Dataset Information

Multiple biomarkers for risk prediction in chronic heart failure.

ABSTRACT: Prior studies have suggested using a panel of biomarkers that measure diverse biological processes as a prognostic tool in chronic heart failure. Whether this approach improves risk prediction beyond clinical evaluation is unknown.In a multicenter cohort of 1513 chronic systolic heart failure patients, we measured a contemporary biomarker panel consisting of high-sensitivity C-reactive protein, myeloperoxidase, B-type natriuretic peptide, soluble fms-like tyrosine kinase receptor-1, troponin I, soluble toll-like receptor-2, creatinine, and uric acid. From this panel, we calculated a parsimonious multimarker score and assessed its performance in predicting risk of death, cardiac transplantation, or ventricular assist device placement in comparison to an established clinical risk score, the Seattle Heart Failure Model (SHFM). During a median follow-up of 2.5 years, there were 317 outcomes: 187 patients died; 99 were transplanted; and 31 had a ventricular assist device placed. In unadjusted Cox models, patients in the highest tertile of the multimarker score had a 13.7-fold increased risk of adverse outcomes compared with the lowest tertile (95% confidence interval, 8.75-21.5). These effects were independent of the SHFM (adjusted hazard ratio, 6.80; 95% confidence interval, 4.18-11.1). Addition of the multimarker score to the SHFM led to a significantly improved area under the receiver operating characteristic curve of 0.803 versus 0.756 (P=0.003) and appropriately reclassified a significant number of patients who had the outcome into a higher risk category (net reclassification improvement, 25.2%; 95% confidence interval, 14.2-36.2%; P<0.001).In ambulatory chronic heart failure patients, a score derived from multiple biomarkers integrating diverse biological pathways substantially improves prediction of adverse events beyond current metrics.

SUBMITTER: Ky B

PROVIDER: S-EPMC3387487 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multiple biomarkers for risk prediction in chronic heart failure.

Ky Bonnie B French Benjamin B Levy Wayne C WC Sweitzer Nancy K NK Fang James C JC Wu Alan H B AH Goldberg Lee R LR Jessup Mariell M Cappola Thomas P TP

Circulation. Heart failure 20120223 2

<h4>Background</h4>Prior studies have suggested using a panel of biomarkers that measure diverse biological processes as a prognostic tool in chronic heart failure. Whether this approach improves risk prediction beyond clinical evaluation is unknown.<h4>Methods and results</h4>In a multicenter cohort of 1513 chronic systolic heart failure patients, we measured a contemporary biomarker panel consisting of high-sensitivity C-reactive protein, myeloperoxidase, B-type natriuretic peptide, soluble fm ...[more]

PMID: 22361079

Similar Datasets

Project description:the purpose of this study was to assess the predictive accuracy of conventional cardiovascular risk factors for incident heart failure and atrial fibrillation, and the added benefit of multiple biomarkers reflecting diverse pathophysiological pathways.heart failure and atrial fibrillation are interrelated cardiac diseases associated with substantial morbidity and mortality and increasing incidence. Data on prediction and prevention of these diseases in healthy individuals are limited.in 5,187 individuals from the community-based MDCS (Malmö Diet and Cancer Study), we studied the performance of conventional risk factors and 6 biomarkers including midregional pro-atrial natriuretic peptide (MR-proANP), N-terminal pro-B-type natriuretic peptide (NT-proBNP), midregional pro-adrenomedullin, cystatin C, C-reactive protein (CRP), and copeptin.during a mean follow-up of 14 years, 112 individuals were diagnosed with heart failure and 284 individuals with atrial fibrillation. NT-proBNP (hazard ratio [HR]: 1.63 per SD, 95% confidence interval [CI]: 1.29 to 2.06, p < 0.001), CRP (HR: 1.57 per SD, 95% CI: 1.28 to 1.94, p < 0.001), and MR-proANP (HR: 1.26 per SD, 95% CI: 1.02 to 1.56, p = 0.03) predicted incident heart failure independently of conventional risk factors and other biomarkers. MR-proANP (HR: 1.62, 95% CI: 1.42 to 1.84, p < 0.001) and CRP (HR: 1.18, 95% CI: 1.03 to 1.34, p = 0.01) independently predicted atrial fibrillation. Addition of biomarkers to conventional risk factors improved c-statistics from 0.815 to 0.842 for heart failure and from 0.732 to 0.753 for atrial fibrillation and the integrated discrimination improvement for both diseases (p < 0.001). Net reclassification improvement (NRI) with biomarkers was observed in 22% of individuals for heart failure (NRI, p < 0.001) and in 7% for atrial fibrillation (NRI, p = 0.06), mainly due to up-classification of individuals who developed disease (heart failure: 29%, atrial fibrillation: 19%). Addition of CRP to natriuretic peptides did not improve discrimination or reclassification.conventional cardiovascular risk factors predict incident heart failure and atrial fibrillation with reasonable accuracy in middle-age individuals free from disease. Natriuretic peptides, but not other biomarkers, improve discrimination modestly for both diseases above and beyond conventional risk factors and substantially improve risk classification for heart failure.

Project description:ObjectiveEnergy metabolism plays a crucial role in the improvement of heart dysfunction as well as the development of heart failure (HF). The current study is designed to identify energy metabolism-related diagnostic biomarkers for predicting the risk of HF due to myocardial infarction.MethodsTranscriptome sequencing data of HF patients and non-heart failure (NF) people (GSE66360 and GSE59867) were obtained from gene expression omnibus (GEO) database. Energy metabolism-related differentially expressed genes (DEGs) were screened between HF and NF samples. The subtyping consistency analysis was performed to enable the samples to be grouped. The immune infiltration level among subtypes was assessed by single sample gene set enrichment analysis (ssGSEA). Random forest algorithm (RF) and support vector machine (SVM) were applied to identify diagnostic biomarkers, and the receiver operating characteristic curves (ROC) was plotted to validate the accuracy. Predictive nomogram was constructed and validated based on the result of the RF. Drug screening and gene-miRNA network were analyzed to predict the energy metabolism-related drugs and potential molecular mechanism.ResultsA total of 22 energy metabolism-related DEGs were identified between HF and NF patients. The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. More importantly, 10 drugs and 15 miRNA were predicted as drug target and hub miRNA that associated with energy metabolism-related genes, providing further information on clinical HF treatment.ConclusionThis study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.

Dataset Information

Multiple biomarkers for risk prediction in chronic heart failure.

Publications

Multiple biomarkers for risk prediction in chronic heart failure.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets