{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T19:31:00Z","timestamp":1783366260586,"version":"3.54.6"},"reference-count":14,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T00:00:00Z","timestamp":1624320000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Arrhythmia"],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Patients with atrial fibrillation (AF) usually have a heterogeneous co\u2010morbid history, with dynamic changes in risk factors impacting on multiple adverse outcomes. We investigated a large prospective cohort of patients with multimorbidity, using a machine\u2010learning approach, accounting for the dynamic nature of comorbidity risks and incident AF.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using machine\u2010learning, we studied a prospective US cohort using medical\/pharmacy databases of 1\u00a0091\u00a0911 patients, with an incident AF cohort of 14\u00a0078 and non\u2010AF cohort of 1\u00a0077\u00a0833 enrolled in the 4\u2010year study. Five incident clinical outcomes (heart failure, stroke, myocardial infarction, major bleeding, and cognitive impairment) were examined in relationship to AF status (AF vs non\u2010AF), diverse multi\u2010morbid (conditions and medications) history, and demographic parameters (age and gender), with supervised machine\u2010learning techniques.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Complex inter\u2010relationships of various comorbidities were uncovered for AF cases, leading to 6\u2010fold higher risk of heart failure relative to the non\u2010AF cohort (OR 6.02, 95% CI 5.72\u20106.33), followed by myocardial infarction (OR=2.68), stroke (OR=2.19), and major bleeding (OR=1.36). Supervised machine learning algorithms on the original populations yielded comparable results for both neural network and logistic regression algorithms in terms of discriminant validity, with c\u2010indexes for incident adverse outcomes: heart failure (0.924, 95%CI 0.923\u20100.925), stroke (0.871, 95%CI 0.869\u20100.873), myocardial infarction (0.901, 95% CI 0.899\u20100.903), major bleeding (0.700, 95%CI 0.697\u20100.703), and cognitive impairment (0.919, 95% CI 0.9170.921). External calibration of all models demonstrated a good fit between the predicted probabilities and observed events. Decision curve analysis demonstrated that the obtained models were much more clinically useful than the \u201ctreat all\u201d strategy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Complex multimorbidity relationships uncovered using a machine learning approach for incident AF cases have major consequences for integrated care management, with implications for risk stratification and adverse clinical outcomes. This approach may facilitate automated approaches in the presence of multimorbidity, potentially helping decision making.<\/jats:p><\/jats:sec>","DOI":"10.1002\/joa3.12555","type":"journal-article","created":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T05:30:16Z","timestamp":1624426216000},"page":"931-941","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Revisiting the dynamic risk profile of cardiovascular\/non\u2010cardiovascular multimorbidity in incident atrial fibrillation patients and five cardiovascular\/non\u2010cardiovascular outcomes: A machine\u2010learning approach"],"prefix":"10.1002","volume":"37","author":[{"given":"Gregory Y. H.","family":"Lip","sequence":"first","affiliation":[{"name":"Liverpool Centre for Cardiovascular Science University of Liverpool and Liverpool Heart &amp; Chest Hospital  Liverpool UK  <!--Query ID=\"q4\" Text=\"AUTHOR: Please check that authors and their affiliations are correct.\"-->"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Tran","sequence":"additional","affiliation":[{"name":"IngenioRX  Indianapolis IN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ash","family":"Genaidy","sequence":"additional","affiliation":[{"name":"Anthem Inc  Indianapolis IN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patricia","family":"Marroquin","sequence":"additional","affiliation":[{"name":"Anthem Inc  Indianapolis IN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cara","family":"Estes","sequence":"additional","affiliation":[{"name":"Anthem Inc  Indianapolis IN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,6,22]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.i4482"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1681\/ASN.2018050514"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehw233"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.285.22.2864"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1378\/chest.09-1584"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2017.10.085"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-0038-1636534"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamacardio.2017.4665"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjcard.2019.05.071"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cjca.2016.06.006"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.111.962688"},{"key":"e_1_2_8_13_1","unstructured":"2013 Statistical Analysis Software Base SAS 9.4 Procedures Guide: Statistical Procedures"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0191592"},{"key":"e_1_2_8_15_1","first-page":"1947","article-title":"Integrated care in atrial fibrillation: A systematic review and meta\u2010analysis","volume":"103","author":"Gallagher C","year":"2017","journal-title":"Heart"}],"container-title":["Journal of Arrhythmia"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/joa3.12555","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/joa3.12555","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/joa3.12555","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:48:22Z","timestamp":1693356502000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/joa3.12555"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,22]]},"references-count":14,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["10.1002\/joa3.12555"],"URL":"https:\/\/doi.org\/10.1002\/joa3.12555","archive":["Portico"],"relation":{},"ISSN":["1880-4276","1883-2148"],"issn-type":[{"value":"1880-4276","type":"print"},{"value":"1883-2148","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,22]]},"assertion":[{"value":"2021-04-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}