荷兰阿姆斯特丹公共卫生研究所Roderick C Slieker团队研究了2型糖尿病患者肾病预测模型的性能。2021年9月28日出版的《英国医学杂志》发表了这项成果。
为了识别和评估肾病预后模型的质量和准确性，并在2型糖尿病患者的外部队列中验证这些模型，研究组在PubMed和Embase数据库中检索用于预测2型糖尿病患者肾病风险的模型开发的研究，并进行系统评审和外部验证。筛查、数据提取和偏倚风险评估重复进行。合格的模型在Hoorn糖尿病护理系统（DCS）队列中进行外部验证（11450例），其观察指标与开发模型相同。计算肾病风险，并与随访2年、5年和10年观察到的风险进行比较。模型性能评估基于截距调整校准和辨别（C统计）。
纳入系统评价的41项研究报告了64个模型，其中46个模型是在糖尿病人群中开发的，18个模型是在包括糖尿病作为预测因子的普通人群中开发的。预测结果包括蛋白尿、糖尿病肾病、慢性肾病（一般人群）和终末期肾病。在不同的预测结果中，46个模型报告的明显差别很大，在糖尿病人群中开发的模型从0.60到0.99不等，在一般人群中开发的模型中从0.59到0.96不等。
在41项研究中，有31项报告了校准结果，并且模型通常校准良好。在64个检索到的模型中，有21个在Hoorn DCS队列中进行了外部验证，用于预测蛋白尿、糖尿病肾病和慢性肾病的风险，在预测范围和模型中表现出相当大的差异。然而，对于所有三种结果，至少有两种模型的C统计量>0.8，表明具有良好的辨别力。在第二次外部验证中，针对糖尿病肾病开发的模型优于针对慢性肾病的模型。模型通常在所有三个预测范围内都得到了很好的校准。
该研究确定了多种预测模型来预测蛋白尿、糖尿病肾病、慢性肾病和终末期肾病。在外部验证中，蛋白尿、糖尿病肾病和慢性肾病的鉴别和校准在不同的预测范围和模型中差异很大。然而，对于每种观察结果，特定模型在三个预测范围内均表现出良好的辨别和校准。
附：英文原文
Title: Performance of prediction models for nephropathy in people with type 2 diabetes: systematic review and external validation study
Author: Roderick C Slieker, Amber A W A van der Heijden, Moneeza K Siddiqui, Marlous Langendoen-Gort, Giel Nijpels, Ron Herings, Talitha L Feenstra, Karel G M Moons, Samira Bell, Petra J Elders, Leen M ’t Hart, Joline W J Beulens
Issue&Volume: 2021/09/28
Abstract:
Objectives To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes.
Design Systematic review and external validation.
Data sources PubMed and Embase.
Eligibility criteria Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes.
Methods Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell’s C statistic).
Results 41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons.
Conclusions This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting.
DOI: 10.1136/bmj.n2134
Source: https://www.bmj.com/content/374/bmj.n2134
BMJ-British Medical Journal:《英国医学杂志》，创刊于1840年。隶属于BMJ出版集团，最新IF：93.333
官方网址：http://www.bmj.com/
投稿链接：https://mc.manuscriptcentral.com/bmj
本期文章：《英国医学杂志》：Online/在线发表
