{"id":62169,"date":"2025-11-09T16:03:34","date_gmt":"2025-11-09T15:03:34","guid":{"rendered":"https:\/\/pharma-trend.com\/en\/anumana-advances-ai-driven-cardiovascular-science-with-late-breaking-heart-failure-study-and-multiple-abstracts-at-aha-2025\/"},"modified":"2025-11-09T16:03:34","modified_gmt":"2025-11-09T15:03:34","slug":"anumana-advances-ai-driven-cardiovascular-science-with-late-breaking-heart-failure-study-and-multiple-abstracts-at-aha-2025","status":"publish","type":"post","link":"https:\/\/pharma-trend.com\/en\/anumana-advances-ai-driven-cardiovascular-science-with-late-breaking-heart-failure-study-and-multiple-abstracts-at-aha-2025\/","title":{"rendered":"Anumana Advances AI-Driven Cardiovascular Science with Late-Breaking Heart Failure Study and Multiple Abstracts at AHA 2025"},"content":{"rendered":"
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\nSimultaneous publication in Journal of the American College of Cardiology demonstrates impact of ECG-AI on heart failure risk assessment<\/i><\/p>\n

CAMBRIDGE, Mass.–(BUSINESS WIRE)–Anumana<\/a>, a leader in AI-powered cardiovascular diagnostics, today announced new groundbreaking clinical data presented at the American Heart Association (AHA) Scientific Sessions 2025. Among the studies highlighted was a late-breaking featured science presentation, published simultaneously in the Journal of the American College of Cardiology<\/i><\/a>, showing that AI applied to the electrocardiogram (ECG-AI) can enhance near-term prediction of incident heart failure beyond established clinical risk models. Anumana also has three additional abstracts being presented at the meeting.<\/p>\n

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\nThe featured study, \u201cEnhanced Prediction of Incident Heart Failure Using Artificial Intelligence-Driven Analysis of 12-Lead Electrocardiogram Waveforms: A HeartShare\/AMP-HF Pooled Cohort Analysis<\/i>,\u201d analyzed data from more than 14,000 participants across three major longitudinal cohorts\u2014the Framingham Heart Study<\/a>, Multi-Ethnic Study of Atherosclerosis<\/a>, and Cardiovascular Health Study<\/a>. Investigators found that integrating Anumana\u2019s ECG-AI with the PREVENT-HF clinical risk equation significantly improved short-term heart failure risk prediction, reclassifying up to 12.5% of individuals into higher-risk categories not identified by clinical factors alone. Participants with positive ECG-AI results were more than 20 times as likely to develop heart failure within three years as those with negative results.<\/p>\n

\n\u201cAI analysis of standard 12-lead electrocardiograms (ECG-AI) allows detection of subtle electrical changes that signal early cardiac dysfunction. The findings from this analysis suggest that ECG-AI can enhance detection of patients at risk for development of HF when added to standard clinical risk assessment with the PREVENT-HF score,\u201d said Akshay S. Desai, MD, MPH, Director of the Heart Failure Disease Management Program, Brigham and Women\u2019s Hospital, and lead study investigator. \u201cThe implication is that ECG-AI may help clinicians to identify at-risk patients years before symptoms of HF appear, creating opportunities to start preventive therapy sooner and improve long-term outcomes.\u201d<\/p>\n

\nThe study was conducted with the National Heart, Lung, and Blood Institute\u2019s HeartShare\/AMP Heart Failure Program<\/a> using the BioData Catalyst platform<\/a>, which accelerates reproducible biomedical research to drive scientific advances. The collaboration enabled access to deeply phenotyped, longitudinal cohorts and robust analytic capabilities, supporting a rigorous evaluation of ECG-AI for predicting early heart failure risk.<\/p>\n

\n\u201cThis publication marks meaningful progress toward a future where AI helps prevent disease rather than just detecting it,\u201d said Simos Kedikoglou, MD, President and COO of Anumana. \u201cIt demonstrates how our ECG-AI LEF algorithm can uncover early signs of heart failure, supporting clinicians in identifying at-risk patients sooner and enabling more proactive care.\u201d<\/p>\n

\nAnumana\u2019s ECG-AI\u2122 LEF algorithm analyzes standard 12-lead ECGs to identify patients with LEF, a key indicator of heart failure. The algorithm achieved an AUC of 0.944, with a sensitivity of 90.2% and specificity of 85.1%, reflecting its strong ability to detect patients at risk for heart failure.<\/p>\n

\nIn addition to the featured science, Anumana presented three abstracts highlighting the versatility of AI across cardiovascular conditions:<\/p>\n