MessAGe 风险评分：用于激素依赖型肾病综合征肾活检决策的工具。

MeSsAGe risk score: tool for renal biopsy decision in steroid-dependent nephrotic syndrome.

机构信息

Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Level 12 NUHS Tower Block, 1E Kent Ridge Road, Singapore, 119228, Singapore.

Biostatistics Unit, National University of Singapore, Singapore, Singapore.

出版信息

Pediatr Res. 2019 Mar;85(4):477-483. doi: 10.1038/s41390-019-0277-z. Epub 2019 Jan 15.

DOI:10.1038/s41390-019-0277-z

PMID:30659270

Abstract

BACKGROUND

A lack of consensus exists as to the timing of kidney biopsy in children with steroid-dependent nephrotic syndrome (SDNS) where minimal change disease (MCD) predominates. This study aimed at examining the applicability of a biomarker-assisted risk score model to select SDNS patients at high risk of focal segmental glomerulosclerosis (FSGS) for biopsy.

METHODS

Fifty-five patients with SDNS and biopsy-proven MCD (n = 40) or FSGS (n = 15) were studied. A risk score model was developed with variables consisting of age, sex, eGFR, suPAR levels and percentage of CD8 memory T cells. Following multivariate regression analysis, total risk score was calculated as sum of the products of odds ratios and corresponding variables. Predictive cut-off point was determined using receiver operator characteristics (ROC) curve analysis.

RESULTS

Plasma suPAR levels in FSGS patients were significantly higher, while percentage of CD45ROCD8CD3 was significantly lower than in MCD patients and controls. ROC analysis suggests the risk score model with threshold score of 16.7 (AUC 0.84, 95% CI 0.72-0.96) was a good predictor of FSGS on biopsy. The 100% PPV cut-off was >24.0, while the 100% NPV was <13.3.

CONCLUSION

A suPAR and CD8 memory T cell percentage-based risk score model was developed to stratify SDNS patients for biopsy and for predicting FSGS.

摘要

背景

在以微小病变病（MCD）为主的激素依赖性肾病综合征（SDNS）患儿中，肾脏活检的时机存在争议。本研究旨在探讨生物标志物辅助风险评分模型在选择 SDNS 患者中 FSGS 风险较高的患者进行活检的适用性。

方法

研究了 55 例 SDNS 患者，其中活检证实为 MCD（n=40）或 FSGS（n=15）。风险评分模型的变量包括年龄、性别、eGFR、suPAR 水平和 CD8 记忆 T 细胞百分比。进行多变量回归分析后，总风险评分计算为各变量比值的乘积之和。使用接受者操作特征（ROC）曲线分析确定预测截断点。

结果

FSGS 患者的血浆 suPAR 水平明显升高，而 CD45ROCD8CD3 的百分比明显低于 MCD 患者和对照组。ROC 分析表明，阈值评分 16.7 的风险评分模型（AUC 0.84，95%CI 0.72-0.96）是预测活检中 FSGS 的良好指标。100%PPV 的截断值>24.0，而 100%NPV 的截断值<13.3。