{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T21:35:47Z","timestamp":1793136947281,"version":"4.5.0"},"reference-count":27,"publisher":"BMJ","issue":"1","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"unspecified","delay-in-days":30,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Open Heart"],"accepted":{"date-parts":[[2022,5,4]]},"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>To use echocardiographic and clinical features to develop an explainable clinical risk prediction model in patients with aortic stenosis (AS), including those with low-gradient AS (LGAS), using machine learning (ML).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>In 1130 patients with moderate or severe AS, we used bootstrap lasso regression (BLR), an ML method, to identify echocardiographic and clinical features important for predicting the combined outcome of all-cause mortality or aortic valve replacement (AVR) within 5 years after the initial echocardiogram. A separate hold out set, from a different centre (n=540), was used to test the generality of the model. We also evaluated model performance with respect to each outcome separately and in different subgroups, including patients with LGAS.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Out of 69 available variables, 26 features were identified as predictive by BLR and expert knowledge was used to further reduce this set to 9 easily available and input features without loss of efficacy. A ridge logistic regression model constructed using these features had an area under the receiver operating characteristic curve (AUC) of 0.74 for the combined outcome of mortality\/AVR. The model reliably identified patients at high risk of death in years 2\u20135 (HRs \u22652.0, upper vs other quartiles, for years 2\u20135, p&lt;0.05, p=not significant in year 1) and was also predictive in the cohort with LGAS (n=383, HRs\u22653.3, p&lt;0.05). The model performed similarly well in the independent hold out set (AUC 0.78, HR \u22652.5 in years 1\u20135, p&lt;0.05).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>In two separate longitudinal databases, ML identified prognostic features and produced an algorithm that predicts outcome for up to 5 years of follow-up in patients with AS, including patients with LGAS. Our algorithm, the Aortic Stenosis Risk (ASteRisk) score, is available online for public use.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1136\/openhrt-2022-001990","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T11:26:02Z","timestamp":1653996362000},"page":"e001990","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":21,"title":["Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score"],"prefix":"10.1136","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8985-3947","authenticated-orcid":false,"given":"Mayooran","family":"Namasivayam","sequence":"first","affiliation":[{"name":"Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul D","family":"Myers","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John V","family":"Guttag","sequence":"additional","affiliation":[{"name":"Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7233-7409","authenticated-orcid":false,"given":"Romain","family":"Capoulade","sequence":"additional","affiliation":[{"name":"l\u2019institut du thorax, CHU Nantes, CNRS, INSERM, University of Nantes, Nantes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3607-279X","authenticated-orcid":false,"given":"Philippe","family":"Pibarot","sequence":"additional","affiliation":[{"name":"Cardiology, Quebec Heart and Lung Institute, Laval University, Quebec City, Quebec, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael H","family":"Picard","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judy","family":"Hung","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Collin M","family":"Stultz","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA"},{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA"},{"name":"Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA"},{"name":"Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"key":"2025081710173191000_9.1.e001990.1","first-page":"941","article-title":"Aortic valve disease in the older adult","volume":"13","author":"Bhatia","year":"2016","journal-title":"J Geriatr Cardiol"},{"key":"2025081710173191000_9.1.e001990.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2019.08.004"},{"key":"2025081710173191000_9.1.e001990.3","first-page":"e35","article-title":"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":"2020","journal-title":"J Am Coll Cardiol"},{"key":"2025081710173191000_9.1.e001990.4","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehp575"},{"key":"2025081710173191000_9.1.e001990.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2018.04.026"},{"key":"2025081710173191000_9.1.e001990.6","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning with electronic health records","volume":"1","author":"Rajkomar","year":"2018","journal-title":"NPJ Digit Med"},{"key":"2025081710173191000_9.1.e001990.7","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.amsu.2016.09.002","article-title":"Using electronic health record collected clinical variables to predict medical intensive care unit mortality","volume":"11","author":"Calvert","year":"2016","journal-title":"Ann Med Surg"},{"key":"2025081710173191000_9.1.e001990.8","doi-asserted-by":"crossref","unstructured":"Schlesinger DE , Stultz CM . 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