Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
JAMA. 2019 Dec 3;322(21):2104-2114. doi: 10.1001/jama.2019.17379.
Early identification of individuals at elevated risk of developing chronic kidney disease (CKD) could improve clinical care through enhanced surveillance and better management of underlying health conditions.
To develop assessment tools to identify individuals at increased risk of CKD, defined by reduced estimated glomerular filtration rate (eGFR).
DESIGN, SETTING, AND PARTICIPANTS: Individual-level data analysis of 34 multinational cohorts from the CKD Prognosis Consortium including 5 222 711 individuals from 28 countries. Data were collected from April 1970 through January 2017. A 2-stage analysis was performed, with each study first analyzed individually and summarized overall using a weighted average. Because clinical variables were often differentially available by diabetes status, models were developed separately for participants with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external cohorts (n = 2 253 540).
Demographic and clinical factors.
Incident eGFR of less than 60 mL/min/1.73 m2.
Among 4 441 084 participants without diabetes (mean age, 54 years, 38% women), 660 856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781 627 participants with diabetes (mean age, 62 years, 13% women), 313 646 incident cases (40%) occurred during a mean follow-up of 3.9 years. Equations for the 5-year risk of reduced eGFR included age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, body mass index, and albuminuria concentration. For participants with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction between the 2. The risk equations had a median C statistic for the 5-year predicted probability of 0.845 (interquartile range [IQR], 0.789-0.890) in the cohorts without diabetes and 0.801 (IQR, 0.750-0.819) in the cohorts with diabetes. Calibration analysis showed that 9 of 13 study populations (69%) had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25.
Equations for predicting risk of incident chronic kidney disease developed from more than 5 million individuals from 34 multinational cohorts demonstrated high discrimination and variable calibration in diverse populations. Further study is needed to determine whether use of these equations to identify individuals at risk of developing chronic kidney disease will improve clinical care and patient outcomes.
早期识别有发展为慢性肾脏病（CKD）风险的个体，可以通过增强监测和更好地管理潜在健康状况来改善临床护理。
开发评估工具，以识别肾小球滤过率（eGFR）降低的 CKD 风险增加的个体。
设计、设置和参与者：来自 CKD 预后联合会的 34 个多国队列的个体水平数据分析，包括来自 28 个国家的 5222711 人。数据收集自 1970 年 4 月至 2017 年 1 月。进行了 2 阶段分析，每个研究首先单独分析，然后使用加权平均值进行汇总。由于临床变量通常根据糖尿病状态存在差异，因此为有糖尿病和无糖尿病的参与者分别开发了模型。在 9 个外部队列（n=2253540）中也测试了区分度和校准度。
人口统计学和临床因素。
eGFR 低于 60mL/min/1.73m2 的事件。
在 4441084 名无糖尿病的参与者中（平均年龄 54 岁，38%为女性），在平均 4.2 年的随访期间，发生了 660856 例（14.9%）eGFR 降低的事件。在 781627 名有糖尿病的参与者中（平均年龄 62 岁，13%为女性），在平均 3.9 年的随访期间，发生了 313646 例（40%）事件。用于预测 eGFR 降低的 5 年风险的方程包括年龄、性别、种族/民族、eGFR、心血管疾病史、曾经吸烟者、高血压、体重指数和白蛋白尿浓度。对于有糖尿病的参与者，模型还包括糖尿病药物、糖化血红蛋白和两者之间的交互作用。在无糖尿病的队列中，风险方程的 5 年预测概率的中位数 C 统计量为 0.845（四分位距 [IQR]，0.789-0.890），在有糖尿病的队列中为 0.801（IQR，0.750-0.819）。校准分析显示，在 13 个研究人群中有 9 个（69%）的观察到的风险与预测风险之间的斜率在 0.80 到 1.25 之间。在 9 个外部验证队列的 18 个研究人群中，区分度相似；校准显示，在 18 个研究人群中有 16 个（89%）的观察到的风险与预测风险之间的斜率在 0.80 到 1.25 之间。
从 34 个多国队列的 500 多万名个体中开发的预测慢性肾脏病事件风险的方程，在不同人群中表现出较高的区分度和可变的校准度。需要进一步研究，以确定这些方程是否用于识别有发展为慢性肾脏病风险的个体，将改善临床护理和患者结局。
