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A Multiparametric Approach Based on NT-proBNP, ST2, and Galectin3 for Stratifying One Year Prognosis of Chronic Heart Failure Outpatients

机译:基于NT-proBNP,ST2和Galectin3的多参数方法用于确定慢性心力衰竭门诊病人的一年预后

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Galectin-3 and ST2 are emerging biomarkers involved in myocardial fibrosis. We evaluate the relevance of a multiparametric biomarker approach based on increased serum levels of NT-proBNP, galectin-3, and ST2 in stratifying the prognosis of chronic heart failure (CHF) outpatients. In 315 CHF outpatients in stable clinical condition clinical and echocardiographic evaluations were performed. Routine chemistry and serum levels of NT-proBNP, galectin-3, and ST2 were also assessed. During a 12 month follow-up, cardiovascular death, and/or heart failure (HF) occurred in 64 patients. The presence of NT-proBNP, galectin-3, and ST2 were higher than the recommended cutoffs and were all associated with events at univariate Cox regression analysis, as well as in a multivariate analysis including the three biomarkers. When a score based on the number of biomarkers above the recommended cut-offs was used (in a range of 0–3), it was associated with events both with respect to the univariate (HR 2.96, 95% CI 2.21–3.95, p < 0.001, C-index 0.78) and the multivariate (HR 1.52, 95% CI 1.06–2.17, p: 0.023, C-index 0.87) analyses, after correction for the variables of a reference model. Our results suggest that an easy prognostic approach based on the combination of three biomarkers, although with partially-overlapping pathophysiological mechanisms, is able to identify patients with the highest risk of heart failure progression.
机译:Galectin-3和ST2是涉及心肌纤维化的新兴生物标志物。我们基于增加的血清NT-proBNP,galectin-3和ST2的血清水平评估多参数生物标记物方法在分层慢性心力衰竭(CHF)门诊患者预后中的相关性。在315名稳定的瑞士法郎门诊患者中,进行了临床和超声心动图评估。还评估了NT-proBNP,galectin-3和ST2的常规化学和血清水平。在12个月的随访中,有64例患者发生了心血管死亡和/或心力衰竭(HF)。 NT-proBNP,galectin-3和ST2的存在高于推荐的临界值,并且都与单变量Cox回归分析以及包括三个生物标记物的多变量分析中的事件相关。当使用基于高于推荐临界值的生物标志物数量的分数(范围为0–3)时,它与单变量事件均相关(HR 2.96,95%CI 2.21–3.95,p <0.001,C指数0.78)和多元变量(HR 1.52,95%CI 1.06-2.17,p:0.023,C指数0.87)在对参考模型变量进行校正后进行分析。我们的结果表明,尽管结合了三种生物标志物,但基于三种生物标志物的简便预后方法能够识别出心力衰竭进展风险最高的患者。

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