Department of Medicine, Tufts Medical Center, 800 Washington St, PO Box 391, Boston, MA 02111, USA.
JAMA. 2011 Apr 20;305(15):1553-9. doi: 10.1001/jama.2011.451. Epub 2011 Apr 11.
Chronic kidney disease (CKD) is common. Kidney disease severity can be classified by estimated glomerular filtration rate (GFR) and albuminuria, but more accurate information regarding risk for progression to kidney failure is required for clinical decisions about testing, treatment, and referral.
To develop and validate predictive models for progression of CKD.
DESIGN, SETTING, AND PARTICIPANTS: Development and validation of prediction models using demographic, clinical, and laboratory data from 2 independent Canadian cohorts of patients with CKD stages 3 to 5 (estimated GFR, 10-59 mL/min/1.73 m(2)) who were referred to nephrologists between April 1, 2001, and December 31, 2008. Models were developed using Cox proportional hazards regression methods and evaluated using C statistics and integrated discrimination improvement for discrimination, calibration plots and Akaike Information Criterion for goodness of fit, and net reclassification improvement (NRI) at 1, 3, and 5 years.
Kidney failure, defined as need for dialysis or preemptive kidney transplantation.
The development and validation cohorts included 3449 patients (386 with kidney failure [11%]) and 4942 patients (1177 with kidney failure [24%]), respectively. The most accurate model included age, sex, estimated GFR, albuminuria, serum calcium, serum phosphate, serum bicarbonate, and serum albumin (C statistic, 0.917; 95% confidence interval [CI], 0.901-0.933 in the development cohort and 0.841; 95% CI, 0.825-0.857 in the validation cohort). In the validation cohort, this model was more accurate than a simpler model that included age, sex, estimated GFR, and albuminuria (integrated discrimination improvement, 3.2%; 95% CI, 2.4%-4.2%; calibration [Nam and D'Agostino χ(2) statistic, 19 vs 32]; and reclassification for CKD stage 3 [NRI, 8.0%; 95% CI, 2.1%-13.9%] and for CKD stage 4 [NRI, 4.1%; 95% CI, -0.5% to 8.8%]).
A model using routinely obtained laboratory tests can accurately predict progression to kidney failure in patients with CKD stages 3 to 5.
慢性肾脏病（CKD）较为常见。可通过估算肾小球滤过率（GFR）和白蛋白尿来对肾脏疾病的严重程度进行分级，但在进行检测、治疗和转诊等临床决策时，需要更准确的信息来预测进展为肾衰竭的风险。
开发和验证 CKD 进展的预测模型。
设计、地点和参与者：使用来自加拿大 2 个独立 CKD 分期为 3 至 5 期（估计肾小球滤过率 10-59mL/min/1.73m²）的患者的人口统计学、临床和实验室数据，对预测模型进行开发和验证。这些患者在 2001 年 4 月 1 日至 2008 年 12 月 31 日期间被转诊至肾病医生处。使用 Cox 比例风险回归方法开发模型，并通过 C 统计量、整合判别改善、校准图和赤池信息量准则（Akaike Information Criterion，AIC）评估模型的拟合优度，以及在 1、3 和 5 年时评估净重新分类改善（net reclassification improvement，NRI）。
肾衰竭，定义为需要透析或预先进行肾移植。
开发和验证队列分别包含 3449 例患者（386 例发生肾衰竭[11%]）和 4942 例患者（1177 例发生肾衰竭[24%]）。最准确的模型包含年龄、性别、估计肾小球滤过率、白蛋白尿、血清钙、血清磷、血清碳酸氢盐和血清白蛋白（开发队列的 C 统计量为 0.917[95%置信区间（CI），0.901-0.933]，验证队列为 0.841[95%CI，0.825-0.857]）。在验证队列中，该模型优于仅包含年龄、性别、估计肾小球滤过率和白蛋白尿的简单模型（综合判别改善 3.2%[95%CI，2.4%-4.2%]；校准[Nam 和 D'Agostino χ²检验，19 与 32]；以及 CKD 3 期的重新分类[NRI，8.0%[95%CI，2.1%-13.9%]和 CKD 4 期[NRI，4.1%[95%CI，-0.5%至 8.8%]）。
使用常规实验室检查可准确预测 CKD 分期为 3 至 5 期患者的肾衰竭进展。
