Abstract

Objective To investigate the relationship between the serum high sensitive c-reactive protein (hs-CRP) and prealbumin (PAB) and the acute heart failure during the early stage of acute myocardial infarction (MI) patients.Methods A total of 181 MI patients (male:n =154,85.1% vs.female:n =27,14.9%),admitted between Seppember 2010 and September 2012,were enrolled.They were divided into heart failure group (n =114,63.0%) with Killip classification recorded and control group (n =67,37.0%) without heart failure.The levels of serum hs-CRP and PAB were determined from the venous blood in the followed morning after admission.The clinical data were analyzed by logistic regression,Spearman correlation,and ROC curve.Results The serum level of hs-CRP (mg/L) in the heart failure group was significantly higher than that in the control group (P =0.000),while the serum level of PAB (mg/L) in the heart failure group was significantly lower than that in the control group (P =0.000).High level of hs-CRP and low level of PAB were significantly correlated with Killip classification (rhs-CRP =0.234,Phs-CRP =0.003 ; rPAB =-0.321,PPAB =0.000).Serum hs-CRP (P =0.023,OR 1.086,95% Cl 1.012-1.167) and PAB (P =0.038,OR O.991,95% CI O.983-0.999) were the independent risk biomarkers of acute heart failure subsequent to myocardial infarction determined by multivariate logistic regression analysis.The area under the ROC curve:AUChs-CRP =0.722,95% CI 0.651-0.786; AUCPAB =0.723,95% CI 0.652-0.787.Conclusions With high level of serum hs-CRP or low level of serum PAB during the early stage of acute myocardial infarction,patients were predisposed to the development of acute heart failure consequently.Both of them are the independent risk biomarkers of acute heart failure subsequent to myocardial infarction.Furthermore,they were significantly correlated with severity of the heart failure in terms of Killip classification. Key words: Myocardial infarction; Acute heart failure; High sensitive C-reactive protein; Prealbumin; Killip classification; ROC curve; Logistic regression analysis ; Risk factor

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