmonary hypertension. TAKE-HOME MESSAGE: Patients with repaired congenital defects should be followed up by specialized congenital services at reasonable interval based on the lesion and the results of the repair.
-
Journal articleKoutentaki F, Nicastro L, Kelwick R, et al., 2025,
Investigating the mechanisms of small extracellular vesicles in cardiovascular disease using the living myocardial slice platform
, Frontiers in Cardiovascular Medicine, Vol: 12, ISSN: 2297-055XCardiovascular disease remains the leading cause of death worldwide. Extracellular vesicles (EVs) play a regulatory role in homeostasis, associated with their contribution to cell-cell communication. Recently, it has been confirmed that they also regulate the progression of cardiovascular disease. Specifically, myocardial injury induces an increase in the secretion of small extracellular vesicles (sEVs), both in the cardiac microenvironment and peripheral circulation. Small extracellular vesicles (sEVs) are lipid bilayer particles within the size range of 35-200nm and are secreted by all cell types. Their high content of bioactive cargo – primarily miRNA – is altered in response to external stimuli, leading to behavioral changes of the recipient cells. In the context of cardiovascular disease, this change leads to acute and long term functional, structural, and biochemical effects on the myocardium. However, the mechanism behind the altered sEVs secretion and their changes in content in the context of cardiovascular disease is yet to be determined. That is partially due to the challenges associated with the isolation of cardiac-derived sEVs, which are essential for the investigation of the mechanisms behind cardiovascular disease progression. Living myocardial slices (LMS) provide an ideal platform for the isolation and investigation of sEVs function in the myocardium. Indeed, LMS not only maintain the cellular complexity and architecture of the native adult myocardium but can also be cultured over days/weeks without significant alterations in cardiac function, making them a reliable model for sEVs isolation and characterization at multiple timepoints. This review aims to summarize recent findings on the effect of sEVs on the onset and progression of cardiovascular disease and to discuss different methods for their isolation from LMSs and the investigation of their functional, structural, and biochemical effect on the myocardium.
-
Conference paperO'Neill M, Ma J, Aldridge J, et al., 2025,
Cohort-scale automated patch clamp data improves variant classification and penetrance stratification for <i>SCN5A</i>-Brugada Syndrome
, American-Heart-Association Scientific Sessions / American-Heart-Association Resuscitation Science Symposium, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322 -
Conference paperPrzybylski R, Norrish G, Claggett B, et al., 2025,
Natural History and Outcomes of Massive Left Ventricular Hypertrophy in Childhood Hypertrophic Cardiomyopathy
, American-Heart-Association Scientific Sessions / American-Heart-Association Resuscitation Science Symposium, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322 -
Conference paperFoley M, Mohsin M, Ahmed-Jushuf F, et al., 2025,
Endocardial to epicardial blood flow ratio predicts the placebo-controlled efficacy of the coronary sinus reducer
, American-Heart-Association Scientific Sessions / American-Heart-Association Resuscitation Science Symposium, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322 -
Conference paperPastika L, Zeidaabadi B, Patlatzoglou K, et al., 2025,
AI-enhanced ECG for diastolic dysfunction: development, validation and prognosis across five international cohorts
, American-Heart-Association Scientific Sessions / American-Heart-Association Resuscitation Science Symposium, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322 -
Conference paperMubarak O, Constantine A, Moledina S, et al., 2025,
Long-Term Outcomes in Adults with Double Outlet Right Ventricle: A Multi-centred Cohort Study
, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322 -
Journal articleJurcut R, Barriales-Villa R, Biagini E, et al., 2025,
Key priorities for the implementation of the 2023 ESC Guidelines for the management of cardiomyopathies in low-resource settings.
