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    血清Cys-C、Hcy、BNP与高龄慢性心力衰竭病人并发心房颤动的相关性研究

    Study on the correlation of serum Cys-C, Hcy, and BNP with complicated atrial fibrillation in elderly patients with chronic heart failure

      摘要
    • 摘要:
      目的 探讨血清胱抑素C(Cys-C)、同型半胱氨酸(Hcy)、脑钠肽(BNP)与高龄慢性心力衰竭病人并发心房颤动的相关性。
      方法 选取90例高龄慢性心力衰竭病人，根据是否并发心房颤动分为心房颤动组和无心房颤动组，各45例。收集病人的人口统计学资料、超声心动图检查指标和实验室检验指标。采用单因素分析相关指标，通过多因素logistic回归分析探讨高龄慢性心力衰竭并发心房颤动的危险因素, 并构建高龄慢性心力衰竭并发心房颤动的风险预测模型。采用ROC曲线评价模型的预测效能，采用校准曲线和决策曲线对模型的校准度和临床实用性进行评估。
      结果 2组病人Cys-C、Hcy、BNP、左心室射血分数差异均有统计学意义(P < 0.01)。多元logistic回归分析显示，Cys-C、Hcy、BNP为高龄慢性心衰并发房颤的独立影响因素(P < 0.05~P < 0.01)。将Cys-C、Hcy、BNP分别作为协变量X1、X2、X3，得出预测模型表达式为：Logit(P)=2.297 8-1.254 1× X1-1.446 8×X2-1.806 3×X3, 该模型最佳概率预测阈值为0.511，灵敏度为73.3%，特异度为82.2%，AUC为0.820(0.732~0.909)。校准曲线分析表明该模型具有较好的预测房颤能力，决策曲线分析表明该预测模型具有较好的临床实用性。
      结论 血清Cys-C、Hcy、BNP与高龄慢性心力衰竭病人并发心房颤动具有显著相关性，多因素综合构建的预测模型对高龄慢性心力衰竭病人并发心房颤动具有较好的预测价值。

       

      Abstract:
      Objective To investigate the correlation of serum cystatin C (Cys-C), homocysteine (Hcy), brain natriuretic peptide (BNP) with complicated atrial fibrillation in elderly patients with chronic heart failure.
      Methods A total of 90 elderly patients with chronic heart failure were selected and divided into an atrial fibrillation group and a non-atrial fibrillation group based on whether they had atrial fibrillation, with 45 cases in each group.The demographic data, echocardiographic examination indicators, and laboratory test indicators of patients were collected.Univariate analysis was used to evaluate relevant indicators, multivariate logistic regression analysis was applied to explore the risk factors for complicated atrial fibrillation in elderly patients with chronic heart failure, and a risk prediction model for complicated atrial fibrillation in elderly patients with chronic heart failure was constructed.ROC curve was used to evaluate the predictive efficacy of the model, and calibration curve and decision curve were employed to evaluate the calibration and clinical utility of the model.
      Results The differences of Cys-C, Hcy, BNP and left ventricular ejection fraction of patients between the two groups were statistically significant (P < 0.01).Multivariate logistic regression analysis showed that Cys-C, Hcy, and BNP were independent influencing factors for complicated atrial fibrillation in elderly patients with chronic heart failure (P < 0.05 to P < 0.01).Cys-C, Hcy, and BNP were used as covariates X1, X2, X3 to obtain the expression formula of the prediction model as: Logit(P)=2.297 8-1.254 1×X1-1.446 8×X2-1.806 3×X3.The optimal probability prediction threshold of this model was 0.511, with a sensitivity of 73.3%, specificity of 82.2%, and AUC of 0.820 (95%CI: 0.732-0.909).Calibration curve analysis showed that the model had good predictive ability for atrial fibrillation, and decision curve analysis showed that the predictive model had good clinical utility.
      Conclusions Serum Cys-C, Hcy, and BNP are significantly associated with complicated atrial fibrillation in elderly patients with chronic heart failure, and the constructed predictive model based on multivariate has good predictive value for complicated atrial fibrillation in elderly patients with chronic heart failure.

       

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