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Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Early identification of atrial fibrillation (AF) can reduce the risk of stroke, heart failure, and other serious cardiovascular outcomes. However, paroxysmal AF may not be detected even after a two-week continuous monitoring period. We developed a model to quantify the risk of near-term AF in a two-week period, based on AF-free ECG intervals of up to 24\u2009h from 459,889 patch-based ambulatory single-lead ECG (modified lead II) recordings of up to 14 days. A deep learning model was used to integrate ECG morphology data with demographic and heart rhythm features toward AF prediction. Observing a 1-day AF-free ECG recording, the model with deep learning features produced the most accurate prediction of near-term AF with an area under the curve AUC\u2009=\u20090.80 (95% confidence interval, CI\u2009=\u20090.79\u20130.81), significantly improving discrimination compared to demographic metrics alone (AUC 0.67; CI\u2009=\u20090.66\u20130.68). Our model was able to predict incident AF over a two-week time frame with high discrimination, based on AF-free single-lead ECG recordings of various lengths. Application of the model may enable a digital strategy for improving diagnostic capture of AF by risk stratifying individuals with AF-negative ambulatory monitoring for prolonged or recurrent monitoring, potentially leading to more rapid initiation of treatment.<\/jats:p>","DOI":"10.1038\/s41746-023-00966-w","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T09:21:13Z","timestamp":1702372873000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6470-6537","authenticated-orcid":false,"given":"Matteo","family":"Gadaleta","sequence":"first","affiliation":[]},{"given":"Patrick","family":"Harrington","sequence":"additional","affiliation":[]},{"given":"Eric","family":"Barnhill","sequence":"additional","affiliation":[]},{"given":"Evangelos","family":"Hytopoulos","sequence":"additional","affiliation":[]},{"given":"Mintu P.","family":"Turakhia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9256-7914","authenticated-orcid":false,"given":"Steven R.","family":"Steinhubl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2208-7912","authenticated-orcid":false,"given":"Giorgio","family":"Quer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"966_CR1","first-page":"1040","volume":"8","author":"MP Turakhia","year":"2015","unstructured":"Turakhia, M. 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S.R.S. is a consultant for PhysIQ. The other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"229"}}