{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T21:07:05Z","timestamp":1769288825091,"version":"3.49.0"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"vor","delay-in-days":1271,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T00:00:00Z","timestamp":1655078400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"VZW Cardiovascular Research Center, Aalst, Belgium"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Risk stratification in patients with a new onset or worsened heart failure (HF) is essential for clinical decision making. We have utilized a novel approach to enrich patient level prognostication using longitudinally gathered data to develop ML-based algorithms predicting all-cause 30, 90, 180, 360, and 720 day mortality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>In a cohort of 2449 HF patients hospitalized between 1 January 2011 and 31 December 2017, we utilized 422 parameters derived from 151 451 patient exams. They included clinical phenotyping, ECG, laboratory, echocardiography, catheterization data or percutaneous and surgical interventions reflecting the standard of care as captured in individual electronic records. The development of predictive models consisted of 101 iterations of repeated random subsampling splits into balanced training and validation sets. ML models yielded area under the receiver operating characteristic curve (AUC-ROC) performance ranging from 0.83 to 0.89 on the outcome-balanced validation set in predicting all-cause mortality at aforementioned time-limits. The 1 year mortality prediction model recorded an AUC of 0.85. We observed stable model performance across all HF phenotypes: HFpEF 0.83 AUC, HFmrEF 0.85 AUC, and HFrEF 0.86 AUC, respectively. Model performance improved when utilizing data from more hospital contacts compared with only data collected at baseline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our findings present a novel, patient-level, comprehensive ML-based algorithm for predicting all-cause mortality in new or worsened heart failure. Its robust performance across phenotypes throughout the longitudinal patient follow-up suggests its potential in point-of-care clinical risk stratification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ehf2.14011","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T07:30:23Z","timestamp":1655105423000},"page":"3575-3584","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Utilizing Longitudinal Data in Assessing All-Cause Mortality in Patients Hospitalized with Heart Failure"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8773-1732","authenticated-orcid":false,"given":"Robert","family":"Herman","sequence":"first","affiliation":[{"name":"Powerful Medical , Bratislava ,","place":["Slovak Republic"]},{"name":"Sigmund Freud University , Vienna ,","place":["Austria"]},{"name":"Department of Advanced Biomedical Sciences, University of Naples Frederico II , Naples ,","place":["Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Vanderheyden","sequence":"additional","affiliation":[{"name":"Cardiovascular Center, OLV Hospital , Aalst ,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boris","family":"Vavrik","sequence":"additional","affiliation":[{"name":"Powerful Medical , Bratislava ,","place":["Slovak Republic"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Monika","family":"Beles","sequence":"additional","affiliation":[{"name":"Cardiovascular Center, OLV Hospital , Aalst ,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timotej","family":"Palus","sequence":"additional","affiliation":[{"name":"Powerful Medical , Bratislava ,","place":["Slovak Republic"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"Nelis","sequence":"additional","affiliation":[{"name":"Cardiovascular Center, OLV Hospital , Aalst ,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Goethals","sequence":"additional","affiliation":[{"name":"Cardiovascular Center, OLV Hospital , Aalst ,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sofie","family":"Verstreken","sequence":"additional","affiliation":[{"name":"Cardiovascular Center, OLV Hospital , Aalst ,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riet","family":"Dierckx","sequence":"additional","affiliation":[{"name":"Cardiovascular Center, OLV Hospital , Aalst ,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Penicka","sequence":"additional","affiliation":[{"name":"Cardiovascular Center, OLV Hospital , Aalst ,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ward","family":"Heggermont","sequence":"additional","affiliation":[{"name":"Cardiovascular Center, OLV Hospital , Aalst ,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jozef","family":"Bartunek","sequence":"additional","affiliation":[{"name":"Cardiovascular Center, OLV Hospital , Aalst ,","place":["Belgium"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,6,13]]},"reference":[{"key":"2026012114154996700_ehf214011-bib-0001","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1007\/s11897-017-0321-4","article-title":"Clinical phenotyping of heart failure with biomarkers: Current and future perspectives","volume":"14","author":"Senthong","year":"2017","journal-title":"Curr Heart Fail Rep"},{"key":"2026012114154996700_ehf214011-bib-0002","first-page":"440","article-title":"Risk prediction in patients with heart failure: A systematic review and analysis. JACC","volume":"2","author":"Rahimi","year":"2014","journal-title":"Heart Failure"},{"key":"2026012114154996700_ehf214011-bib-0003","doi-asserted-by":"crossref","first-page":"50","DOI":"10.15420\/cfr.2018.27.2","article-title":"Biomarkers in routine heart failure clinical care","volume":"5","author":"Nadar","year":"2019","journal-title":"Card Fail Rev"},{"key":"2026012114154996700_ehf214011-bib-0004","volume-title":"Deep learning","author":"Goodfellow","year":"2016"},{"key":"2026012114154996700_ehf214011-bib-0005","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/S0140-6736(19)31721-0","article-title":"An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction","volume":"394","author":"Attia","year":"2019","journal-title":"The