{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T13:09:33Z","timestamp":1792760973233,"version":"4.5.0"},"reference-count":75,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2024,2,6]]},"abstract":"<jats:sec sec-type=\"background\" xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug\u2010eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods and Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">We developed and validated a new AI\u2010based pipeline using retrospective data of drug\u2010eluting stent\u2010treated patients, sourced from the Cerner Health Facts data set (n=98\u2009236) and Optum's de\u2010identified Clinformatics Data Mart Database (n=9978). The 36\u2009months following drug\u2010eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI\u2010DAPT model. The AI\u2010DAPT demonstrated peak accuracy in the 30 to 36\u2009months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%\u201392%] for ischemia and 84% [95% CI, 82%\u201387%] for bleeding predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">Our AI\u2010DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.123.029900","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T06:17:43Z","timestamp":1706681863000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":17,"title":["Dynamic Prognosis Prediction for Patients on DAPT After Drug\u2010Eluting Stent Implantation: Model Development and Validation"],"prefix":"10.1161","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8865-7717","authenticated-orcid":false,"given":"Fang","family":"Li","sequence":"first","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"},{"name":"Department of Artificial Intelligence and Informatics Mayo Clinic Jacksonville FL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2644-4908","authenticated-orcid":false,"given":"Laila","family":"Rasmy","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1395-6805","authenticated-orcid":false,"given":"Yang","family":"Xiang","sequence":"additional","affiliation":[{"name":"Peng Cheng Laboratory Shenzhen Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0434-2513","authenticated-orcid":false,"given":"Jingna","family":"Feng","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"},{"name":"Department of Artificial Intelligence and Informatics Mayo Clinic Jacksonville FL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9970-2517","authenticated-orcid":false,"given":"Ahmed","family":"Abdelhameed","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"},{"name":"Department of Artificial Intelligence and Informatics Mayo Clinic Jacksonville FL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2685-951X","authenticated-orcid":false,"given":"Xinyue","family":"Hu","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"},{"name":"Department of Artificial Intelligence and Informatics Mayo Clinic Jacksonville FL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1577-874X","authenticated-orcid":false,"given":"Zenan","family":"Sun","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0916-2722","authenticated-orcid":false,"given":"David","family":"Aguilar","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, McGovern Medical School University of Texas Health Science Center at Houston Houston TX USA"},{"name":"LSU School of Medicine, LSU Health New Orleans New Orleans LA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8244-274X","authenticated-orcid":false,"given":"Abhijeet","family":"Dhoble","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, McGovern Medical School University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0322-4566","authenticated-orcid":false,"given":"Jingcheng","family":"Du","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8878-8652","authenticated-orcid":false,"given":"Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1069-9236","authenticated-orcid":false,"given":"Shuteng","family":"Niu","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4014-2957","authenticated-orcid":false,"given":"Yifang","family":"Dang","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3519-7515","authenticated-orcid":false,"given":"Xinyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6541-1773","authenticated-orcid":false,"given":"Ziqian","family":"Xie","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4387-5574","authenticated-orcid":false,"given":"Yi","family":"Nian","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4059-9455","authenticated-orcid":false,"given":"JianPing","family":"He","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0889-2261","authenticated-orcid":false,"given":"Yujia","family":"Zhou","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9949-7007","authenticated-orcid":false,"given":"Jianfu","family":"Li","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"},{"name":"Department of Artificial Intelligence and Informatics Mayo Clinic Jacksonville FL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9021-5595","authenticated-orcid":false,"given":"Mattia","family":"Prosperi","sequence":"additional","affiliation":[{"name":"Data Intelligence Systems Lab, Department of Epidemiology, College of Public Health and Health Professions &amp; College of Medicine University of Florida Gainesville FL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2238-5429","authenticated-orcid":false,"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[{"name":"Department of Health Outcomes and Biomedical Informatics, College of Medicine University of Florida Gainesville FL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7754-1890","authenticated-orcid":false,"given":"Degui","family":"Zhi","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4267-1924","authenticated-orcid":false,"given":"Cui","family":"Tao","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA"},{"name":"Department of Artificial Intelligence and Informatics Mayo Clinic Jacksonville FL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000001038"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2018.5281"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.21037\/jtd.2016.10.93"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2018.09.057"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1210816"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(17)30397-5"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2016.02.064"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.3775"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2929264"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2017.03.571"},{"key":"e_1_3_2_12_2","first-page":"1","volume-title":"Machine Learning in Cardiovascular Medicine","author":"Al'Aref SJ","year":"2020","unstructured":"Al'Aref SJ, Singh G, Baskaran L, Metaxas D. Machine Learning in Cardiovascular Medicine. Academic Press; 2020:1\u2013454."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcin.2019.02.035"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2019.6835"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2019.12.21"},{"key":"e_1_3_2_16_2","first-page":"E122","article-title":"Machine learning on high\u2010dimensional data to predict bleeding post percutaneous coronary intervention","volume":"32","author":"Rayfield C","year":"2020","unstructured":"Rayfield C, Agasthi P, Mookadam F, Yang EH, Venepally NR, Ramakrishna H, Slomka P, Holmes DR Jr, Arsanjani R. Machine learning on high\u2010dimensional data to predict bleeding post percutaneous coronary intervention. J Invasive Cardiol. 