{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T01:50:35Z","timestamp":1793065835061,"version":"4.5.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000274","name":"British Heart Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000274","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013342","name":"NIHR Imperial BRC","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013342","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Aims<\/jats:title>\n                  <jats:p>Implantable loop recorders (ILRs) provide continuous single-lead ambulatory electrocardiogram (aECG) monitoring. Whether these aECGs could be used to identify worsening heart failure (HF) is unknown.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods and results<\/jats:title>\n                  <jats:p>We linked ILR aECG from Medtronic device database to the left ventricular ejection fraction (LVEF) measurements in Optum\u00ae de-identified electronic health record dataset. We trained an artificial intelligence (AI) algorithm [aECG-convolutional neural network (CNN)] on a dataset of 35 741 aECGs from 2247 patients to identify LVEF \u2264 40% and assessed its performance using the area under the receiver operating characteristic curve. Ambulatory electrocardiogram-CNN was then used to identify patients with increasing risk of HF hospitalization in a real-world cohort of 909 patients with prior HF diagnosis. This dataset provided 12 467 follow-up monthly evaluations, with 201 HF hospitalizations. For every month, time-series features from these predictions were used to categorize patients into high- and low-risk groups and predict HF hospitalization in the next month. The risk of HF hospitalization in the next 30 days was significantly higher in the cohort that aECG-CNN identified as high risk [hazard ratio (HR) 1.89; 95% confidence interval (CI) 1.28\u20132.79; P = 0.001] compared with low risk, even after adjusting patient demographics (HR 1.88; 95% CI 1.27\u20132.79 P = 0.002).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>An AI algorithm trained to detect LVEF \u226440% using ILR aECGs can also readily identify patients at increased risk of HF hospitalizations by monitoring changes in the probability of HF over 30 days.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztae035","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T15:09:01Z","timestamp":1715180941000},"page":"435-443","source":"Crossref","is-referenced-by-count":6,"title":["Dynamic risk stratification of worsening heart failure using a deep learning-enabled implanted ambulatory single-lead electrocardiogram"],"prefix":"10.1093","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9989-6331","authenticated-orcid":false,"given":"James Philip","family":"Howard","sequence":"first","affiliation":[{"name":"National Heart and Lung Institute, Imperial College London , Du Cane Road, W12 0HS, London , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neethu","family":"Vasudevan","sequence":"additional","affiliation":[{"name":"Research and Technology, Cardiac Rhythm Management, Medtronic Inc. , Minneapolis, MN , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shantanu","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Research and Technology, Cardiac Rhythm Management, Medtronic Inc. , Minneapolis, MN , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"Landman","sequence":"additional","affiliation":[{"name":"Research and 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