The MEST score provides earlier risk prediction in lgA nephropathy
Publication year
2016Source
Kidney International. Supplement, 89, 1, (2016), pp. 167-75ISSN
Publication type
Article / Letter to editor
Display more detailsDisplay less details
Organization
Nephrology
Journal title
Kidney International. Supplement
Volume
vol. 89
Issue
iss. 1
Page start
p. 167
Page end
p. 75
Subject
Radboudumc 11: Renal disorders RIHS: Radboud Institute for Health SciencesAbstract
The Oxford Classification of IgA nephropathy (IgAN) includes the following four histologic components: mesangial (M) and endocapillary (E) hypercellularity, segmental sclerosis (S) and interstitial fibrosis/tubular atrophy (T). These combine to form the MEST score and are independently associated with renal outcome. Current prediction and risk stratification in IgAN requires clinical data over 2 years of follow-up. Using modern prediction tools, we examined whether combining MEST with cross-sectional clinical data at biopsy provides earlier risk prediction in IgAN than current best methods that use 2 years of follow-up data. We used a cohort of 901 adults with IgAN from the Oxford derivation and North American validation studies and the VALIGA study followed for a median of 5.6 years to analyze the primary outcome (50% decrease in eGFR or ESRD) using Cox regression models. Covariates of clinical data at biopsy (eGFR, proteinuria, MAP) with or without MEST, and then 2-year clinical data alone (2-year average of proteinuria/MAP, eGFR at biopsy) were considered. There was significant improvement in prediction by adding MEST to clinical data at biopsy. The combination predicted the outcome as well as the 2-year clinical data alone, with comparable calibration curves. This effect did not change in subgroups treated or not with RAS blockade or immunosuppression. Thus, combining the MEST score with cross-sectional clinical data at biopsy provides earlier risk prediction in IgAN than our current best methods.
This item appears in the following Collection(s)
- Academic publications [246625]
- Faculty of Medical Sciences [93367]
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.