{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T20:20:37Z","timestamp":1788898837552,"version":"build-2803163510"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Artificial intelligence electrocardiography (AI-ECG) algorithms are emerging tools for identifying individuals at risk of atrial fibrillation (AF). We evaluated the predictive performance of a validated AI-ECG algorithm for incident AF in UK Biobank participants with AF risk factors, irrespective of prevalent cardiovascular disease, and its incremental value when added to clinical predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>The AI-ECG tool was applied to sinus rhythm ECGs from UK-Biobank participants with risk factors for AF but no AF. Model performance was evaluated using time-dependent ROC-AUC and Harrell\u2019s C-index. Multivariable Cox regression was used to identify clinical risk factors associated with incident AF and to quantify the contribution of AI-ECG. A total of 21 842 participants (56% male) were included. The median follow-up time was 3.7 years (IQR 0.5\u20135.4) The ECG-AI tool achieved a ROC-AUC of 0.73 (95% CI 0.68\u20130.78) at 1 and 0.69 (95% CI 0.66\u20130.72) at 3 years. A multivariable Cox regression model using clinical parameters achieved a ROC-AUC of 0.71 (95% CI 0.66\u20130.75) at 1 and 0.71 (95% CI 0.68\u20130.74) at 3 years. By adding ECG-AI to the clinical Cox regression model, the ROC-AUC increased to 0.75 (95%CI 0.71\u20130.80) at 1 and 0.74 (95% CI 0.71\u20130.77) at 3 years. AI-ECG showed a hazard ratio of 1.23 per decile increase (95% CI 1.18\u20131.27).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An AI-ECG algorithm improved the prediction of incident AF when added to a clinical parameter-based model over a median follow-up time of 3.7 years among individuals with comorbidities predisposing to AF who may benefit from targeted screening and preventive strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztag025","type":"journal-article","created":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T12:36:10Z","timestamp":1770467770000},"source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence 12-lead electrocardiography to determine atrial fibrillation risk among UK Biobank participants with predisposing conditions"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7564-1775","authenticated-orcid":false,"given":"Yi","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Cardiology, Bern University Hospital, Inselspital, University of Bern , Bern ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos C","family":"Siontis","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , Rochester, MN ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zachi I","family":"Attia","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , Rochester, MN ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isaac","family":"Shiri","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Bern University Hospital, Inselspital, University of Bern , Bern ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0827-1329","authenticated-orcid":false,"given":"Laurent","family":"Roten","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Bern University Hospital, Inselspital, University of Bern , Bern ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Baj","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Bern University Hospital, Inselspital, University of Bern , Bern ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pooya Mohammadi","family":"Kazaj","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Bern University Hospital, Inselspital, University of Bern , Bern ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5788-9734","authenticated-orcid":false,"given":"Francisco","family":"Lopez-Jimenez","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , Rochester, MN ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernard J","family":"Gersh","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , Rochester, MN ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6029-0597","authenticated-orcid":false,"given":"Christoph","family":"Gr\u00e4ni","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Bern University Hospital, Inselspital, University of Bern , Bern ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7197-8415","authenticated-orcid":false,"given":"Tobias","family":"Reichlin","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Bern University Hospital, Inselspital, University of Bern , Bern ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul A","family":"Friedman","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , Rochester, MN ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2653-6762","authenticated-orcid":false,"given":"Stephan","family":"Windecker","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Bern University Hospital, Inselspital, University