NT-proBNP联合肾小球滤过率对慢性心功能不全患者心衰程度的预测价值

目的: 研究N末端B型利钠肽原（NT proBNP）联合估算肾小球滤过率（eGFR）对慢性心衰患者心衰程度的预测价值。方法: 收募有慢性心衰病史、入院完善NT proBNP和eGFR的临床心功能下降患者124例，并按照心功能等级及eGFR进行分组，分析肾功能正常及减低患者间血浆NT proBNP水平差异，Pearson相关分析NT proBNP与eGFR的相关性，二元Logistic回归分析纳入心衰程度的危险因素并构建预测模型，受试者工作特征（ROC）曲线评估NT proBNP与预测模型对慢性心衰程度的预测效能。结果： 肾功能减低组血浆NT proBNP水平较肾功能正常组明显增高（P<0.01）。对心功能Ⅲ级和Ⅳ级分别进行亚组分析，结果显示肾功能正常组与肾功能减低组患者NT proBNP亦有统计学差异（P均<0.01）。Pearson相关性分析显示，慢性心衰患者的血浆NT proBNP水平与eGFR呈负相关（r=-0.499，P<0.01)。Logistic回归分析后纳入NT proBNP、肾功能分期以及两变量乘积建立模型，ROC曲线分析提示预测模型比单用NT proBNP对慢性心衰患者心功能恶化程度预测性更好（曲线下面积分别为0.808和0.751）。结论： 慢性心衰患者血浆NT proBNP水平与eGFR呈负性关联，且NT proBNP联合eGFR对慢性心功能不全患者心衰程度具有较高的预测能力。

The predictive value of NT-proBNP combined with glomerular filtration rate on the degree of heart failure in patients with chronic heart failure

Objective: To assess the predictive value of the combination of NT proBNP and eGFR on the degree of heart failure in patients with chronic heart failure. Methods: A total of 124 patients with history of chronic heart failure who were admitted to hospital and with results of NT proBNP and eGFR were divided into two groups according to cardiac function grade and eGFR. The difference of plasma NT proBNP level between patients with normal renal function and decreased renal function was analyzed. Pearson correlation analysis was used to detect the correlation of NT proBNP and eGFR. Binary Logistic regression analysis included the risk factors of the degree of heart failure and a prediction model was developed. The area under ROC curve was calculated to assess the prediction effect of NT proBNP and prediction model on the degree of chronic heart failure. Results: There was a significant difference in the level of NT proBNP between the normal renal function group and the decreased renal function group (P<0.01). It is the same in subgroup analysis of cardiac function (grade Ⅲ: P< 0.01; grade Ⅳ: P<0.01). Pearson correlation analysis showed that the plasma NT proBNP level was negatively correlated with eGFR in patients with chronic heart failure (r=-0.499, P<0.01). After Logistic regression analysis, NT proBNP, renal function stage and product of two variables were included to establish the model. ROC curve analysis showed that the prediction model was better than NT proBNP alone in predicting the degree of deterioration of heart function in patients with chronic heart failure (AUC0.808 vs. 0.751). Conclusion: There was a negative correlation between NT proBNP and eGFR in patients with chronic heart failure, and NT proBNP combined with eGFR could be used to predict the degree of heart failure in patients with chronic heart failure.