Cohort profile: the Chronic Kidney Disease Prognosis Consortium.

Matsushita, K; Ballew, SH; Astor, BC; Jong, PE; Gansevoort, RT; Hemmelgarn, BR; Levey, AS; Levin, A; Wen, CP; Woodward, M; Coresh, J; Chronic Kidney Disease Prognosis Consortium, (incd. Fletcher, AE; ), ; (2013) Cohort profile: the Chronic Kidney Disease Prognosis Consortium. International journal of epidemiology, 42 (6). pp. 1660-8. ISSN 0300-5771 DOI:

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The Chronic Kidney Disease Prognosis Consortium (CKD-PC) was established in 2009 to provide comprehensive evidence about the prognostic impact of two key kidney measures that are used to define and stage CKD, estimated glomerular filtration rate (eGFR) and albuminuria, on mortality and kidney outcomes. CKD-PC currently consists of 46 cohorts with data on these kidney measures and outcomes from >2 million participants spanning across 40 countries/regions all over the world. CKD-PC published four meta-analysis articles in 2010-11, providing key evidence for an international consensus on the definition and staging of CKD and an update for CKD clinical practice guidelines. The consortium continues to work on more detailed analysis (subgroups, different eGFR equations, other exposures and outcomes, and risk prediction). CKD-PC preferably collects individual participant data but also applies a novel distributed analysis model, in which each cohort runs statistical analysis locally and shares only analysed outputs for meta-analyses. This distributed model allows inclusion of cohorts which cannot share individual participant level data. According to agreement with cohorts, CKD-PC will not share data with third parties, but is open to including further eligible cohorts. Each cohort can opt in/out for each topic. CKD-PC has established a productive and effective collaboration, allowing flexible participation and complex meta-analyses for studying CKD.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology
PubMed ID: 23243116
Web of Science ID: 329870400027


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