{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T04:59:31Z","timestamp":1790312371271,"version":"4.1.0"},"reference-count":15,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T00:00:00Z","timestamp":1786233600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T00:00:00Z","timestamp":1786233600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Noninvasive Electrocardiol"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cardiac implantable electronic devices (CIEDs) frequently detect brief, often subclinical atrial fibrillation (AF), but their value for predicting progression to persistent AF remains uncertain. Statistical and machine learning (ML) approaches may enable dynamic risk stratification using this longitudinal device data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop a risk stratification model using clinical and CIED\u2010derived AF burden measured over a rolling 6\u2010month window to predict progression to persistent AF.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed continuous CIED data from 1985 patients without prior persistent AF implanted between 2016 and 2024 at a tertiary medical center. AF burden and clinical variables were summarized using overlapping 6\u2010month rolling windows to estimate 1\u2010year risk of persistent AF. Associations were evaluated using Kaplan\u2013Meier and Cox proportional hazards models. A gradient\u2010boosted decision tree model (XGBoost) was used to predict progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      During a mean follow\u2010up of 1192\u2009days, 874 patients (44%) developed paroxysmal AF, of whom 257 (29%) progressed to persistent AF after a mean of 813\u2009days. Patients with no AF or &lt;\u20091\u2009h\/day of AF in the prior 6\u2009months had &gt;\u200997% 1\u2010year freedom from persistent AF, whereas those with &gt;\u20098\u2009h\/day had a 63% progression rate. Higher AF burden was strongly associated with progression (maximum HR 8.66,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). The ML model demonstrated high predictive performance (sensitivity 99.4%, specificity 95.7%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>CIED\u2010detected AF burden is strongly associated with progression to persistent AF. ML\u2010based analysis of 6\u2010month device data enables accurate, point\u2010in\u2010time risk stratification to support earlier and more targeted clinical management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/anec.70229","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T05:24:35Z","timestamp":1786339475000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Risk Assessment for the Development of Persistent Atrial Fibrillation Using Statistical and Machine Learning Approaches"],"prefix":"10.1111","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5887-0424","authenticated-orcid":false,"given":"Alexei","family":"Nakonechnyi","sequence":"first","affiliation":[{"name":"University of Rochester Medical Center  Rochester New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4417-9289","authenticated-orcid":false,"given":"Shaul","family":"Geliaks","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center  Rochester New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilan","family":"Goldenberg","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center  Rochester New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7230-1927","authenticated-orcid":false,"given":"Dillon J.","family":"Dzikowicz","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center  Rochester New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emre","family":"Aktas","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center  Rochester New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan","family":"Schleede","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center  Rochester New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"McNitt","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center  Rochester New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agam","family":"Purohit","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center  Rochester New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Burr W.","family":"Hall","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center  Rochester New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Spencer Z.","family":"Rosero","sequence":"additional","affiliation":[{"name":"University of Rochester Medical Center  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