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Background The establishment of the Oxford classification and newly developed prediction models have improved the prognostic information for immunoglobulin A nephropathy (IgAN). Considering new treatment options, optimizing prognostic information and improving existing prediction models are favorable. Methods We used random forest survival analysis to select possible predictors of end-stage kidney disease among 37 candidate variables in a cohort of 232 patients with biopsy-proven IgAN retrieved from the Norwegian Kidney Biopsy Registry. The predictive value of variables with relative importance >5% was assessed using concordance statistics and the Akaike information criterion. Pearson's correlation coefficient was used to identify correlations between the selected variables. Results The median follow-up period was 13.7 years. An isolated analysis of histological variables identified six variables with relative importance >5%: T %, segmental glomerular sclerosis without characteristics associated with other subtypes (not otherwise specified, NOS), normal glomeruli, global sclerotic glomeruli, segmental adherence and perihilar glomerular sclerosis. When histopathological and clinical variables were combined, estimated glomerular filtration rate (eGFR), proteinuria and serum albumin were added to the list. T % showed a better prognostic value than tubular atrophy/interstitial fibrosis (T) lesions with C-indices at 0.74 and 0.67 and was highly correlated with eGFR. Analysis of the subtypes of segmental glomerulosclerosis (S) lesions revealed that NOS and perihilar glomerular sclerosis were associated with adverse outcomes. Conclusions Reporting T lesions as a continuous variable, normal glomeruli and subtypes of S lesions could provide clinicians with additional prognostic information and contribute to the improved performance of the Oxford classification and prognostic tools. [ABSTRACT FROM AUTHOR]

Background Recently, two immunoglobulin A (IgA) nephropathy-prediction tools were developed that combine clinical and histopathologic parameters. The International IgAN Prediction Tool predicts the risk for 50% declines in the estimated glomerular filtration rate or end-stage kidney disease up to 80 months after diagnosis. The IgA Nephropathy Clinical Decision Support System uses artificial neural networks to estimate the risk for end-stage kidney disease. We aimed to externally validate both prediction tools using a Norwegian cohort with a long-term follow-up. Methods We included 306 patients with biopsy-proven primary IgA nephropathy in this study. Histopathologic samples were retrieved from the Norwegian Kidney Biopsy Registry and reclassified according to the Oxford Classification. We used discrimination and calibration as principles for externally validating the prognostic models. Results The median patient follow-up was 17.1 years. A cumulative, dynamic, time-dependent receiver operating characteristic analysis showed area under the curve values ranging from 0.90 at 5 years to 0.83 at 20 years for the International IgAN Prediction Tool, while time-naive analysis showed an area under the curve value at 0.83 for the IgA Nephropathy Clinical Decision Support System. The International IgAN Prediction Tool was well calibrated, while the IgA Nephropathy Clinical Decision Support System tends to underestimate risk for patients at higher risk and overestimates risk in the lower risk categories. Conclusions We have externally validated two prediction tools for IgA nephropathy. The International IgAN Prediction Tool performed well, while the IgA Nephropathy Clinical Decision Support System has some limitations. [ABSTRACT FROM AUTHOR]