Yixiu Liang,Arunashis Sau,Boroumand Zeidaabadi,Joseph Barker,Konstantinos Patlatzoglou,Libor Pastika,Ewa Sieliwonczyk,Zachary Whinnett,Nicholas S Peters,Ziqing Yu,Xi Liu,Shuo Wang,Hongyang Lu,Daniel B Kramer,Jonathan W Waks,Yangang Su,Junbo Ge,Fu Siong Ng
{"title":"Artificial intelligence-enhanced electrocardiography to predict regurgitant valvular heart diseases: an international study.","authors":"Yixiu Liang,Arunashis Sau,Boroumand Zeidaabadi,Joseph Barker,Konstantinos Patlatzoglou,Libor Pastika,Ewa Sieliwonczyk,Zachary Whinnett,Nicholas S Peters,Ziqing Yu,Xi Liu,Shuo Wang,Hongyang Lu,Daniel B Kramer,Jonathan W Waks,Yangang Su,Junbo Ge,Fu Siong Ng","doi":"10.1093/eurheartj/ehaf448","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf448","url":null,"abstract":"BACKGROUND 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.","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"12 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Rheumatic valve disease and acceleration of cardiac aging in Africa: next-generation answers for an old, orphan problem.","authors":"Maurizio Pesce,Pasquale Maffia,Bamba Gaye","doi":"10.1093/eurheartj/ehaf350","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf350","url":null,"abstract":"","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"108 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144639954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ilse R Kelters, Yvonne Koop, Martin E Young, Andreas Daiber, Linda W van Laake
{"title":"Circadian rhythms in cardiovascular disease.","authors":"Ilse R Kelters, Yvonne Koop, Martin E Young, Andreas Daiber, Linda W van Laake","doi":"10.1093/eurheartj/ehaf367","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf367","url":null,"abstract":"<p><p>Circadian rhythms, controlled by the suprachiasmatic nucleus and peripheral clocks, regulate 24-h cycles in biological processes such as the cardiovascular system. Circadian rhythms influence autonomic balance, with parasympathetic dominance during sleep supporting cardiac recovery and sympathetic activation during the day supporting circulatory demand. Congruent with systemic and cellular circadian rhythmicity, 24-h patterns arise in the pathophysiology of cardiovascular diseases, including ischaemic heart disease, heart failure, and arrhythmias. Daily variations influence the timing and outcome of myocardial infarction, with studies reporting patterns in infarct size depending on the time of onset. Similar daily patterns are observed in cardio- and cerebrovascular complications. In heart failure, circadian rhythms are dampened but remain intact, suggesting the potential for incorporating timing in diagnostics and therapies. Sudden cardiac death follows a distinct pattern, with a higher incidence in the morning. Atrial fibrillation onset, on the other hand, occurs more frequently at night. Risk factors and modifiers, such as physiological, psychological, lifestyle, and environmental factors and comorbidities interact with circadian rhythms, thereby impacting cellular pathomechanisms and development of cardiovascular health and disease. Chronotherapy, which aligns treatments with circadian rhythms, has demonstrated potential for improving the efficacy of cardiovascular therapies. This review examines the influence of circadian rhythms on cardiovascular health in the context of specific cardiac diseases and risk factors, and it highlights the therapeutic opportunities informed by circadian patterns.</p>","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":" ","pages":""},"PeriodicalIF":37.6,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144636596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives.","authors":"Francesco Costa,Juan Jose Gomez Doblas,Arancha Díaz Expósito,Marianna Adamo,Fabrizio D'Ascenzo,Lukasz Kołtowski,Luca Saba,Guiomar Mendieta,Felice Gragnano,Paolo Calabrò,Lina Badimon,Borja Ibañez,Roxana Mehran,Dominick J Angiolillo,Thomas Lüscher,Davide Capodanno","doi":"10.1093/eurheartj/ehaf474","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf474","url":null,"abstract":"Recent advances in artificial intelligence (AI) have shown great potential in improving cardiovascular pharmacotherapy by optimizing drug selection, predicting therapeutic efficacy and adverse effects, ultimately improving patient outcomes. Leveraging techniques like machine learning and in silico modelling, AI can identify populations likely to benefit from specific treatments, expedite novel drug discovery and reduce costs. Computational methods can also facilitate the detection of drug interactions and tailor interventions based on real-world data, supporting personalized care. Artificial intelligence-based approaches also show promise in streamlining clinical trial design and execution, leveraging on real-time data on patient responsiveness, enhancing recruitment efficiency. However, in order to fully realize these benefits, robust validation across diverse patient populations is necessary to ensure accuracy and generalizability. In addition, addressing concerns regarding data quality, privacy, and bias is equally critical to avoid exacerbating existing healthcare disparities. Scientific societies and regulatory agencies must ultimately establish standardized frameworks for data management, model certification, and transparency, to enable safe and effective integration of AI into clinical practice. This manuscript aims at systematically reviewing the current state-of-the-art applications of AI in cardiovascular pharmacotherapy, describing their current potential in guiding treatment decisions, refine trial methodologies and support drug discovery.","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"94 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144630360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jens Erik Nielsen-Kudsk,Lucas V A Boersma,A John Camm
{"title":"Left atrial appendage closure: a reappraisal of atrial fibrillation guidelines.","authors":"Jens Erik Nielsen-Kudsk,Lucas V A Boersma,A John Camm","doi":"10.1093/eurheartj/ehaf454","DOIUrl":"https://doi.org/10.1093/eurheartj/ehaf454","url":null,"abstract":"","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":"29 1","pages":""},"PeriodicalIF":39.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144630255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thijs J Luttikholt, Jos Thannhauser, Niels van Royen
{"title":"Detection of large areas of thin-cap fibroatheroma in a recurrent STEMI patient using a novel artificial intelligence algorithm: moving from 2D to 3D.","authors":"Thijs J Luttikholt, Jos Thannhauser, Niels van Royen","doi":"10.1093/eurheartj/ehaf189","DOIUrl":"10.1093/eurheartj/ehaf189","url":null,"abstract":"","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":" ","pages":"2712"},"PeriodicalIF":37.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12257293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Oral health benefits and safety of xylitol and potential cardiovascular risk: questioning the validity of the model of Witkowski et al.","authors":"Gregory C Valentine, Eva Söderling, Peter Milgrom","doi":"10.1093/eurheartj/ehaf058","DOIUrl":"10.1093/eurheartj/ehaf058","url":null,"abstract":"","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":" ","pages":"2705-2706"},"PeriodicalIF":37.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12257477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}