of Bern , Bern ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4308-0456","authenticated-orcid":false,"given":"Peter A","family":"Noseworthy","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , Rochester, MN ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2128-9205","authenticated-orcid":false,"given":"George C M","family":"Siontis","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Bern University Hospital, Inselspital, University of Bern , Bern ,","place":["Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"2026031018380053200_ztag025-B1","doi-asserted-by":"crossref","first-page":"e944","DOI":"10.1161\/CIR.0000000000000740","article-title":"Subclinical and device-detected atrial fibrillation: pondering the knowledge gap: a scientific statement from the American Heart Association","volume":"140","author":"Noseworthy","year":"2019","journal-title":"Circulation"},{"key":"2026031018380053200_ztag025-B2","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1016\/j.amjcard.2013.05.063","article-title":"Estimates of current and future incidence and prevalence of atrial fibrillation in the U.S. adult population","volume":"112","author":"Colilla","year":"2013","journal-title":"Am J Cardiol"},{"key":"2026031018380053200_ztag025-B3","doi-asserted-by":"crossref","first-page":"107843","DOI":"10.1016\/j.diabres.2019.107843","article-title":"Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9(th) edition","volume":"157","author":"Saeedi","year":"2019","journal-title":"Diabetes Res Clin Pract"},{"key":"2026031018380053200_ztag025-B4","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s11910-018-0871-1","article-title":"Detection of atrial fibrillation in cryptogenic stroke","volume":"18","author":"Haeusler","year":"2018","journal-title":"Curr Neurol Neurosci Rep"},{"key":"2026031018380053200_ztag025-B5","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.jchf.2016.12.011","article-title":"A multisensor algorithm predicts heart failure events in patients with implanted devices","volume":"5","author":"Boehmer","year":"2017","journal-title":"JACC Heart Fail"},{"key":"2026031018380053200_ztag025-B6","doi-asserted-by":"crossref","first-page":"4291","DOI":"10.1093\/eurheartj\/ehae465","article-title":"Artificial intelligence in cardiovascular medicine: clinical applications","volume":"45","author":"L\u00fcscher","year":"2024","journal-title":"Eur Heart J"},{"key":"2026031018380053200_ztag025-B7","doi-asserted-by":"crossref","first-page":"e100466","DOI":"10.1136\/bmjhci-2021-100466","article-title":"Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials","volume":"28","author":"Siontis","year":"2021","journal-title":"BMJ Health Care Inform"},{"key":"2026031018380053200_ztag025-B8","doi-asserted-by":"crossref","first-page":"e367","DOI":"10.1016\/S2589-7500(24)00047-5","article-title":"Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review","volume":"6","author":"Han","year":"2024","journal-title":"Lancet Digit Health"},{"key":"2026031018380053200_ztag025-B9","doi-asserted-by":"crossref","first-page":"e258052","DOI":"10.1001\/jamanetworkopen.2025.8052","article-title":"Generalizability of FDA-approved AI-enabled medical devices for clinical use","volume":"8","author":"Windecker","year":"2025","journal-title":"JAMA Netw Open"},{"key":"2026031018380053200_ztag025-B10","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1016\/S0140-6736(22)01637-3","article-title":"Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial","volume":"400","author":"Noseworthy","year":"2022","journal-title":"Lancet"},{"key":"2026031018380053200_ztag025-B11","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.ajem.2022.04.032","article-title":"Evaluating atrial fibrillation artificial intelligence for the ED: statistical and clinical implications","volume":"57","author":"Kaminski","year":"2022","journal-title":"Am J Emerg Med"},{"key":"2026031018380053200_ztag025-B12","author":"Artificial intelligence\u2014electrocardiography to detect atrial fibrillation: trend of probability before and after the first episode | European Heart Journal\u2014Digital Health | Oxford Academic"},{"key":"2026031018380053200_ztag025-B13","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.jaccao.2024.02.006","article-title":"Artificial intelligence electrocardiography to predict atrial fibrillation in patients with chronic lymphocytic leukemia","volume":"6","author":"Christopoulos","year":"2024","journal-title":"JACC CardioOncol"},{"key":"2026031018380053200_ztag025-B14","doi-asserted-by":"crossref","first-page":"e008153","DOI":"10.1161\/CIRCOUTCOMES.121.008153","article-title":"Use of artificial intelligence tools across different clinical settings","volume":"14","author":"Siontis","year":"2021","journal-title":"Circ Cardiovasc Qual