{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T01:51:33Z","timestamp":1791597093808,"version":"4.3.4"},"reference-count":28,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R01 HL161253"],"award-info":[{"award-number":["R01 HL161253"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Study Objectives<\/jats:title>\n                    <jats:p>Polysomnography (PSG) is the gold standard for diagnosing sleep disorders, which are recognized as cardiovascular risk factors. Single-lead electrocardiograms (ECGs) are recorded during PSG but rarely analyzed, presenting an opportunity to leverage overnight ECGs for cardiovascular risk prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We applied a deep residual neural network with attention to single-lead ECGs from PSG, combined with sleep stage data, to predict 10-year risk of atrial fibrillation (AF), stroke, myocardial infarction (MI), heart failure (HF), and death. The network, trained for arrhythmia detection, was finetuned on 15\u2009809 Massachusetts General Hospital patients. External validation used cohorts from Emory University Hospital (EUH) (n\u2009=\u20099810) and Beth Israel Deaconess Medical Center (BIDMC) (n\u2009=\u200912\u2009576), with outcomes derived from electronic health records (ICD-9\/10 codes).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Cox proportional hazards models showed that neural-network output (NN-output) was strongly associated with long-term cardiovascular risk (p\u2009&amp;lt;\u2009.0001), even after adjusting for risk factors (age, sex, body mass index, smoking, hypertension, diabetes) and sleep characteristics (apnea-hypopnea-index, arousal-index, periodic limb movement index, time spend in N1-N3, REM and sleep efficiency). A one-standard deviation increase in NN-output was associated with elevated hazard ratios (95% CI): AF 2.03 (1.82-2.27) (EUH) and 2.72 (2.31-3.21) (BIDMC); stroke 1.19 (1.09-1.31) and 1.40 (1.12-1.74); MI 1.38 (1.22-1.55) and 1.10 (0.86-1.40); HF 1.69 (1.55-1.85) and 2.33 (2.05-2.65); all-cause mortality 1.82 (1.60-2.06) and 1.65 (1.41-1.93).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Deep learning applied to nocturnal ECGs with sleep stage information captures cardiac abnormalities, offering a tool for cardiovascular risk stratification. Integrating such prediction into PSG could enhance detection and support personalized clinical decision-making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/sleep\/zsag229","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T11:20:15Z","timestamp":1788520815000},"source":"Crossref","is-referenced-by-count":0,"title":["Prediction of cardiovascular outcomes using electrocardiography from overnight polysomnography"],"prefix":"10.1093","author":[{"given":"Zuzana","family":"Koscova","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, School of Medicine, Emory University , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samaneh","family":"Nasiri","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, School of Medicine, Emory University , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4688-7965","authenticated-orcid":false,"given":"Matthew A","family":"Reyna","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, School of Medicine, Emory University , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neal K","family":"Bhatia","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Medicine, School of Medicine, Emory University , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Bahrami","family":"Rad","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, School of Medicine, Emory University , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4913-6825","authenticated-orcid":false,"given":"Reza","family":"Sameni","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, School of Medicine, Emory University , Atlanta, GA ,","place":["United States"]},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-8312","authenticated-orcid":false,"given":"Haoqi","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Neurology, Beth Israel Deaconess Medical Center , Boston, MA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niels","family":"Turley","sequence":"additional","affiliation":[{"name":"Department of Neurology, Beth Israel Deaconess Medical Center , Boston, MA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umakanth","family":"Katwa","sequence":"additional","affiliation":[{"name":"Boston Children\u2019s Hospital, Department of Internal Medicine , Boston, MA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dennis","family":"Hwang","sequence":"additional","affiliation":[{"name":"Kaiser Permanente San Bernardino County, Sleep Disorders Center , San Bernardino, CA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katie L","family":"Stone","sequence":"additional","affiliation":[{"name":"Sutter Health\/California Pacific Medical Center , Center for Aging Science, San