A diagnostic model for minimal change disease based on biological parameters.
A diagnostic model for minimal change disease based on biological parameters.
复制标题
基于生物学参数的微小病变诊断模型
作者：
Zhu H;Han Q;Zhang D;Wang Y;Gao J;Geng W;Yang X;Chen X
背景微小病变病（MCD）是肾病综合征（NS）的一种。在这项研究中，我们的目的是建立一个数学诊断模型的基础上，生物学参数的分类MCD。方法将798例NS患者分为MCD组和对照组。两组生物学指标比较采用t检验。采用Logistic回归建立诊断模型，并采用受试者工作特征（ROC）分析评价模型的诊断价值。结果抗磷脂酶A2受体等13项指标（抗PLA 2 R）（P = 0.000），总蛋白（TP）（P = 0.000），白蛋白（ALB）（P = 0.000），直接胆红素（DB）（P = 0.002），肌酐（Cr）（P = 0.000），总胆固醇（CH）（P = 0.000），乳酸脱氢酶（LDH）（P = 0.007），高密度脂蛋白胆固醇（HDL）（P = 0.000），低密度脂蛋白胆固醇（LDL）（P = 0.000），凝血酶时间（TT）血浆纤维蛋白原（FIB）（P = 0.000）、免疫球蛋白A（伊加）（P = 0.008）和补体C3（P = 0.019）与MCD显著相关。CH、HDL、LDL、TT和FIB的ROC曲线下面积均大于0.70。Logistic回归分析显示CH和TT是MCD的危险因素。根据“CH+TT”的ROC曲线下面积（AUC）为0.827，敏感性为83.0%，特异性为69.8%（P = 0.000）。结论所建立的CH和TT诊断模型可用于MCD的分型诊断。
Background Minimal change disease (MCD) is a kind of nephrotic syndrome (NS). In this study, we aimed to establish a mathematical diagnostic model based on biological parameters to classify MCD. Methods A total of 798 NS patients were divided into MCD group and control group. The comparison of biological indicators between two groups were performed with t-tests. Logistic regression was used to establish the diagnostic model, and the diagnostic value of the model was estimated using receiver operating characteristic (ROC) analysis. Results Thirteen indicators including Anti-phospholipase A2 receptor (anti-PLA2R) (P = 0.000), Total protein (TP) (P = 0.000), Albumin (ALB) (P = 0.000), Direct bilirubin (DB) (P = 0.002), Creatinine (Cr) (P = 0.000), Total cholesterol (CH) (P = 0.000), Lactate dehydrogenase (LDH) (P = 0.007), High density lipoprotein cholesterol (HDL) (P = 0.000), Low density lipoprotein cholesterol (LDL) (P = 0.000), Thrombin time (TT) (P = 0.000), Plasma fibrinogen (FIB) (P = 0.000), Immunoglobulin A (IgA) (P = 0.008) and Complement 3 (C3) (P = 0.019) were significantly correlated with MCD. Furthermore, the area under ROC curves of CH, HDL, LDL, TT and FIB were more than 0.70. Logistic analysis demonstrated that CH and TT were risk factors for MCD. According to the ROC of “CH+TT”, the AUC was 0.827, with the sensitivity of 83.0% and the specificity of 69.8% (P = 0.000). Conclusion The established diagnostic model with CH and TT could be used for classified diagnosis of MCD.
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