Outcomes"},{"key":"2026031018380053200_ztag025-B15","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","article-title":"An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction","volume":"394","author":"Attia","year":"2019","journal-title":"Lancet"},{"key":"2026031018380053200_ztag025-B16","doi-asserted-by":"crossref","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":"Jabbour","year":"2024","journal-title":"Eur Heart J"},{"key":"2026031018380053200_ztag025-B17","doi-asserted-by":"crossref","first-page":"12818","DOI":"10.1038\/s41598-021-92172-5","article-title":"A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm","volume":"11","author":"Baek","year":"2021","journal-title":"Sci Rep"},{"key":"2026031018380053200_ztag025-B18","doi-asserted-by":"crossref","first-page":"1","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":"Gadaleta","year":"2023","journal-title":"NPJ Digit Med"},{"key":"2026031018380053200_ztag025-B19","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1161\/CIRCULATIONAHA.121.057480","article-title":"ECG-Based deep learning and clinical risk factors to predict atrial fibrillation","volume":"145","author":"Khurshid","year":"2022","journal-title":"Circulation"},{"key":"2026031018380053200_ztag025-B20","doi-asserted-by":"crossref","first-page":"ii52","DOI":"10.1093\/europace\/euaa346","article-title":"Risk factor management and atrial fibrillation","volume":"23","author":"Hendriks","year":"2021","journal-title":"Europace"},{"key":"2026031018380053200_ztag025-B21","doi-asserted-by":"crossref","first-page":"e009355","DOI":"10.1161\/CIRCEP.120.009355","article-title":"Artificial intelligence\u2013electrocardiography to predict incident atrial fibrillation","volume":"13","author":"Christopoulos","year":"2020","journal-title":"Circ Arrhythm Electrophysiol"},{"key":"2026031018380053200_ztag025-B22","doi-asserted-by":"crossref","first-page":"e296","DOI":"10.1371\/journal.pmed.0040296","article-title":"The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies","volume":"4","author":"von Elm","year":"2007","journal-title":"PLoS Med"},{"key":"2026031018380053200_ztag025-B23","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"2026031018380053200_ztag025-B24","doi-asserted-by":"crossref","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","article-title":"UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age","volume":"12","author":"Sudlow","year":"2015","journal-title":"PLoS Med"},{"key":"2026031018380053200_ztag025-B25","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1136\/jamia.2009.001230","article-title":"International classification of diseases, 10th edition, clinical modification and procedure coding system: descriptive overview of the next generation HIPAA code sets","volume":"17","author":"Steindel","year":"2010","journal-title":"J Am Med Inform Assoc"},{"key":"2026031018380053200_ztag025-B26","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1093\/eurheartj\/ehs314","article-title":"The CHA2DS2-VASc score identifies those patients with atrial fibrillation and a CHADS2 score of 1 who are unlikely to benefit from oral anticoagulant therapy","volume":"34","author":"Coppens","year":"2013","journal-title":"Eur Heart J"},{"key":"2026031018380053200_ztag025-B27","author":"2024 ESC Guidelines for the management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS) | European Heart Journal | Oxford Academic"},{"key":"2026031018380053200_ztag025-B28","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1161\/CIR.0000000000001193","article-title":"2023 ACC\/AHA\/ACCP\/HRS guideline for the diagnosis and management of atrial fibrillation: a report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines","volume":"149","author":"Joglar","year":"2024","journal-title":"Circulation"},{"key":"2026031018380053200_ztag025-B29","doi-asserted-by":"crossref","first-page":"2523","DOI":"10.2147\/COPD.S143691","article-title":"Incidence and risk factors of atrial fibrillation in Asian COPD patients","volume":"12","author":"Liao","year":"2017","journal-title":"COPD"},{"key":"2026031018380053200_ztag025-B30","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.ccep.2020.11.007","article-title":"Atrial fibrillation in valvular heart disease","volume":"13","author":"John","year":"2021","journal-title":"Card Electrophysiol Clin"},{"key":"2026031018380053200_ztag025-B31","doi-asserted-by":"crossref","first-page":"e000102","DOI":"10.1161\/JAHA.112.000102","article-title":"Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF Consortium","volume":"2","author":"Alonso","year":"2013","journal-title":"J Am Heart Assoc"},{"key":"2026031018380053200_ztag025-B32","doi-asserted-by":"crossref","first-page":"e006273","DOI":"10.1161\/CIRCEP.118.006273","article-title":"Frequency of cardiac rhythm abnormalities in a half million adults","volume":"11","author":"Khurshid","year":"2018","journal-title":"Circ Arrhythm