Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA (J Coresh, K Matsushita, Y Sang, SH Ballew); Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada (TC Turin); Johns Hopkins Medical Institutions, Baltimore, MD, 21205 (LJ Appel); The George Institute for Global Health, University of Sydney, Sydney, NSW, Australia (H Arima); Department of Nephrology and Transplantation, Royal Prince Alfred Hospital, Sydney, NSW, Australia and Sydney Medical School, University of Sydney, Sydney, NSW, Australia (SJ Chadban); Department of Medicine, University of Salerno, Salerno, Italy (M Cirillo); BC Provincial Renal Agency, Vancouver, Canada (O Djurdjev); Nephrology Department, Geisinger Medical Center, Danville, PA, USA (JA Green); Department of Internal Medicine IV-Nephrology and Hypertension, Saarland University Medical Center, D-66421 Homburg, Germany (GH Heine); Division of Nephrology at Tufts Medical Center, Boston, MA 02111, USA (LA Inker, AS Levey); Department of Health and Welfare, Ibaraki Prefectural Office, Mito, Japan (F Irie); Minneapolis VA Health Care System and Department of Medicine, University of Minnesota, Minneapolis, MN, USA (A Ishani); University of California San Diego, San Diego, CA, USA (JH Ix); Memphis Veterans Affairs Medical Center and University of Tennessee Health Science Center, Memphis, TN, USA (CP Kovesdy); Division of Applied Health Sciences, University of Aberdeen, and NHS Grampian, Foresterhill, Aberdeen, UK (A Marks); Department of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan, Department of Planning for Drug Development and Clinical Evaluation, Tohoku University Graduate School of Pharmaceutical Sciences, Sendai, Japan, and Department of Health Science, Shiga University of Medical Science, Setatuskinowa, Otsu, Japan (T Ohkubo); Medical Informatics Department, Maccabi Healthcare Services, and Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel (V Shalev); Department of Family Medicine and Population Health, Virginia Commonwealth University, School of Medicine, 830 E. Main Street, P.O. Box 980212, Richmond, VA 23298-0212 (A Shankar); China Medical University Hospital, Taichung, Taiwan and Institute of Population Health Science, National Health Research Institutes, Zhunan, Taiwan (CP Wen); Department of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands (PE de Jong, RT Gansevoort); Dialysis Unit, University Hospital of The Ryukyus, 207 Uehara, Nishihara, Okinawa 903-0215, Japan (K Iseki); Inserm U1018, CESP Center for Research in Epidemiology and Population Health, Villejuif, France and UMRS 1018, Paris-Sud University, Villejuif, France (B Stengel).
JAMA. 2014 Jun 25;311(24):2518-2531. doi: 10.1001/jama.2014.6634.
The established chronic kidney disease (CKD) progression end point of end-stage renal disease (ESRD) or a doubling of serum creatinine concentration (corresponding to a change in estimated glomerular filtration rate [GFR] of −57% or greater) is a late event.
To characterize the association of decline in estimated GFR with subsequent progression to ESRD with implications for using lesser declines in estimated GFR as potential alternative end points for CKD progression. Because most people with CKD die before reaching ESRD, mortality risk also was investigated.
Individual meta-analysis of 1.7 million participants with 12,344 ESRD events and 223,944 deaths from 35 cohorts in the CKD Prognosis Consortium with a repeated measure of serum creatinine concentration over 1 to 3 years and outcome data.
Transfer of individual participant data or standardized analysis of outputs for random-effects meta-analysis conducted between July 2012 and September 2013, with baseline estimated GFR values collected from 1975 through 2012.
End-stage renal disease (initiation of dialysis or transplantation) or all-cause mortality risk related to percentage change in estimated GFR over 2 years, adjusted for potential confounders and first estimated GFR.
