Centro de Investigación en Nutrición y Salud, Instituto Nacional de Salud Pública, Cuernavaca, México.
Facultad de Medicina, Universidad Juárez Autónoma de Tabasco, Tabasco, México.
Prev Chronic Dis. 2023 Apr 20;20:E30. doi: 10.5888/pcd20.220380.
Chronic kidney disease (CKD) is an important public health problem. In 2017, the global prevalence was estimated at 9.1%. Appropriate tools to predict the risk of developing CKD are necessary to prevent its progression. Type 2 diabetes is a leading cause of CKD; screening the population living with the disease is a cost-effective solution to prevent CKD. The aim of our study was to identify the existing prediction scores and their diagnostic accuracy for detecting CKD in apparently healthy populations and populations with type 2 diabetes.
We conducted an electronic search in databases, including Medline/PubMed, Embase, Health Evidence, and others. For the inclusion criteria we considered studies with a risk predictive score in healthy populations and populations with type 2 diabetes. We extracted information about the models, variables, and diagnostic accuracy, such as area under the receiver operating characteristic curve (AUC), C statistic, or sensitivity and specificity.
We screened 2,359 records and included 13 studies for healthy population, 7 studies for patients with type 2 diabetes, and 1 for both populations. We identified 12 models for patients with type 2 diabetes; the range of C statistic was from 0.56 to 0.81, and the range of AUC was from 0.71 to 0.83. For healthy populations, we identified 36 models with the range of C statistics from 0.65 to 0.91, and the range of AUC from 0.63 to 0.91.
This review identified models with good discriminatory performance and methodologic quality, but they need more validation in populations other than those studied. This review did not identify risk models with variables comparable between them to enable conducting a meta-analysis.
慢性肾脏病（CKD）是一个重要的公共卫生问题。2017 年，全球患病率估计为 9.1%。为了预防 CKD 的进展，需要有合适的工具来预测其发生风险。2 型糖尿病是 CKD 的主要病因；对患有该疾病的人群进行筛查是预防 CKD 的一种具有成本效益的解决方案。我们研究的目的是确定现有的预测评分及其对检测普通人群和 2 型糖尿病患者中 CKD 的诊断准确性。
我们在包括 Medline/PubMed、Embase、Health Evidence 在内的数据库中进行了电子检索。纳入标准为考虑在普通人群和 2 型糖尿病患者中具有风险预测评分的研究。我们提取了有关模型、变量和诊断准确性的信息，例如受试者工作特征曲线（ROC）下面积（AUC）、C 统计量或敏感性和特异性。
我们筛选了 2359 条记录，纳入了 13 项针对普通人群的研究、7 项针对 2 型糖尿病患者的研究和 1 项针对这两种人群的研究。我们确定了 12 项针对 2 型糖尿病患者的模型；C 统计量的范围为 0.56 至 0.81，AUC 的范围为 0.71 至 0.83。针对普通人群，我们确定了 36 项模型，C 统计量的范围为 0.65 至 0.91，AUC 的范围为 0.63 至 0.91。
本综述确定了具有良好判别性能和方法学质量的模型，但它们需要在除研究人群之外的人群中进行更多验证。本综述未确定具有可比较变量的风险模型，无法进行荟萃分析。
