{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T18:50:54Z","timestamp":1783104654962,"version":"3.54.6"},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Department of Cardiovascular Medicine"},{"DOI":"10.13039\/100000871","name":"Mayo Clinic","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Betty Knight Scripps Professor of Cardiovascular Disease Clinical Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Aims<\/jats:title>\n                  <jats:p>Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods and results<\/jats:title>\n                  <jats:p>A comprehensive database including 9611 patients with serial transthoracic echocardiograms was collected from a single institution across three clinical sites. The data set included parameters from echocardiograms, electrocardiograms, laboratory values, and diagnosis codes. Data from a single clinical site were preserved as an independent test group. Machine learning models were trained to identify progression to severe stenosis and all-cause mortality and tested in their performance for endpoints at 2 and 5 years. In the independent test group, the AS progression model differentiated those with progression to severe AS within 2 and 5 years with an area under the curve (AUC) of 0.86 for both. The feature of greatest importance was aortic valve mean gradient, followed by other valve haemodynamic measurements including valve area and dimensionless index. The mortality model identified those with mortality within 2 and 5 years with an AUC of 0.84 and 0.87, respectively. Smaller reduced-input validation models had similarly robust findings.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Machine learning models can be used in patients with mild AS to identify those at high risk of disease progression and mortality. Implementation of such models may facilitate real-time, patient-specific follow-up recommendations.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztae085","type":"journal-article","created":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T17:53:58Z","timestamp":1731347638000},"page":"63-72","source":"Crossref","is-referenced-by-count":6,"title":["Use of artificial intelligence to predict outcomes in mild aortic valve stenosis"],"prefix":"10.1093","volume":"6","author":[{"given":"Raghav R","family":"Julakanti","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , 200 First Street SW, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1233-3924","authenticated-orcid":false,"given":"Ratnasari","family":"Padang","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , 200 First Street SW, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1340-0647","authenticated-orcid":false,"given":"Christopher G","family":"Scott","sequence":"additional","affiliation":[{"name":"Division of Biomedical Statistics and Informatics, Mayo Clinic , Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jordi","family":"Dahl","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , 200 First Street SW, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nader J","family":"Al-Shakarchi","sequence":"additional","affiliation":[{"name":"Department of Medicine, Mayo Clinic , Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Coby","family":"Metzger","sequence":"additional","affiliation":[{"name":"Medial EarlySign , Hod Hasharon ,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alon","family":"Lanyado","sequence":"additional","affiliation":[{"name":"Medial EarlySign , Hod Hasharon ,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John I","family":"Jackson","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , 200 First Street SW, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6095-5579","authenticated-orcid":false,"given":"Vuyisile T","family":"Nkomo","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , 200 First Street SW, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6800-3521","authenticated-orcid":false,"given":"Patricia A","family":"Pellikka","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Mayo Clinic , 200 First Street SW, Rochester, MN 55905, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"2025012123510718500_ztae085-B1","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.ijcard.2018.01.054","article-title":"Cardiovascular morbidity and mortality in patients with aortic valve sclerosis: a systematic review and meta-analysis","volume":"260","author":"Di Minno","year":"2018","journal-title":"Int J Cardiol"},{"key":"2025012123510718500_ztae085-B2","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1016\/j.athoracsur.2006.07.048","article-title":"Clinical profile and natural history of 453 nonsurgically managed patients with severe aortic stenosis","volume":"82","author":"Varadarajan","year":"2006","journal-title":"Ann Thorac Surg"},{"key":"2025012123510718500_ztae085-B3","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1002\/clc.23922","article-title":"Predicting