基于综合炎症反应评分构建慢性肾衰竭病人左心室功能障碍风险的动态列线图

    Constructing a dynamic nomogram for left ventricular dysfunction risk in patients with chronic kidney failure based on comprehensive inflammation response scoring

    • 摘要:
      目的 基于中性粒细胞计数与淋巴细胞计数比值(NLR)、血小板计数与淋巴细胞计数比值(PLR)和单核细胞计数与淋巴细胞计数比值(MLR)构建综合炎症反应评分(CIRS),并构建动态列线图预测慢性肾衰竭(CRF)病人左心室功能障碍(LVSD)风险。
      方法 选取196例CRF病人。使用Pearson检验分析NLR、PLR和MLR与左心室射血分数(LVEF)间相关性。基于ROC曲线计算NLR、PLR和MLR的曲线下面积(AUC)并根据最佳截断值并构建CIRS。采用多因素logistic回归分析CRF病人LVSD风险的独立危险因素并构建列线图。采用校正曲线,决策曲线和临床影响曲线分析评估列线图预测效能和临床净收益。使用DynNom等软件开发预测CRF病人LVSD风险的动态列线图。
      结果 196例病人中,CRF病人LVSD发生率为25.0%(49/196)。LVSD病人体质量指数(BMI)、磷酸盐、纤维蛋白原(Fib)、丙氨酸氨基转移酶(ALT)、血尿素氮高于无LVSD病人,收缩压(SBP)、、血红蛋白(Hb)、白蛋白(Alb)、钙(Ca)低于无LVSD病人(P<0.05 ~ P<0.01)。NLR、PLR和MLR与LVEF呈显著负相关关系(P < 0.01)。NLR、PLR和MLR诊断CRF病人LVSD的AUC分别为0.828(0.767 ~ 0.878)、0.795(0.732 ~ 0.849)和0.821(0.760 ~ 0.872),最佳截断值分别为>4.31、>179.66和>0.22。根据NLR、PLR和MLR最佳截断值构建CIRS,其诊断CRF病人LVSD的AUC为0.902(0.852 ~ 0.940)。多因素logistic回归分析显示,Hb、Alb、Ca、磷酸盐、Fib和CIRS是CRF病人LVSD风险独立影响因素(P<0.05)。校准曲线分析显示列线图的拟合程度良好;决策曲线分析结果显示,当阈值概率>0.015时,该列线图预测CRF病人发生LVSD的临床净收益>0;临床影响曲线分析结果显示,该列线图判定预测高风险人群与实际发生高风险人群高度匹配。
      结论 基于NLR、PLR和MLR构建的CIRS,并结合Hb、Alb、Ca、P和CIRS开发的动态列线图能准确预测CRF病人LVSD风险。

       

      Abstract:
      Objective To construct a comprehensive inflammatory response score (CIRS) based on the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR), and to develop a dynamic nomogram for predicting the risk of left ventricular dysfunction (LVSD) in patients with chronic renal failure (CRF).
      Methods A total of 196 CRF patients were selected. The correlation between NLR, PLR, and MLR and left ventricular ejection fraction (LVEF) was analyzed using the Pearson test. The area under the curve (AUC) of NLR, PLR, and MLR was calculated based on the receiver operating characteristic (ROC) curve, and the CIRS was constructed according to the optimal cutoff values. Multivariate logistic regression analysis was used to identify independent risk factors for LVSD in CRF patients and to construct a nomogram. Calibration curves, decision curves, and clinical impact curves were used to evaluate the predictive performance and clinical net benefit of the nomogram. DynNom and other software were used to develop a dynamic nomogram for predicting the risk of LVSD in CRF patients.
      Results Among the 196 patients, the incidence of LVSD in CRF patients was 25.0% (49/196). Patients with LVSD had higher body mass index (BMI), phosphate, fibrinogen (Fib), alanine aminotransferase (ALT), and blood urea nitrogen levels, and lower systolic blood pressure (SBP), hemoglobin (Hb), albumin (Alb), and calcium (Ca) levels compared to patients without LVSD (P < 0.05 to P < 0.01). NLR, PLR, and MLR were significantly negatively correlated with LVEF (P < 0.01). The AUCs of NLR, PLR, and MLR for diagnosing LVSD in CRF patients were 0.828 (0.767 to 0.878), 0.795 (0.732 to 0.849), and 0.821 (0.760 to 0.872), respectively, with optimal cutoff values of >4.31, >179.66, and >0.22, respectively. CIRS was constructed based on the optimal cutoff values of NLR, PLR, and MLR, and its AUC for diagnosing LVSD in CRF patients was 0.902 (0.852 to 0.940). Multivariate logistic regression analysis revealed that Hb, Alb, Ca, phosphate, Fib, and CIRS were independent risk factors for LVSD in CRF patients (P < 0.05). Calibration curve analysis indicated good fit of the nomogram; decision curve analysis showed that when the threshold probability was >0.015, the clinical net benefit of the nomogram in predicting LVSD in CRF patients was >0; clinical impact curve analysis demonstrated that the nomogram's predicted high-risk individuals closely matched the actual high-risk individuals.
      Conclusion The CIRS constructed based on NLR, PLR, and MLR, combined with Hb, Alb, Ca, P, and CIRS to develop a dynamic nomogram, can accurately predict the risk of LVSD in CRF patients.

       

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