George Institute for Global Health, Sydney, Australia.
Am J Kidney Dis. 2012 Nov;60(5):770-8. doi: 10.1053/j.ajkd.2012.04.025. Epub 2012 Jun 12.
Tools are needed to predict which individuals with diabetes will develop kidney disease and its complications.
An observational analysis of a randomized controlled trial.
SETTING & PARTICIPANTS: The ADVANCE (Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation) Study followed up 11,140 participants with type 2 diabetes for 5 years.
Readily available baseline demographic and clinical variables.
(1) Major kidney-related events (doubling of serum creatinine to ≥2.26 mg/dL [≥200 μmol/L], renal replacement therapy, or renal death) in all participants, and (2) new-onset albuminuria in participants with baseline normoalbuminuria.
Cox proportional hazard regression models predicting the outcomes were used to generate risk scores. Discrimination of the risk prediction models was compared with that of models based on estimated glomerular filtration rate (eGFR) alone, urinary albumin-creatinine ratio (ACR) alone, and their combination.
Risk scores for major kidney-related events and new-onset albuminuria were derived from 7- and 8-variable models, respectively. Baseline eGFR and ACR were dominant although models based on the 2 factors, alone or combined, had less discrimination (P<0.05) than the risk prediction models containing additional variables (risk prediction model C statistics of 0.847 [95% CI, 0.815-0.880] for major kidney-related events, and 0.647 [95% CI, 0.637-0.658] for new-onset albuminuria). Novel risk factors for new-onset albuminuria included Asian ethnicity and greater waist circumference, and for major kidney-related events, less education. The risk prediction models had acceptable calibration for both outcomes (modified Hosmer-Lemeshow test, P=0.9 and P=0.06, respectively).
The follow-up period was limited to 5 years. Results are applicable to people with type 2 diabetes at risk of vascular disease.
Risk scores have been developed for early and late events in diabetic nephropathy. Although eGFR and urinary ACR are important components of the prediction models, the extra variables considered add significantly to discrimination and, in the case of new-onset albuminuria, are required to achieve satisfactory calibration.
需要工具来预测哪些糖尿病患者会发展为肾脏疾病及其并发症。
一项针对随机对照试验的观察性分析。
ADVANCE（糖尿病和血管疾病行动：培哚普利氨氯地平预先评估）研究随访了 11140 名 2 型糖尿病患者 5 年。
基线时可获得的人口统计学和临床变量。
（1）所有参与者的主要肾脏相关事件（血清肌酐加倍至≥2.26mg/dL[≥200μmol/L]、肾脏替代治疗或肾脏死亡），以及（2）基线正常白蛋白尿患者的新发性白蛋白尿。
用于生成风险评分的 Cox 比例风险回归模型预测结果。与仅基于估计肾小球滤过率（eGFR）、尿白蛋白-肌酐比值（ACR）或两者组合的模型相比，比较了风险预测模型的区分度。
主要肾脏相关事件和新发性白蛋白尿的风险评分分别来自 7 变量和 8 变量模型。尽管基于 2 个因素的模型（单独或组合）的区分度较差（P<0.05），但 eGFR 和 ACR 仍是主要因素（风险预测模型主要肾脏相关事件的 C 统计量为 0.847[95%CI，0.815-0.880]，新发性白蛋白尿为 0.647[95%CI，0.637-0.658]）。新发性白蛋白尿的新危险因素包括亚洲种族和更大的腰围，以及主要肾脏相关事件的受教育程度较低。对于这两种结局，风险预测模型的校准均为可接受（改良 Hosmer-Lemeshow 检验，P=0.9 和 P=0.06）。
随访时间限制在 5 年。结果适用于有血管疾病风险的 2 型糖尿病患者。
已为糖尿病肾病的早期和晚期事件开发了风险评分。尽管 eGFR 和尿 ACR 是预测模型的重要组成部分，但考虑到其他变量可显著提高预测的区分度，在新发性白蛋白尿的情况下，还需要这些变量来达到令人满意的校准度。
