Individualized treatment effects of corticosteroids in IgA nephropathy

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Abstract
IgA nephropathy (IgAN) is a leading cause of kidney failure with diverse clinical presentations and treatment responses, particularly to corticosteroids, with conflicting evidence. Due to potentially severe side effects and heterogenous responses more individualized approaches are needed. We developed a causal machine learning framework for predicting individualized treatment effects of corticosteroids in IgAN by integrating clinical variables, histopathological scores, and deep learning-based biomarkers from digitized kidney biopsies (pathomics) of 1,022 patients from eight retrospective international cohorts. At the cohort-level, corticosteroids showed no significant effect on five-year kidney survival. However, the framework identified subpopulations with and without significant treatment benefit, improving progression-free kidney survival and also reducing overtreatment in low-benefit patients. Pathomics highlighted tubulointerstitial inflammation and glomerular tuft deformation as predictors of corticosteroid response. Our framework offers a blueprint for precision therapy in IgAN, supporting clinical decision-making in the era of emerging targeted treatments.

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