, Eur Heart J Qual Care Clin Outcomes, Vol: 11, Pages: 910-918ESC Guidelines provide best practice, evidence-based recommendations for diagnosing and treating patients with cardiovascular diseases. It is not always possible for best practices to be followed, however, particularly in low-resource settings. To address this issue, a set of guideline-related documents were created to identify key priorities for users in these settings. The documents highlight the related recommendations and describe key strategies for clinicians to approach implementation of these recommendations or discuss alternatives which are in line with the intention of the recommendations, if not having all of the same advantages. The suggestions cannot be used as exact substitutes for the original recommendations in the guidelines, which have not been altered and continue to reflect best practice. This document on key priorities for low-resource settings was developed by the task force chairs and other members of the task force who produced the 2023 ESC Guidelines for the management of cardiomyopathies, which are freely available on the ESC website (https://www.escardio.org/Guidelines). This document also underwent external review including international experts from within and beyond Europe and ESC partner organizations, including the Interamerican Society of Cardiology, the Pan-African Society of Cardiology, the Asian Pacific Society of Cardiology, and the ASEAN Federation of Cardiology.
-
Journal articleAdemi Z, Rodda SE, Vivoda K, et al., 2025,
Highlights from the manifesto on the health economics of cardiovascular disease prevention
, PharmacoEconomics, Vol: 43, Pages: 1281-1292, ISSN: 1170-7690Cardiovascular disease (CVD) is a major contributor to the health and economic burden of disease globally. In this paper we discuss the literature on the health economics of the prevention and early intervention in CVD. We reveal the large economic impact of CVD and provide the economic argument supporting the calls for early detection and diagnosis of CVD outlined in the Global Heart Hub's patient-led Manifesto for Change. Many challenges in conducting cost-effectiveness analyses of interventions for CVD prevention are identified, as well as the emerging statistical and economic methods to help overcome these issues. Lastly, we acknowledge the profound disparities in cardiovascular health faced by minority or underserved populations, and the important role that prevention and early intervention can play in improving health equity.
-
Journal articleStroeks SLVM, Merlo M, Mora-Ayestaran N, et al., 2025,
Sex Differences in Prognosis of Patients With Genetic Dilated Cardiomyopathy
, CIRCULATION-HEART FAILURE, Vol: 18, ISSN: 1941-3289 -
Journal articleBen-Aicha S, Srivastava P, Kararigas G, et al., 2025,
Harnessing multiomics technologies and machine learning for advancing personalized theranostic approaches in atherosclerosis
, PHARMACOLOGICAL REVIEWS, Vol: 77, ISSN: 0031-6997 -
Journal articleNaidoo P, Fernandes P, Dadashi Serej N, et al., 2025,
Consensus-guided evaluation of self-supervised learning in echocardiographic segmentation.
, Comput Biol Med, Vol: 198BACKGROUND: Left ventricle segmentation is a fundamental task in echocardiography, essential for assessing cardiac function. However, deep learning models for segmentation rely on large labelled datasets, which are expensive and time-consuming to annotate. Self-supervised learning has emerged as a promising approach to leverage unlabelled data, but its effectiveness for left ventricle segmentation remains underexplored. METHODS: This study investigates self-supervised learning for echocardiographic segmentation, comparing various pretext tasks. The impact of dataset size and distribution on pre-training is examined, revealing that excessive unlabelled data can degrade performance due to redundancy and low variability. A novel multi-expert labelled dataset is introduced to enhance segmentation evaluation, using consensus-based annotations to reduce annotation noise and improve reliability. RESULTS: Among the self-supervised learning methods evaluated, contrastive learning consistently outperforms other approaches, particularly in low-label settings. The study demonstrates that AI models pre-trained using self-supervised learning and fine-tuned with only 15% of labelled data achieve stronger alignment with multi-expert consensus than any individual expert. CONCLUSION: The findings suggest that AI models can generalise well across expert annotations, providing more reliable and reproducible assessments.
-
Journal articleReddy RK, Bernstein H, Howard JP, et al., 2025,
Complete revascularization with percutaneous coronary intervention for acute coronary syndromes presenting with multivessel disease
, CURRENT OPINION IN CARDIOLOGY, Vol: 40, Pages: 410-416, ISSN: 0268-4705
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.