progression of aortic stenosis by measuring serum calcification propensity","volume":"45","author":"Kurmann","year":"2022","journal-title":"Clin Cardiol"},{"key":"2025012123510718500_ztae085-B4","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1056\/NEJMra1313875","article-title":"Aortic-valve stenosis\u2014from patients at risk to severe valve obstruction","volume":"371","author":"Otto","year":"2014","journal-title":"N Engl J Med"},{"key":"2025012123510718500_ztae085-B5","doi-asserted-by":"crossref","first-page":"806","DOI":"10.2459\/JCM.0000000000001126","article-title":"The progression rate of aortic stenosis: key to tailoring the management and potential target for treatment","volume":"22","author":"Tafciu","year":"2021","journal-title":"J Cardiovasc Med (Hagerstown)"},{"key":"2025012123510718500_ztae085-B6","doi-asserted-by":"crossref","first-page":"4121","DOI":"10.1111\/jocs.15918","article-title":"Machine learning in medicine: it has arrived, let\u2019s embrace it","volume":"36","author":"Pappada","year":"2021","journal-title":"J Card Surg"},{"key":"2025012123510718500_ztae085-B7","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s12938-023-01106-x","article-title":"Machine learning-enhanced echocardiography for screening coronary artery disease","volume":"22","author":"Guo","year":"2023","journal-title":"Biomed Eng Online"},{"key":"2025012123510718500_ztae085-B8","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1016\/j.jcmg.2021.06.007","article-title":"Can machine learning help simplify the measurement of diastolic function in echocardiography?","volume":"14","author":"Arnaout","year":"2021","journal-title":"JACC Cardiovasc Imaging"},{"key":"2025012123510718500_ztae085-B9","first-page":"e72","article-title":"2020 ACC\/AHA guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines","volume":"143","author":"Otto","year":"2021","journal-title":"Circulation"},{"key":"2025012123510718500_ztae085-B10","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Chen","year":"2016"},{"key":"2025012123510718500_ztae085-B11","doi-asserted-by":"crossref","first-page":"3351","DOI":"10.1093\/eurheartj\/ehx381","article-title":"Staging classification of aortic stenosis based on the extent of cardiac damage","volume":"38","author":"Genereux","year":"2017","journal-title":"Eur Heart J"},{"key":"2025012123510718500_ztae085-B12","first-page":"4768","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Lundberg","year":"2017"},{"key":"2025012123510718500_ztae085-B13","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1002\/9780470906514","volume-title":"Statistical Methods in Diagnostic Medicine","author":"Zhou","year":"2011","edition":"2nd ed."},{"key":"2025012123510718500_ztae085-B14","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/s10654-014-9936-x","article-title":"Assessment of risk factors for developing incident aortic stenosis: the Troms\u00f8 Study","volume":"29","author":"Eveborn","year":"2014","journal-title":"Eur J Epidemiol"},{"key":"2025012123510718500_ztae085-B15","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1016\/S0735-1097(23)02411-7","article-title":"Aortic stenosis baseline severity predicts progression: a systematic review and meta-analysis","volume":"81","author":"Prosperi-Porta","year":"2023","journal-title":"J Am Coll Cardiol"},{"key":"2025012123510718500_ztae085-B16","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.jcmg.2022.12.008","article-title":"Machine learning to optimize the echocardiographic follow-up of aortic stenosis","volume":"16","author":"S\u00e1nchez-Puente","year":"2023","journal-title":"JACC Cardiovasc Imaging"},{"key":"2025012123510718500_ztae085-B17","doi-asserted-by":"crossref","first-page":"3290","DOI":"10.1161\/CIRCULATIONAHA.104.495903","article-title":"Outcome of 622 adults with asymptomatic, hemodynamically significant aortic stenosis during prolonged follow-up","volume":"111","author":"Pellikka","year":"2005","journal-title":"Circulation"}],"container-title":["European Heart Journal - Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ehjdh\/advance-article-pdf\/doi\/10.1093\/ehjdh\/ztae085\/60951811\/ztae085.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/6\/1\/63\/60951811\/ztae085.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/6\/1\/63\/60951811\/ztae085.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T23:51:27Z","timestamp":1737503487000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ehjdh\/article\/6\/1\/63\/7890754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,11]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,11,11]]},"published-print":{"date-parts":[[2025,1,22]]}},"URL":"https:\/\/doi.org\/10.1093\/ehjdh\/ztae085","relation":{},"ISSN":["2634-3916"],"issn-type":[{"value":"2634-3916","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,1]]},"published":{"date-parts":[[2024,11,11]]}}}