Platelet-to-Albumin Ratio: A Novel IgA Nephropathy Prognosis Predictor

Since the Oxford Classification of IgA nephropathy (IgAN) was published in 2009, MEST scores have been increasingly used in clinical practice. Further retrospective cohort studies have confirmed that in biopsy specimens with a minimum of 8 glomeruli, mesangial hypercellularity (M), segmental sclerosis (S), and interstitial fibrosis/tubular atrophy (T) lesions predict clinical outcome. In a larger, more broadly based cohort than in the original Oxford study, crescents (C) are predictive of outcome, and we now recommend that C be added to the MEST score, and biopsy reporting should provide a MEST-C score. Inconsistencies in the reporting of M and endocapillary cellularity (E) lesions have been reported, so a web-based educational tool to assist pathologists has been developed. A large study showed E lesions are predictive of outcome in children and adults, but only in those without immunosuppression. A review of S lesions suggests there may be clinical utility in the subclassification of segmental sclerosis, identifying those cases with evidence of podocyte damage. It has now been shown that combining the MEST score with clinical data at biopsy provides the same predictive power as monitoring clinical data for 2 years; this requires further evaluation to assess earlier effective treatment intervention. The IgAN Classification Working Group has established a well-characterized dataset from a large cohort of adults and children with IgAN that will provide a substrate for further studies to refine risk prediction and clinical utility, including the MEST-C score and other factors. Show more

IgA nephropathy (IgAN) is a leading cause of CKD and renal failure. Recent international collaborative efforts have led to important discoveries that have improved our understanding of some of the key steps involved in the immunopathogenesis of IgAN. Furthermore, establishment of multicenter networks has contributed to rigorous design and execution of clinical trials that have provided important insights regarding immunotherapy in IgAN. In this article, we review emerging developments in clinical and translational IgAN research and describe how these novel findings will influence future strategies to improve the outcome of patients with IgAN. Show more

Propensity-score matching is increasingly being used to estimate the effects of treatments using observational data. In many-to-one (M:1) matching on the propensity score, M untreated subjects are matched to each treated subject using the propensity score. The authors used Monte Carlo simulations to examine the effect of the choice of M on the statistical performance of matched estimators. They considered matching 1–5 untreated subjects to each treated subject using both nearest-neighbor matching and caliper matching in 96 different scenarios. Increasing the number of untreated subjects matched to each treated subject tended to increase the bias in the estimated treatment effect; conversely, increasing the number of untreated subjects matched to each treated subject decreased the sampling variability of the estimated treatment effect. Using nearest-neighbor matching, the mean squared error of the estimated treatment effect was minimized in 67.7% of the scenarios when 1:1 matching was used. Using nearest-neighbor matching or caliper matching, the mean squared error was minimized in approximately 84% of the scenarios when, at most, 2 untreated subjects were matched to each treated subject. The authors recommend that, in most settings, researchers match either 1 or 2 untreated subjects to each treated subject when using propensity-score matching. Show more