Electrophysiol"},{"key":"2026031018380053200_ztag025-B33","author":"UK Biobank"},{"key":"2026031018380053200_ztag025-B34","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1002\/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3","article-title":"The lasso method for variable selection in the Cox model","volume":"16","author":"Tibshirani","year":"1997","journal-title":"Stat Med"},{"key":"2026031018380053200_ztag025-B35","first-page":"1","article-title":"Mice: multivariate imputation by chained equations in R","volume":"45","author":"van Buuren","year":"2011","journal-title":"J Stat Soft"},{"key":"2026031018380053200_ztag025-B36","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1007\/BF02924688","article-title":"Rubin, D.B.: multiple imputation for nonresponse in surveys","volume":"31","author":"Toutenburg","year":"1990","journal-title":"Stat Papers"},{"key":"2026031018380053200_ztag025-B37","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1016\/j.chest.2018.07.040","article-title":"Antithrombotic therapy for atrial fibrillation: CHEST guideline and expert panel report","volume":"154","author":"Lip","year":"2018","journal-title":"Chest"},{"key":"2026031018380053200_ztag025-B38","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1056\/NEJMoa1802686","article-title":"Rivaroxaban for stroke prevention after embolic stroke of undetermined source","volume":"378","author":"Hart","year":"2018","journal-title":"N Engl J Med"},{"key":"2026031018380053200_ztag025-B39","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1161\/STROKEAHA.113.003389","article-title":"Prolonged cardiac monitoring for detection of paroxysmal atrial fibrillation after cerebral ischemia","volume":"45","author":"Rabinstein","year":"2014","journal-title":"Stroke"},{"key":"2026031018380053200_ztag025-B40","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1161\/CIRCULATIONAHA.111.029801","article-title":"Prolonged rhythm monitoring for the detection of occult paroxysmal atrial fibrillation in ischemic stroke of unknown cause","volume":"124","author":"Seet","year":"2011","journal-title":"Circulation"},{"key":"2026031018380053200_ztag025-B41","doi-asserted-by":"crossref","first-page":"e009355","DOI":"10.1161\/CIRCEP.120.009355","article-title":"Artificial intelligence-electrocardiography to predict incident atrial fibrillation: a population-based study","volume":"13","author":"Christopoulos","year":"2020","journal-title":"Circ Arrhythm Electrophysiol"},{"key":"2026031018380053200_ztag025-B42","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jclinepi.2014.09.007","article-title":"External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination","volume":"68","author":"Siontis","year":"2015","journal-title":"J Clin Epidemiol"},{"key":"2026031018380053200_ztag025-B43","doi-asserted-by":"crossref","first-page":"e003355","DOI":"10.1161\/CIRCGEN.121.003355","article-title":"Predictive accuracy of a clinical and genetic risk model for atrial fibrillation","volume":"14","author":"Khurshid","year":"2021","journal-title":"Circ Genom Precis Med"},{"key":"2026031018380053200_ztag025-B44","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1038\/s41562-023-01579-9","article-title":"Participation bias in the UK Biobank distorts genetic associations and downstream analyses","volume":"7","author":"Schoeler","year":"2023","journal-title":"Nat Hum Behav"},{"key":"2026031018380053200_ztag025-B45","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1093\/europace\/euaa334","article-title":"Risk prediction of atrial fibrillation in the community combining biomarkers and genetics","volume":"23","author":"B\u00f6rschel","year":"2021","journal-title":"Europace"},{"key":"2026031018380053200_ztag025-B46","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1016\/j.echo.2019.07.003","article-title":"Echocardiographic risk assessment to guide screening for atrial fibrillation","volume":"32","author":"Ramkumar","year":"2019","journal-title":"J Am Soc Echocardiogr"}],"container-title":["European Heart Journal - Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ehjdh\/advance-article-pdf\/doi\/10.1093\/ehjdh\/ztag025\/66845175\/ztag025.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/7\/3\/ztag025\/66845175\/ztag025.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/7\/3\/ztag025\/66845175\/ztag025.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T22:38:12Z","timestamp":1773182292000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ehjdh\/article\/doi\/10.1093\/ehjdh\/ztag025\/8471700"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,11]]},"references-count":46,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/ehjdh\/ztag025","relation":{},"ISSN":["2634-3916"],"issn-type":[{"value":"2634-3916","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,4]]},"published":{"date-parts":[[2026,2,11]]},"article-number":"ztag025"}}