Lancet"},{"key":"2026012114154996700_ehf214011-bib-0006","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1093\/eurheartj\/ehz902","article-title":"Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: The SEMMELWEIS-CRT score","volume":"41","author":"Tokodi","year":"2020","journal-title":"Eur Heart J"},{"key":"2026012114154996700_ehf214011-bib-0007","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1161\/CIRCULATIONAHA.119.041980","article-title":"Machine learning to predict the likelihood of acute myocardial infarction","volume":"140","author":"Than","year":"2019","journal-title":"Circulation"},{"key":"2026012114154996700_ehf214011-bib-0008","doi-asserted-by":"crossref","first-page":"241","DOI":"10.4155\/cli.13.7","article-title":"The handling of missing data in clinical trials","volume":"3","author":"Kenward","year":"2013","journal-title":"Clin Investig"},{"key":"2026012114154996700_ehf214011-bib-0009","doi-asserted-by":"crossref","DOI":"10.1002\/9781118625590","volume-title":"Applied regression analysis","author":"Draper","year":"1998"},{"key":"2026012114154996700_ehf214011-bib-0010","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1093\/ehjci\/jev014","article-title":"Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging","volume":"16","author":"Lang","year":"2015","journal-title":"European Heart Journal-Cardiovascular Imaging"},{"key":"2026012114154996700_ehf214011-bib-0011","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-47509-7","volume-title":"Fundamentals of data mining in genomics and proteomics","author":"Dubitzky","year":"2007"},{"key":"2026012114154996700_ehf214011-bib-0012","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1007\/s00059-016-4496-3","article-title":"The new ESC guidelines for acute and chronic heart failure 2016","volume":"41","author":"Oeing","year":"2016","journal-title":"Herz"},{"key":"2026012114154996700_ehf214011-bib-0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2019.103337","article-title":"Deep learning for electronic health records: A comparative review of multiple deep neural architectures","volume":"101","author":"Ayala Solares","year":"2020","journal-title":"J Biomed Inform"},{"key":"2026012114154996700_ehf214011-bib-0014","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1161\/CIRCULATIONAHA.105.584102","article-title":"The Seattle heart failure model","volume":"113","author":"Levy","year":"2006","journal-title":"Circulation"},{"key":"2026012114154996700_ehf214011-bib-0015","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1093\/eurheartj\/ehs337","article-title":"Predicting survival in heart failure: A risk score based on 39 372 patients from 30 studies","volume":"34","author":"Pocock","year":"2013","journal-title":"Eur Heart J"},{"key":"2026012114154996700_ehf214011-bib-0016","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12916-020-01894-2","article-title":"A heart failure phenotype stratified model for predicting 1-year mortality in patients admitted with acute heart failure: Results from an individual participant data meta-analysis of four prospective European cohorts","volume":"19","author":"Chen","year":"2021","journal-title":"BMC Med"},{"key":"2026012114154996700_ehf214011-bib-0017","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3389\/fcvm.2021.611055","article-title":"Sex-specific patterns of mortality predictors among patients undergoing cardiac resynchronization therapy: A machine learning approach","volume":"8","author":"Tokodi","year":"2021","journal-title":"Front Cardiovasc Med"},{"key":"2026012114154996700_ehf214011-bib-0018","article-title":"Hypochloremia is associated with increased risk of all-cause mortality in patients in the coronary care unit: A cohort study","volume":"48","author":"Li","year":"2020","journal-title":"J Int Med Res"},{"key":"2026012114154996700_ehf214011-bib-0019","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1054\/jcaf.2002.0804216","article-title":"Low serum total cholesterol is associated with marked increase in mortality in advanced heart failure","volume":"8","author":"Horwich","year":"2002","journal-title":"J Card Fail"},{"key":"2026012114154996700_ehf214011-bib-0020","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1002\/ejhf.2144","article-title":"Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction","volume":"23","author":"Woolley","year":"2021","journal-title":"Eur J Heart Fail"},{"key":"2026012114154996700_ehf214011-bib-0021","article-title":"Proteomic differences between heart failure with preserved ejection fraction patients and comorbidity controls","volume":"138","author":"Swat","year":"2018","journal-title":"Circulation"}],"container-title":["ESC Heart Failure"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ehf2.14011","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/ehf2.14011","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/academic.oup.com\/eschf\/article-pdf\/9\/5\/3575\/65352440\/eschf_9_5_3575.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/eschf\/article-pdf\/9\/5\/3575\/65352440\/eschf_9_5_3575.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T19:18:50Z","timestamp":1769023130000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/eschf\/article\/9\/5\/3575\/8323041"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,13]]},"references-count":21,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,6,13]]},"published-print":{"date-parts":[[2022,10,1]]}},"URL":"https:\/\/doi.org\/10.1002\/ehf2.14011","relation":{},"ISSN":["2055-5822"],"issn-type":[{"value":"2055-5822","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,10]]},"published":{"date-parts":[[2022,6,13]]},"assertion":[{"value":"2021-11-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-05-31","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}