Francisco, CA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2329-6847","authenticated-orcid":false,"given":"Lynn M","family":"Trotti","sequence":"additional","affiliation":[{"name":"Department of Neurology and Emory Sleep Center, School of Medicine, Emory University , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5575-3953","authenticated-orcid":false,"given":"Robert J","family":"Thomas","sequence":"additional","affiliation":[{"name":"Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center , Boston, MA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6928-5310","authenticated-orcid":false,"given":"Emmanuel","family":"Mignot","sequence":"additional","affiliation":[{"name":"Howard Hughes Medical Institute, Stanford University , Palo Alto, CA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4803-312X","authenticated-orcid":false,"given":"Brandon M","family":"Westover","sequence":"additional","affiliation":[{"name":"Department of Neurology, Beth Israel Deaconess Medical Center , Boston, MA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gari D","family":"Clifford","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, School of Medicine, Emory University , Atlanta, GA ,","place":["United States"]},{"name":"Department of Biomedical Engineering, Georgia Institute of Technology , Atlanta, GA ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"issue":"7","key":"2026100921332298900_ref1","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s11883-024-01207-5","article-title":"The role of sleep in cardiovascular disease","volume":"26","author":"Jaspan","year":"2024","journal-title":"Curr Atheroscler Rep"},{"issue":"6","key":"2026100921332298900_ref2","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1016\/j.jacc.2021.05.048","article-title":"Sleep disordered breathing and cardiovascular disease: JACC state-of-the-art review","volume":"78","author":"Cowie","year":"2021","journal-title":"J Am Coll Cardiol"},{"issue":"12","key":"2026100921332298900_ref3","doi-asserted-by":"publisher","DOI":"10.3390\/cells12121661","article-title":"Mechanisms of atrial fibrillation in obstructive sleep apnoea","volume":"12","author":"James","year":"2023","journal-title":"Cells"},{"key":"2026100921332298900_ref4","doi-asserted-by":"publisher","first-page":"467","DOI":"10.4022\/jafib.467","article-title":"Atrial fibrillation associated with heart failure, stroke and mortality","volume":"5","author":"Bordignon","year":"2012","journal-title":"J Atr Fibrillation"},{"issue":"21","key":"2026100921332298900_ref5","doi-asserted-by":"publisher","first-page":"e015926","DOI":"10.1161\/JAHA.120.015926","article-title":"Nocturnal arrhythmias and heart-rate swings in patients with obstructive sleep apnea syndrome treated with beta blockers","volume":"9","author":"Lombardi","year":"2020","journal-title":"J Am Heart Assoc"},{"issue":"4","key":"2026100921332298900_ref6","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1002\/clc.23984","article-title":"Association between insomnia and the incidence of myocardial infarction: a systematic review and meta-analysis","volume":"46","author":"Dean","year":"2023","journal-title":"Clin Cardiol"},{"key":"2026100921332298900_ref7","doi-asserted-by":"publisher","first-page":"e50","DOI":"10.1017\/S2045796019000441","article-title":"Depressive symptoms and sleep problems as risk factors for heart disease: a prospective community study","volume":"29","author":"Desch\u00eanes","year":"2019","journal-title":"Epidemiol Psychiatric Sci"},{"issue":"4","key":"2026100921332298900_ref8","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1111\/j.1365-2869.2011.00990.x","article-title":"Sleep disturbance is associated with cardio-vascular and metabolic disorders","volume":"21","author":"Grandner","year":"2012","journal-title":"J Sleep Res"},{"issue":"1","key":"2026100921332298900_ref9","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/S0022-3999(97)00302-4","article-title":"Chronic insomnia and activity of the stress system","volume":"45","author":"Vgontzas","year":"1998","journal-title":"J Psychosom Res"},{"key":"2026100921332298900_ref10","year":"2023"},{"key":"2026100921332298900_ref11","volume-title":"The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications (Version 2.5)","author":"Berry","year":"2018"},{"key":"2026100921332298900_ref12","volume-title":"2017 Computing in Cardiology (CinC 2017)","author":"Morteza"},{"key":"2026100921332298900_ref13","volume-title":"2021 Computing in Cardiology (CinC 2021)","author":"Petr"},{"key":"2026100921332298900_ref14","volume-title":"2021 Computing in Cardiology (CinC)","author":"Reyna Matthew"},{"key":"2026100921332298900_ref15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/9781118445112.stat06001.pub2","volume-title":"Wiley StatsRef: Statistics Reference Online","author":"\u00d8rnulf","year":"2018"},{"issue":"2","key":"2026100921332298900_ref16","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1177\/0962280208092301","article-title":"Multi-state