{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T04:39:06Z","timestamp":1790829546817,"version":"4.1.0"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["European J of Heart Fail"],"published-print":{"date-parts":[[2021,12]]},"abstract":"Abstract<\/jats:title>\n \n Aims<\/jats:title>\n Enhanced risk stratification of patients with aortic stenosis (AS) is necessary to identify patients at high risk for adverse outcomes, and may allow for better management of patient subgroups at high risk of myocardial damage. The objective of this study was to identify plasma biomarkers and multimarker profiles associated with adverse outcomes in AS.<\/jats:p>\n <\/jats:sec>\n \n Methods and results<\/jats:title>\n \n We studied 708 patients with calcific AS and measured 49 biomarkers using a Luminex platform. We studied the correlation between biomarkers and the risk of (i) death and (ii) death or heart failure\u2010related hospital admission (DHFA). We also utilized machine\u2010learning methods (a tree\u2010based pipeline optimizer platform) to develop multimarker models associated with the risk of death and DHFA. In this cohort with a median follow\u2010up of 2.8\u2009years, multiple biomarkers were significantly predictive of death in analyses adjusted for clinical confounders, including tumour necrosis factor (TNF)\u2010\u03b1 [hazard ratio (HR) 1.28,\n P<\/jats:italic>\n \u2009<\u20090.0001], TNF receptor 1 (TNFRSF1A; HR 1.38,\n P<\/jats:italic>\n \u2009<\u20090.0001), fibroblast growth factor (FGF)\u201023 (HR 1.22,\n P<\/jats:italic>\n \u2009<\u20090.0001), N\u2010terminal pro B\u2010type natriuretic peptide (NT\u2010proBNP) (HR 1.58,\n P<\/jats:italic>\n \u2009<\u20090.0001), matrix metalloproteinase\u20107 (HR 1.24,\n P<\/jats:italic>\n \u00a0=\u00a00.0002), syndecan\u20101 (HR 1.27,\n P<\/jats:italic>\n \u00a0=\u00a00.0002), suppression of tumorigenicity\u20102 (ST2) (IL1RL1; HR 1.22,\n P<\/jats:italic>\n \u00a0=\u00a00.0002), interleukin (IL)\u20108 (CXCL8; HR 1.22,\n P<\/jats:italic>\n \u00a0=\u00a00.0005), pentraxin (PTX)\u20103 (HR 1.17,\n P<\/jats:italic>\n \u00a0=\u00a00.001), neutrophil gelatinase\u2010associated lipocalin (LCN2; HR 1.18,\n P<\/jats:italic>\n \u2009<\u20090.0001), osteoprotegerin (OPG) (TNFRSF11B; HR 1.26,\n P<\/jats:italic>\n \u00a0=\u20090.0002), and endostatin (COL18A1; HR 1.28,\n P<\/jats:italic>\n \u00a0=\u20090.0012). Several biomarkers were also significantly predictive of DHFA in adjusted analyses including FGF\u201023 (HR 1.36,\n P<\/jats:italic>\n \u00a0<\u20090.0001), TNF\u2010\u03b1 (HR 1.26,\n P<\/jats:italic>\n \u2009<\u20090.0001), TNFR1 (HR 1.34,\n P<\/jats:italic>\n \u00a0<\u20090.0001), angiopoietin\u20102 (HR 1.26,\n P<\/jats:italic>\n \u00a0<\u20090.0001), syndecan\u20101 (HR 1.23,\n P<\/jats:italic>\n \u00a0=\u20090.0006), ST2 (HR 1.27,\n P<\/jats:italic>\n \u00a0<\u20090.0001), IL\u20108 (HR 1.18,\n P<\/jats:italic>\n \u00a0=\u20090.0009), PTX\u20103 (HR 1.18,\n P<\/jats:italic>\n \u00a0=\u20090.0002), OPG (HR 1.20,\n P<\/jats:italic>\n \u00a0=\u20090.0013), and NT\u2010proBNP (HR 1.63,\n P<\/jats:italic>\n \u00a0<\u20090.0001). Machine\u2010learning multimarker models were strongly associated with adverse outcomes (mean 1\u2010year probability of death of 0%, 2%, and 60%; mean 1\u2010year probability of DHFA of 0%, 4%, 97%;\n P<\/jats:italic>\n \u00a0<\u20090.0001). In these models, IL\u20106 (a biomarker of inflammation) and FGF\u201023 (a biomarker of calcification) emerged as the biomarkers of highest importance.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Plasma biomarkers are strongly associated with the risk of adverse outcomes in patients with AS. Biomarkers of inflammation and calcification were most strongly related to prognosis.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/ejhf.2361","type":"journal-article","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T01:25:03Z","timestamp":1633915503000},"page":"2021-2032","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Plasma biomarkers associated with adverse outcomes in patients with calcific aortic stenosis"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7701-8757","authenticated-orcid":false,"given":"Mahesh K.","family":"Vidula","sequence":"first","affiliation":[{"name":"Division of Cardiovascular Medicine Hospital of the University of Pennsylvania Philadelphia PA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alena","family":"Orlenko","sequence":"additional","affiliation":[{"name":"University of Pennsylvania Perelman School of Medicine 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