{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:01:18Z","timestamp":1792785678293,"version":"4.5.0"},"reference-count":41,"publisher":"IOP Publishing","issue":"9","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"name":"ARAID","award":["T39-23R"],"award-info":[{"award-number":["T39-23R"]}],"id":[{"id":"https:\/\/ror.org\/007bpwb04","id-type":"ROR","asserted-by":"crossref"}]},{"name":"MICINN","award":["PID2022- 140556OB-I00"],"award-info":[{"award-number":["PID2022- 140556OB-I00"]}],"id":[{"id":"https:\/\/ror.org\/05r0vyz12","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Marie Sk\u0142odowska Curie","award":["101119941"],"award-info":[{"award-number":["101119941"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2026,9,30]]},"abstract":"Abstract<\/jats:title>\n \n Objective.<\/jats:italic>\n Cardiovascular disease remains one of the leading causes of morbidity and mortality worldwide, highlighting the need for accurate risk prediction. Atrial fibrillation burden (AFB), premature ventricular contraction burden (PVCB), and T-wave alternans (TWA) have been individually associated with adverse outcomes. We hypothesized that these three digital ECG biomarkers provide complementary prognostic information for major cardiovascular endpoints and all-cause mortality (ACM), and developed a machine learning approach combining these biomarkers for risk prediction.\n Approach.<\/jats:italic>\n We analyzed 81 362 Holter recordings from 54 395 individuals across 20 primary care centers in Israel. A random forest model using AFB, PVCB, TWA, and age was trained to predict 5 year risk of heart failure (HF), ischemic stroke (IS), and ACM.\n Main results.<\/jats:italic>\n On the test set, best AUROCs were for HF 0.75 [95% CI: 0.74\u20130.77] (\n \n \n \n <\/jats:tex-math>\n \n \n \n n<\/mml:mi>\n \n pos<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n =<\/mml:mo>\n 622<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ), for IS 0.69 [0.66\u20130.71] (\n \n \n \n <\/jats:tex-math>\n \n \n \n n<\/mml:mi>\n \n pos<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n =<\/mml:mo>\n 365<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ), and for ACM 0.79 [0.77\u20130.80] (\n \n \n \n <\/jats:tex-math>\n \n \n \n n<\/mml:mi>\n \n pos<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n =<\/mml:mo>\n 1060<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ). Combining the three ECG-derived biomarkers showed complementary predictive value and improved discrimination by up to 10% over age alone in individuals aged\n \n \n \n <\/jats:tex-math>\n \n \n <<\/mml:mo>\n 75<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n years.\n Significance.<\/jats:italic>\n AFB, PVCB, and TWA contribute complementary prognostic information for risk stratification. When combined with age, these biomarkers improve predictive performance compared with age alone.\n <\/jats:p>","DOI":"10.1088\/1361-6579\/aea9ec","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T22:53:59Z","timestamp":1789772039000},"page":"095011","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Single-lead ECG biomarkers for cardiovascular and mortality risk prediction"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6003-2145","authenticated-orcid":true,"given":"Lisa","family":"Attali","sequence":"first","affiliation":[{"name":"Technion-IIT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3987-1201","authenticated-orcid":true,"given":"Yosef","family":"Solewicz","sequence":"additional","affiliation":[{"name":"Technion-IIT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shany","family":"Brimer Biton","sequence":"additional","affiliation":[{"name":"Technion-IIT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2162-8513","authenticated-orcid":true,"given":"Hagai","family":"Hamami","sequence":"additional","affiliation":[{"name":"Technion-IIT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eran","family":"Zvuloni","sequence":"additional","affiliation":[{"name":"Technion-IIT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9435-7152","authenticated-orcid":true,"given":"Ilan","family":"Green","sequence":"additional","affiliation":[{"name":"Leumit Health Services, Tel Aviv-Yafo"},{"name":"Tel Aviv University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Izhar","family":"Laufer","sequence":"additional","affiliation":[{"name":"Leumit Health Services, Tel Aviv-Yafo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3434-9254","authenticated-orcid":true,"given":"Pablo","family":"Laguna","sequence":"additional","affiliation":[{"name":"BSICoS Group, Arag\u00f3n Institute of Engineering Research (I3A), IIS Arag\u00f3n, University of Zaragoza"},{"name":"CIBER-BBN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alba","family":"Mart\u00edn-Yebra","sequence":"additional","affiliation":[{"name":"BSICoS Group, Arag\u00f3n Institute of Engineering Research (I3A), IIS Arag\u00f3n, University of Zaragoza"},{"name":"CIBER-BBN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7503-3339","authenticated-orcid":true,"given":"Juan","family":"Pablo Mart\u00ednez","sequence":"additional","affiliation":[{"name":"BSICoS Group, Arag\u00f3n Institute of Engineering Research (I3A), IIS Arag\u00f3n, University of Zaragoza"},{"name":"CIBER-BBN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronit","family":"Almog","sequence":"additional","affiliation":[{"name":"Technion-IIT"},{"name":"Epidemiology Unit, Rambam Health Care Campus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5956-7034","authenticated-orcid":true,"given":"Joachim A","family":"Behar","sequence":"additional","affiliation":[{"name":"Technion-IIT"},{"name":"Technion-IIT"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"pmeaaea9ecbib1","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1161\/STROKEAHA.109.567800","type":"journal-article","article-title":"Premature ventricular complexes and risk of incident stroke: the atherosclerosis risk in communities (ARIC) study","volume":"41","author":"Agarwal","year":"2010","journal-title":"Stroke"},{"key":"pmeaaea9ecbib2","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1161\/STROKEAHA.114.008447","type":"journal-article","article-title":"Premature ventricular complexes on screening electrocardiogram and risk of ischemic stroke","volume":"46","author":"Agarwal","year":"2015","journal-title":"Stroke"},{"key":"pmeaaea9ecbib3","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1093\/ehjdh\/ztab080","type":"journal-article","article-title":"ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure","volume":"2","author":"Akbilgic","year":"2021","journal-title":"Eur. 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Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2026-04-05","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-18","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-30","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}