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type=\u0022text\/css\u0022 rel=\u0022stylesheet\u0022 href=\u0022\/\/d282kpwvnogo5m.cloudfront.net\/sites\/default\/files\/advagg_css\/css__ce2QY63WIanKyr8eSq7eavr1XQRRmFD6ZSmwpyJi8lM__zXwFqpqmxrZOXXcd_TpBQpjuELbmIP9wBR5UuTDWAO4__YJWWMMdfCJuAFm5cUEp88OsodhO3ZA-2lzRfoBsSlk4.css\u0022 media=\u0022all\u0022 \/\u003E\n\u003Clink rel=\u0027stylesheet\u0027 type=\u0027text\/css\u0027 href=\u0027\/sites\/all\/modules\/contrib\/panels\/plugins\/layouts\/onecol\/onecol.css\u0027 \/\u003E\u003C\/head\u003E\u003Cbody\u003E\u003Cdiv class=\u0022panels-ajax-tab-panel panels-ajax-tab-panel-sageoa-tab-art\u0022\u003E\u003Cdiv class=\u0022panel-display panel-1col clearfix\u0022 \u003E\n  \u003Cdiv class=\u0022panel-panel panel-col\u0022\u003E\n    \u003Cdiv\u003E\u003Cdiv class=\u0022panel-pane pane-highwire-markup\u0022 \u003E\n  \n      \n  \n  \u003Cdiv class=\u0022pane-content\u0022\u003E\n    \u003Cdiv class=\u0022highwire-markup\u0022\u003E\u003Cdiv xmlns=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022 id=\u0022content-block-markup\u0022 xmlns:xhtml=\u0022http:\/\/www.w3.org\/1999\/xhtml\u0022\u003E\u003Cdiv class=\u0022article fulltext-view \u0022\u003E\u003Cspan class=\u0022highwire-journal-article-marker-start\u0022\u003E\u003C\/span\u003E\u003Cdiv class=\u0022section abstract\u0022 id=\u0022abstract-1\u0022\u003E\u003Ch2\u003ESummary\u003C\/h2\u003E\n            \u003Cp id=\u0022p-1\u0022\u003EStudies have suggested using a panel of biomarkers that measure diverse biological processes as a prognostic tool for heart failure (HF). This article presents the Penn Heart Failure Study based on the hypothesis that multiple biomarkers considered together are superior to clinical risk stratification in patients with chronic HF [Ky B et al. Circ Heart Fail 2012].\u003C\/p\u003E\n         \u003C\/div\u003E\u003Cul class=\u0022kwd-group\u0022\u003E\u003Cli class=\u0022kwd\u0022\u003ECardiology Genomics\u003C\/li\u003E\u003Cli class=\u0022kwd\u0022\u003EHeart Failure\u003C\/li\u003E\u003C\/ul\u003E\u003Cp id=\u0022p-2\u0022\u003EStudies have suggested using a panel of biomarkers that measure diverse biological processes as a prognostic tool for heart failure (HF). Thomas P. Cappola, MD, ScM, University of Pennsylvania, Philadelphia, Pennsylvania, USA, presented the Penn Heart Failure Study based on the hypothesis that multiple biomarkers considered together are superior to clinical risk stratification in patients with chronic HF [Ky B et al. \u003Cem\u003ECirc Heart Fail\u003C\/em\u003E 2012]. The aim of the study was to derive a biomarker score in ambulatory HF patients that predicts time to transplant, left ventricular assist device (LVAD), or death and to compare its performance to the Seattle Heart Failure Model (SHFM).\u003C\/p\u003E\u003Cp id=\u0022p-3\u0022\u003EA total of 1513 patients with HF were evaluated with biomarker analysis on banked serum, plasma, and DNA; 2D echocardiogram; and detailed clinical covariates (SHFM). Eight candidate biomarkers that measure distinct biological processes and are individually associated with adverse outcomes were evaluated using high-quality assays: troponin I (TnI) for myocyte injury, creatinine for renal function, soluble toll-like receptor-2 (ST2) for myocyte stress, soluble fms-like tyrosine kinase receptor-1 (sFlt-1) for vascular remodeling, B-type natriuretic peptide (BNP) for neurohormones, myeloperoxidase and uric acid for oxidative stress, and high-sensitivity C-reactive protein (hsCRP) for inflammation.\u003C\/p\u003E\u003Cp id=\u0022p-4\u0022\u003EAt a median 2.5 years follow-up, 317 events had been reported, including 31 LVADs, 99 transplants, and 187 deaths. After biomarker evaluation using multiple methods, 7 markers remained in the multimarker score: BNP, sFlt-1, hsCRP, ST2, TnI, uric acid, and creatinine. The multimarker score was a strong predictor of adverse outcomes. The hazard ratio for adverse outcomes was 4.7 in patients with a moderate multimarker score and 15 in patients with a high multimarker score. The multimarker score was a stronger predictor of adverse outcomes than the SHFM score (AUC, 0.798; 95% CI, 0.763 to 0.833; p\u0026lt;0.01). Adding the multimarker score to the SHFM led to a significantly improved AUC of 0.803 (95% CI, 0.769 to 0.837; p\u0026lt;0.01). After SHFM risk stratification, the multimarker score improved risk assignment in 20% of patients (95% CI, 4.8% to 35.1%; p=0.01).\u003C\/p\u003E\u003Cp id=\u0022p-5\u0022\u003EThe derived multimarker score comprised of 7 biomarkers is an accurate predictor of adverse outcomes and has improved predictive accuracy compared to a clinical risk score. The same results were obtained using multiple analytic approaches. Broader screens using unbiased technologies are needed, as are clinical trials to prove the utility of biomarkers for risk prediction.\u003C\/p\u003E\u003Cul class=\u0022copyright-statement\u0022\u003E\u003Cli class=\u0022fn\u0022 id=\u0022copyright-statement-1\u0022\u003E\u00a9 2012 MD Conference Express\u00ae\u003C\/li\u003E\u003C\/ul\u003E\u003Cspan class=\u0022highwire-journal-article-marker-end\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Cspan id=\u0022related-urls\u0022\u003E\u003C\/span\u003E\u003C\/div\u003E\u003Ca href=\u0022http:\/\/mdc.sagepub.com\/content\/12\/3\/15.abstract\u0022 class=\u0022hw-link hw-link-article-abstract\u0022 data-icon-position=\u0022\u0022 data-hide-link-title=\u00220\u0022\u003EView Summary\u003C\/a\u003E\u003C\/div\u003E  \u003C\/div\u003E\n\n  \n  \u003C\/div\u003E\n\u003C\/div\u003E\n  \u003C\/div\u003E\n\u003C\/div\u003E\n\u003C\/div\u003E\u003Cscript type=\u0022text\/javascript\u0022 src=\u0022http:\/\/mdc.sagepub.com\/sites\/all\/modules\/highwire\/highwire\/plugins\/highwire_markup_process\/js\/highwire_openurl.js?nznek1\u0022\u003E\u003C\/script\u003E\n\u003C\/body\u003E\u003C\/html\u003E"}