Semiquantitative classification (SQC) and Oxford classifications predict poor renal outcome better than The International Study of Kidney Disease in Children (ISKDC) and Haas in patients with IgAV nephritis: a multicenter study.

Introduction: Several histologic classifications are used in the evaluation of IgA vasculitis nephritis (IgAVN), however, to date, no studies have determined which one has the strongest association with the severity of IgAVN and, as a consequence, its outcomes.

Materials And Methods: Patients included in the study were diagnosed with IgAV and IgAVN in seven tertiary university medical centers in Croatia, Italy and Israel. The International Study of Kidney Disease in Children (ISKDC), Haas, Oxford, and Semiquantitative classification (SQC) classifications were used in the analysis and description of renal biopsy. Time from biopsy to outcome evaluation was a statistically significant factor in outcome prediction that was used to define the base model, and was a covariate in all the tested models.

Results: Sixty-seven patients were included in this study. The SQC classification proved to be the best one in outcome prediction, followed by the Oxford classification. The ISKDC and Haas classifications could not predict renal outcome. The Oxford parameters for mesangial hypercellularity and tubular atrophy, as well as the SQC parameters for cellular crescents showed an independent statistically significant contribution to outcome prediction. High level of twenty-four hour protein excretion was associated with a higher grade in the Oxford, SQC and ISKDC classifications. Endocapillary proliferation was positively associated with the Pediatric Vasculitis Activity Score (PVAS) at diagnosis, while tubular atrophy was negatively associated.

Conclusion: The SQC, followed by the Oxford classification were found to provide the best classifications of renal biopsy analysis in patients to predict the outcome in patients with IgAVN. Cellular crescents, mesangial hypercellularity and tubular atrophy showed significant contributions, indicating that active and chronic variables should be included in the estimation.