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The MEST score provides earlier risk prediction in IgA nephropathy

Date
2016
Author
ROBERTS, Ian S. D.
Cattran, Daniel C.
COPPO, Rosanna
FEEHALLY, John
Herzenberg, Andrew M.
Barbour, Sean J.
Espino-Hernandez, Gabriela
Reich, Heather N.
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Abstract
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.
URI
http://hdl.handle.net/20.500.12627/6456
https://doi.org/10.1038/ki.2015.322
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Creative Commons Lisansı

İstanbul Üniversitesi Akademik Arşiv Sistemi (ilgili içerikte aksi belirtilmediği sürece) Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

DSpace software copyright © 2002-2016  DuraSpace
Contact Us | Send Feedback
Theme by 
Atmire NV