2020;32:E122\u2013E129.","journal-title":"J Invasive Cardiol"},{"key":"e_1_3_2_17_2","unstructured":"Cerner health facts. UTHealth. Accessed December 16 2019. https:\/\/sbmi.uth.edu\/sbmi\u2010data\u2010service\/data\u2010set\/cerner\/"},{"key":"e_1_3_2_18_2","unstructured":"Optum Claims Data . Optum. Accessed July 29 2022. https:\/\/www.optum.com\/business\/life\u2010sciences\/real\u2010world\u2010data\/claims\u2010data.html"},{"key":"e_1_3_2_19_2","unstructured":"Billing and coding: percutaneous coronary interventions. Centers for Medicare & Medicaid Services. Accessed December 17 2019. https:\/\/www.cms.gov\/medicare\u2010coverage\u2010database\/view\/article.aspx?articleId=57479"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-018-3727-0"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0253580"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2017.12.048"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.118.023118"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.STR.29.8.1602"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1097\/01.sla.0000103066.22732.b8"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.118.035418"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.2147\/CLEP.S265619"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.STR.0000174293.17959.a1"},{"key":"e_1_3_2_29_2","unstructured":"Endicott M. Coding heart procedures in ICD\u20109\u2010CM and ICD\u201010\u2010PCS. American Health Information Management Association. 2015. Accessed February 17 2023. https:\/\/library.ahima.org\/doc?oid=300839"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.thromres.2010.03.009"},{"key":"e_1_3_2_31_2","unstructured":"Concept: Elixhauser Comorbidity Index. Manitoba Centre for Health Policy. Accessed February 17 2023. http:\/\/mchp\u2010appserv.cpe.umanitoba.ca\/viewConcept.php?printer=Y&conceptID=1436"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475-6773.2007.00822.x"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1002\/pds.1518"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.2147\/CLEP.S245555"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.thromres.2015.10.035"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1186\/s42444-019-0005-0"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjcard.2015.10.027"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cjca.2020.01.027"},{"key":"e_1_3_2_39_2","unstructured":"Drugs. Epocrates. Accessed February 17 2023. https:\/\/online.epocrates.com\/drugs"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0104519"},{"key":"e_1_3_2_41_2","unstructured":"Clinical Classifications Software (CCS) for ICD\u20109\u2010CM. Agency for Healthcare Research and Quality. 2016. https:\/\/www.hcup\u2010us.ahrq.gov\/toolssoftware\/ccs\/ccs.jsp"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/d14-1179"},{"key":"e_1_3_2_43_2","unstructured":"Choi E Bahadori MT Kulas JA Schuetz A Stewart WF Sun J. RETAIN: an interpretable predictive model for healthcare using reverse time attention mechanism. arXiv [cs.LG]. 2016. http:\/\/arxiv.org\/abs\/1608.05745"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2019.105758"},{"key":"e_1_3_2_45_2","unstructured":"lightgbm . LGBMClassifier. LightGBM. Accessed January 7 2022. https:\/\/lightgbm.readthedocs.io\/en\/latest\/pythonapi\/lightgbm.LGBMClassifier.html"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1021\/ci034160g"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.7653"},{"key":"e_1_3_2_48_2","unstructured":"Tf.Keras.Layers . CategoryEncoding. TensorFlow. Accessed July 22 2023. https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/keras\/layers\/CategoryEncoding"},{"key":"e_1_3_2_49_2","unstructured":"Wikipedia contributors . Brier score. Wikipedia The Free Encyclopedia. Accessed November 10 2022. https:\/\/en.wikipedia.org\/wiki\/Brier_score"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1111\/ijcp.13591"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpcardiol.2022.101113"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcin.2020.07.005"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)31791-8"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1055\/s-0043-57030"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)32519-8"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2018.7117"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehz056"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-023-02448-8"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06291-2"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2018.06.023"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrcardio.2016.101"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-020-01297-6"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-16378-3"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00455-y"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3224727"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(22)00049-8"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5400"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2021.100179"},{"key":"e_1_3_2_69_2","unstructured":"LightGBM documentation. LightGBM. Accessed February 17 2021. https:\/\/lightgbm.readthedocs.io\/en\/latest\/index.html"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2018.06.011"},{"key":"e_1_3_2_71_2","unstructured":"Sklearn.Calibration.CalibratedClassifierCV. scikit\u2010learn . Accessed August 12 2022. https:\/\/scikit\u2010learn.org\/stable\/modules\/generated\/sklearn.calibration.CalibratedClassifierCV.html"},{"key":"e_1_3_2_72_2","unstructured":"Rasmy L Hao X Zhu J Tran HT Xie Z Zhi D. pytorch_ehr: source codes based on PyTorch to analyze EHR. GitHub. Accessed December 17 2020. https:\/\/github.com\/ZhiGroup\/pytorch_ehr"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","unstructured":"Akiba T Sano S Yanase T Ohta T Koyama M. Optuna: a Next\u2010generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019:2623\u20132631. doi: 10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2019.02.020"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.h246"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2015-008864"}],"container-title":["Journal of the American Heart Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ahajournals.org\/doi\/full\/10.1161\/JAHA.123.029900","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T08:20:00Z","timestamp":1715588400000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ahajournals.org\/doi\/10.1161\/JAHA.123.029900"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,6]]},"references-count":75,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,2,6]]}},"alternative-id":["10.1161\/JAHA.123.029900"],"URL":"https:\/\/doi.org\/10.1161\/jaha.123.029900","relation":{},"ISSN":["2047-9980"],"issn-type":[{"value":"2047-9980","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,6]]},"assertion":[{"value":"2023-02-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e029900"}}