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基于 PLA2R-IgG4 抗体的预测模型评估特发性膜性肾病的风险分层。

A PLA2R-IgG4 Antibody-Based Predictive Model for Assessing Risk Stratification of Idiopathic Membranous Nephropathy.

机构信息

Department of Nephrology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China.

Department of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi 214023, China.

出版信息

J Healthc Eng. 2021 Aug 31;2021:1521013. doi: 10.1155/2021/1521013. eCollection 2021.

Abstract

BACKGROUND

Known as an autoimmune glomerular disease, idiopathic membranous nephropathy (IMN) is considered to be associated with phospholipase A2 receptor (PLA2R) in terms of the main pathogenesis. The quantitative detection of serum PLA2R-IgG and PLA2R-IgG4 antibodies by time-resolved fluoroimmunoassay (TRFIA) was determined, and the value of them, both in the clinical prediction of risk stratification in IMN, was observed in this study.

METHODS

95 patients with IMN proved by renal biopsy were enrolled, who had tested positive for serum PLA2R antibodies by ELISA, and the quantitative detection of serum PLA2R-IgG and PLA2R-IgG4 antibodies was achieved by TRFIA. All the patients were divided into low-, medium-, and high-risk groups, respectively, which were set as dependent variables, according to proteinuria and renal function. Random forest (RF) was used to estimate the value of serum PLA2R-IgG and PLA2R-IgG4 in predicting the risk stratification of progression in IMN.

RESULTS

Out-of-bag estimates of variable importance in RF were employed to evaluate the impact of each input variable on the final classification accuracy. The variable of albumin, PLA2R-IgG, and PLA2R-IgG4 had high values (>0.3) of 0.3156, 0.3981, and 0.7682, respectively, which meant that these three were more important for the risk stratification of progression in IMN. In order to further assess the contribution of PLA2R-IgG and PLA2R-IgG4 to the model, we built four different models and found that PLA2R-IgG4 played an important role in improving the predictive ability of the model.

CONCLUSIONS

In this study, we established a random forest model to evaluate the value of serum PLA2R-IgG4 antibodies in predicting risk stratification of IMN. Compared with PLA2R-IgG, PLA2R-IgG4 is a more efficient biomarker in predicting the risk of progression in IMN.

摘要

背景

特发性膜性肾病(IMN)作为一种自身免疫性肾小球疾病,其主要发病机制与磷脂酶 A2 受体(PLA2R)有关。本研究通过时间分辨荧光免疫分析(TRFIA)定量检测血清 PLA2R-IgG 和 PLA2R-IgG4 抗体,并观察其在 IMN 风险分层临床预测中的价值。

方法

选取经肾活检证实的 95 例 IMN 患者,酶联免疫吸附法(ELISA)检测血清 PLA2R 抗体阳性,TRFIA 定量检测血清 PLA2R-IgG 和 PLA2R-IgG4 抗体。根据蛋白尿和肾功能将所有患者分为低、中、高危组,分别作为因变量。采用随机森林(RF)估计血清 PLA2R-IgG 和 PLA2R-IgG4 预测 IMN 进展风险分层的价值。

结果

RF 中的袋外估计变量重要性用于评估每个输入变量对最终分类准确性的影响。白蛋白、PLA2R-IgG 和 PLA2R-IgG4 这三个变量的重要性值(>0.3)分别为 0.3156、0.3981 和 0.7682,这意味着这三个变量对 IMN 进展的风险分层更为重要。为了进一步评估 PLA2R-IgG 和 PLA2R-IgG4 对模型的贡献,我们构建了四个不同的模型,发现 PLA2R-IgG4 对提高模型的预测能力起着重要作用。

结论

本研究建立了随机森林模型来评估血清 PLA2R-IgG4 抗体在预测 IMN 风险分层中的价值。与 PLA2R-IgG 相比,PLA2R-IgG4 是预测 IMN 进展风险的更有效生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/df97/8424241/ed21f862921d/JHE2021-1521013.001.jpg

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