The adjusted hazard ratios (HRs) of ESRD and mortality were higher with larger estimated GFR decline. Among participants with baseline estimated GFR of less than 60 mL/min/1.73 m2, the adjusted HRs for ESRD were 32.1 (95% CI, 22.3-46.3) for changes of −57% in estimated GFR and 5.4 (95% CI, 4.5-6.4) for changes of −30%. However, changes of −30% or greater (6.9% [95% CI, 6.4%-7.4%] of the entire consortium) were more common than changes of −57% (0.79% [95% CI, 0.52%-1.06%]). This association was strong and consistent across the length of the baseline period (1 to 3 years), baseline estimated GFR, age, diabetes status, or albuminuria. Average adjusted 10-year risk of ESRD (in patients with a baseline estimated GFR of 35 mL/min/1.73 m2) was 99% (95% CI, 95%-100%) for estimated GFR change of −57%, was 83% (95% CI, 71%-93%) for estimated GFR change of −40%, and was 64% (95% CI, 52%-77%) for estimated GFR change of −30% vs 18% (95% CI, 15%-22%) for estimated GFR change of 0%. Corresponding mortality risks were 77% (95% CI, 71%-82%), 60% (95% CI, 56%-63%), and 50% (95% CI, 47%-52%) vs 32% (95% CI, 31%-33%), showing a similar but weaker pattern.
Declines in estimated GFR smaller than a doubling of serum creatinine concentration occurred more commonly and were strongly and consistently associated with the risk of ESRD and mortality, supporting consideration of lesser declines in estimated GFR (such as a 30% reduction over 2 years) as an alternative end point for CKD progression.
已确立的慢性肾脏病 (CKD) 进展终点为终末期肾病 (ESRD) 或血清肌酐浓度加倍(相当于估计肾小球滤过率 [GFR] 的变化−57%或更大)是一个晚期事件。
描述估计肾小球滤过率下降与随后进展为 ESRD 的关联,这对使用较小的估计肾小球滤过率下降作为 CKD 进展的潜在替代终点具有意义。由于大多数 CKD 患者在达到 ESRD 之前死亡,因此也研究了死亡率风险。
对来自 CKD 预后联盟的 35 个队列中的 170 万参与者进行个体荟萃分析,这些参与者的血清肌酐浓度重复测量时间为 1 至 3 年,并且有 12,344 例 ESRD 事件和 223,944 例死亡的结局数据。
2012 年 7 月至 2013 年 9 月之间进行了个体参与者数据的转移或输出的标准化分析,使用从 1975 年至 2012 年收集的基线估计肾小球滤过率值。
与 2 年内估计肾小球滤过率变化相关的 ESRD 和死亡率风险,调整了潜在混杂因素和首次估计肾小球滤过率。
估计肾小球滤过率下降较大时,ESRD 和死亡率的调整后危险比(HRs)更高。在基线估计肾小球滤过率低于 60 mL/min/1.73 m2 的参与者中,ESRD 的调整后 HRs 为估计肾小球滤过率下降−57%时为 32.1(95%CI,22.3-46.3),下降−30%时为 5.4(95%CI,4.5-6.4)。然而,变化−30%或更大(整个联盟的 6.9%[95%CI,6.4%-7.4%])比变化−57%(0.79%[95%CI,0.52%-1.06%])更常见。这种关联在基线期(1 至 3 年)的长度、基线估计肾小球滤过率、年龄、糖尿病状态或白蛋白尿方面都很强且一致。平均调整后的 10 年 ESRD 风险(在基线估计肾小球滤过率为 35 mL/min/1.73 m2 的患者中)为估计肾小球滤过率变化−57%时为 99%(95%CI,95%-100%),估计肾小球滤过率变化−40%时为 83%(95%CI,71%-93%),估计肾小球滤过率变化−30%时为 64%(95%CI,52%-77%),而估计肾小球滤过率变化 0%时为 18%(95%CI,15%-22%)。相应的死亡率风险分别为 77%(95%CI,71%-82%)、60%(95%CI,56%-63%)和 50%(95%CI,47%-52%),而 32%(95%CI,31%-33%),显示出相似但较弱的模式。
血清肌酐浓度加倍以下的估计肾小球滤过率下降更常见,与 ESRD 和死亡率风险强烈且一致相关,支持考虑较小的估计肾小球滤过率下降(例如 2 年内下降 30%)作为 CKD 进展的替代终点。