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形态测量学可预测原发性蛋白尿性肾小球病的早期肾小球滤过率变化:一项使用广义估计方程的纵向队列研究

Morphometry Predicts Early GFR Change in Primary Proteinuric Glomerulopathies: A Longitudinal Cohort Study Using Generalized Estimating Equations.

作者信息

Lemley Kevin V, Bagnasco Serena M, Nast Cynthia C, Barisoni Laura, Conway Catherine M, Hewitt Stephen M, Song Peter X K

机构信息

Department of Pediatrics, Division of Pediatric Nephrology, University of Southern California Keck School of Medicine, Los Angeles, California, United States of America.

Department of Pathology, The Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America.

出版信息

PLoS One. 2016 Jun 10;11(6):e0157148. doi: 10.1371/journal.pone.0157148. eCollection 2016.

Abstract

OBJECTIVE

Most predictive models of kidney disease progression have not incorporated structural data. If structural variables have been used in models, they have generally been only semi-quantitative.

METHODS

We examined the predictive utility of quantitative structural parameters measured on the digital images of baseline kidney biopsies from the NEPTUNE study of primary proteinuric glomerulopathies. These variables were included in longitudinal statistical models predicting the change in estimated glomerular filtration rate (eGFR) over up to 55 months of follow-up.

RESULTS

The participants were fifty-six pediatric and adult subjects from the NEPTUNE longitudinal cohort study who had measurements made on their digital biopsy images; 25% were African-American, 70% were male and 39% were children; 25 had focal segmental glomerular sclerosis, 19 had minimal change disease, and 12 had membranous nephropathy. We considered four different sets of candidate predictors, each including four quantitative structural variables (for example, mean glomerular tuft area, cortical density of patent glomeruli and two of the principal components from the correlation matrix of six fractional cortical areas-interstitium, atrophic tubule, intact tubule, blood vessel, sclerotic glomerulus, and patent glomerulus) along with 13 potentially confounding demographic and clinical variables (such as race, age, diagnosis, and baseline eGFR, quantitative proteinuria and BMI). We used longitudinal linear models based on these 17 variables to predict the change in eGFR over up to 55 months. All 4 models had a leave-one-out cross-validated R2 of about 62%.

CONCLUSIONS

Several combinations of quantitative structural variables were significantly and strongly associated with changes in eGFR. The structural variables were generally stronger than any of the confounding variables, other than baseline eGFR. Our findings suggest that quantitative assessment of diagnostic renal biopsies may play a role in estimating the baseline risk of succeeding loss of renal function in future clinical studies, and possibly in clinical practice.

摘要

目的

大多数肾脏疾病进展的预测模型未纳入结构数据。若模型中使用了结构变量,通常也只是半定量的。

方法

我们研究了在原发性蛋白尿性肾小球病的海王星(NEPTUNE)研究中,对基线肾活检数字图像测量的定量结构参数的预测效用。这些变量被纳入纵向统计模型,以预测长达55个月随访期间估计肾小球滤过率(eGFR)的变化。

结果

参与者是来自NEPTUNE纵向队列研究的56名儿童和成人受试者,他们的数字活检图像进行了测量;25%为非裔美国人,70%为男性,39%为儿童;25人患有局灶节段性肾小球硬化,19人患有微小病变病,12人患有膜性肾病。我们考虑了四组不同的候选预测因子,每组包括四个定量结构变量(例如,平均肾小球毛细血管丛面积、有功能肾小球的皮质密度以及来自六个皮质分数区域 - 间质、萎缩肾小管、完整肾小管、血管、硬化肾小球和有功能肾小球的相关矩阵的两个主成分)以及13个潜在混杂的人口统计学和临床变量(如种族、年龄、诊断以及基线eGFR、定量蛋白尿和体重指数)。我们使用基于这17个变量的纵向线性模型来预测长达55个月期间eGFR的变化。所有4个模型的留一法交叉验证R2约为62%。

结论

定量结构变量的几种组合与eGFR的变化显著且强烈相关。除基线eGFR外,结构变量通常比任何混杂变量都更强。我们的研究结果表明,诊断性肾活检的定量评估可能在未来临床研究中估计肾功能后续丧失的基线风险方面发挥作用,并可能在临床实践中发挥作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c55d/4902229/c77d6c263894/pone.0157148.g001.jpg

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