美国国立卫生研究院Robert G. Nelson联合以色列特拉维夫大学Varda Shalev研究团队近日取得一项新成果。他们建立了慢性肾病发病的风险预测方程。这一研究成果发表在2019年11月8日出版的《美国医学会杂志》上。

若能早期识别慢性肾脏病（CKD）高危人群，即可通过加强监测和更好的健康管理来改善临床治疗。

研究组建立了一个CKD高危人群的评估方法，对CKD预后联盟的34个跨国队列中的个体数据进行分析，包括来自28个国家的5222711人。研究跨度从1970年4月至2017年1月，共进行两阶段分析，第一阶段先对每项研究单独分析，之后采用加权平均法进行总结，根据患者有无糖尿病分别建立模型。第二阶段在9个外部队列的2253540人中进行鉴别和校准测试。肾小球滤过率（eGFR）降低定义为低于60 ml/min/1.7m²。

在4441084名无糖尿病患者中，平均随访4.2年，共有660856例（14.9%）患者发生eGFR降低。而在781267名糖尿病患者中，平均随访3.9年，共有313646例（40%）患者发生eGFR降低。eGFR降低的5年风险方程包括年龄、性别、种族/民族、eGFR、心血管疾病史、吸烟史、高血压、体重指数和蛋白尿浓度。对于糖尿病患者，该模型还包括糖尿病药物、血红蛋白A1c以及两者之间的相互作用。

该风险方程的5年预测概率中位数为0.845，糖尿病组为0.801。校正分析显示，13个研究人群中有9个（69%）观察到的预测风险斜率在0.80-1.25之间。在9个外部验证队列中，18个研究人群的识别率相似；校正分析显示，18个研究人群中有16个（89%）观察到的预测风险斜率在0.80-1.25之间。

总之，这个来自34个国家队列的500多万人的CKD发病风险预测方程在不同的人群中显示出了高度的识别力和可变的校准。但仍需进一步研究来确定使用该方程识别CKD高危人群是否会改善临床护理以及患者的预后。

附：英文原文

Title: Development of Risk Prediction Equations for Incident Chronic Kidney Disease

Author: Robert G. Nelson, Morgan E. Grams, Shoshana H. Ballew, Yingying Sang, Fereidoun Azizi, Steven J. Chadban, Layal Chaker, Stephan C. Dunning, Caroline Fox, Yoshihisa Hirakawa, Kunitoshi Iseki, Joachim Ix, Tazeen H. Jafar, Anna Kttgen, David M. J. Naimark, Takayoshi Ohkubo, Gordon J. Prescott, Casey M. Rebholz, Charumathi Sabanayagam, Toshimi Sairenchi, Ben Schttker, Yugo Shibagaki, Marcello Tonelli, Luxia Zhang, Ron T. Gansevoort, Kunihiro Matsushita, Mark Woodward, Josef Coresh, Varda Shalev

Issue&Volume: November 8, 2019

Abstract: Importance  

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.

Objective  

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 5222711 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=2253540).

Exposures  

Demographic and clinical factors.

Main Outcomes and Measures  

Incident eGFR of less than 60 mL/min/1.73 m2.

Results

Among 4441084 participants without diabetes (mean age, 54 years, 38% women), 660856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781627 participants with diabetes (mean age, 62 years, 13% women), 313646 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.

Conclusions and Relevance  

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.

DOI: 10.1001/jama.2019.17379

Source: https://jamanetwork.com/journals/jama/fullarticle/2755299