{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T19:34:47Z","timestamp":1790710487455,"version":"4.1.0"},"reference-count":31,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"11","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004374","name":"Medtronic, Inc.","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100004374","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circ: Arrhythmia and Electrophysiology"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:sec>\n            <jats:title>BACKGROUND:<\/jats:title>\n            <jats:p>Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertable cardiac monitors (ICMs), may be useful in predicting near-term CVH.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>METHODS:<\/jats:title>\n            <jats:p>Using Optum\u2019s deidentified Clinformatics Data Mart Database (2007\u20132019), linked with the Medtronic CareLink ICM database, we identified patients with &gt;1 days of ICM-detected atrial fibrillation. ICM-detected diagnostic parameters were transformed into simple moving averages over different periods for daily follow-up. A diagnostic trend was defined as the comparison of 2 simple moving averages of different periods for each diagnostic parameter. CVH was defined as any hospital, emergency department, or ambulatory surgical center encounter with a cardiovascular diagnosis-related group or diagnosis code. Machine learning was used to determine which diagnostic trends could best predict patient risk 5 days before CVH.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>RESULTS:<\/jats:title>\n            <jats:p>\n              A total of 2616 patients with ICMs met the inclusion criteria (71\u00b111 years; 55% male). Among them, 1998 (76%) had a planned or unplanned CVH over 605\u2005363 days. Machine learning revealed distinct groups: (A) sinus rhythm (reference), (B) below-average burden, (C) above-average burden, and (D) above-average burden with decreasing patient activity. The relative risk was increased in all groups versus the reference (B, 4.49 [95% CI, 3.74\u20135.40]; C, 8.41 [95% CI, 7.00\u201310.11]; D, 11.15 [95% CI, 9.10\u201313.65]), including a 21% increase in CVH detection over prespecified burden thresholds of duration (\u22651 hour) and quantity (\u22655%). The area under the receiver operating characteristic curve increased from 0.55 when using hourly burden amounts to 0.66 when using burden trends and decreasing patient activity (\n              <jats:italic toggle=\"yes\">P<\/jats:italic>\n              &lt;0.001), a 20% increase in predictive power.\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>CONCLUSIONS:<\/jats:title>\n            <jats:p>Trends in atrial fibrillation were strongly associated with near-term CVH, especially above-average burden coupled with low patient activity. This approach could provide actionable information to guide treatment and reduce CVH.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/circep.124.012991","type":"journal-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T09:00:51Z","timestamp":1729760451000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":7,"title":["Using Atrial Fibrillation Burden Trends and Machine Learning to Predict Near-Term Risk of Cardiovascular Hospitalization"],"prefix":"10.1161","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5022-0461","authenticated-orcid":true,"given":"James","family":"Peacock","sequence":"first","affiliation":[{"name":"White Plains Hospital, NY (J.P.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7781-356X","authenticated-orcid":true,"given":"Evan J.","family":"Stanelle","sequence":"additional","affiliation":[{"name":"Medtronic, Inc, Minneapolis, MN (E.J.S., L.C.J., R.K., D.S.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8547-072X","authenticated-orcid":true,"given":"Lawrence C.","family":"Johnson","sequence":"additional","affiliation":[{"name":"Medtronic, Inc, Minneapolis, MN (E.J.S., L.C.J., R.K., D.S.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8263-8304","authenticated-orcid":true,"given":"Elaine M.","family":"Hylek","sequence":"additional","affiliation":[{"name":"Boston University Medical Center, MA (E.M.H.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahul","family":"Kanwar","sequence":"additional","affiliation":[{"name":"Medtronic, Inc, Minneapolis, MN (E.J.S., L.C.J., R.K., D.S.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1492-2693","authenticated-orcid":true,"given":"Dhanunjaya R.","family":"Lakkireddy","sequence":"additional","affiliation":[{"name":"The Kansas City Heart Rhythm Institute and Research Foundation, Overland Park (D.R.L.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8003-7254","authenticated-orcid":true,"given":"Suneet","family":"Mittal","sequence":"additional","affiliation":[{"name":"Valley Health System, Ridgewood, NJ (S.M.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8718-1534","authenticated-orcid":true,"given":"Rod S.","family":"Passman","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Northwestern University, Feinberg School of Medicine, Chicago, IL (R.S.P.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3129-6161","authenticated-orcid":true,"given":"Andrea M.","family":"Russo","sequence":"additional","affiliation":[{"name":"Electrophysiology and Arrhythmia 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