models for the analysis of time-to-event data","volume":"18","author":"Lu\u00eds","year":"2009","journal-title":"Stat Methods Med Res"},{"issue":"1","key":"2026100921332298900_ref17","doi-asserted-by":"publisher","first-page":"e200225","DOI":"10.1212\/CPJ.0000000000200225","article-title":"Assessing risk of health outcomes from brain activity in sleep: a retrospective cohort study","volume":"14","author":"Haoqi","year":"2024","journal-title":"Neurol Clin Pract"},{"issue":"18","key":"2026100921332298900_ref18","doi-asserted-by":"publisher","first-page":"2543","DOI":"10.1001\/jama.1982.03320430047030","article-title":"Evaluating the yield of medical tests","volume":"247","author":"Harrell","year":"1982","journal-title":"JAMA"},{"issue":"2","key":"2026100921332298900_ref19","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/sim.4780030207","article-title":"Regression modeling strategies for improved prognostic prediction","volume":"3","author":"Harrell","year":"1984","journal-title":"Stat Med"},{"issue":"7","key":"2026100921332298900_ref20","doi-asserted-by":"publisher","first-page":"e023237","DOI":"10.1161\/JAHA.121.023237","article-title":"Identification of incident atrial fibrillation from electronic medical records","volume":"11","author":"Chamberlain","year":"2022","journal-title":"J Am Heart Assoc"},{"issue":"6","key":"2026100921332298900_ref21","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1161\/CIRCEP.112.972224","article-title":"Effects of postmenopausal hormone therapy on incident atrial fibrillation: the Women\u2019s Health Initiative randomized controlled trials","volume":"5","author":"Perez","year":"2012","journal-title":"Circ Arrhythm Electrophysiol"},{"issue":"18","key":"2026100921332298900_ref22","doi-asserted-by":"publisher","first-page":"e895","DOI":"10.1161\/CIR.0000000000001063","article-title":"2022 AHA\/ACC\/HFSA guideline for the Management of Heart Failure: a report of the American College of Cardiology\/American Heart Association joint committee on clinical practice guidelines","volume":"145","author":"Heidenreich","year":"2022","journal-title":"Circulation."},{"key":"2026100921332298900_ref23","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.amjcard.2021.08.042","article-title":"Prevention and treatment of atrial fibrillation via risk factor modification","volume":"160","author":"O\u2019Keefe","year":"2021","journal-title":"Am J Cardiol"},{"issue":"11","key":"2026100921332298900_ref24","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1093\/eurheartj\/ehae914","article-title":"Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study","volume":"46","author":"Dhingra Lovedeep","year":"2025","journal-title":"Eur Heart J"},{"issue":"46","key":"2026100921332298900_ref25","doi-asserted-by":"publisher","first-page":"4920","DOI":"10.1093\/eurheartj\/ehae595","article-title":"Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores","volume":"45","author":"Gilbert","year":"2024","journal-title":"Eur Heart J"},{"issue":"1","key":"2026100921332298900_ref26","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1038\/s41746-023-00966-w","article-title":"Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias","volume":"6","author":"Matteo","year":"2023","journal-title":"NPJ Digit Med"},{"issue":"1","key":"2026100921332298900_ref27","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1038\/s44321-025-00351-y","article-title":"Artificial intelligence-enabled electrocardiography from scientific research to clinical application","volume":"18","author":"Lin","year":"2025","journal-title":"EMBO Mol Med"},{"issue":"4","key":"2026100921332298900_ref28","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1016\/j.mcpdig.2024.10.001","article-title":"Cost-effectiveness of artificial intelligence-enabled electrocardiograms for early detection of low ejection fraction: a secondary analysis of the electrocardiogram artificial intelligence-guided screening for low ejection fraction trial","volume":"2","author":"Thao","year":"2024","journal-title":"Mayo Clin Proc Digit Health"}],"container-title":["SLEEP"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/sleep\/advance-article-pdf\/doi\/10.1093\/sleep\/zsag229\/71719579\/zsag229.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/sleep\/advance-article-pdf\/doi\/10.1093\/sleep\/zsag229\/71719579\/zsag229.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T01:33:27Z","timestamp":1791596007000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/sleep\/advance-article\/doi\/10.1093\/sleep\/zsag229\/8885715"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,9]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1093\/sleep\/zsag229","relation":{},"ISSN":["0161-8105","1550-9109"],"issn-type":[{"value":"0161-8105","type":"print"},{"value":"1550-9109","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,9]]},"article-number":"zsag229"}}