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BACKGROUND: Since primary membranous nephropathy is a heterogeneous disease with variable outcomes and multiple possible therapeutic approaches, all 13 Nephrology Units of the Italian region Emilia Romagna decided to analyze their experience in the management of this challenging glomerular disease. METHODS: We retrospectively studied 205 consecutive adult patients affected by biopsy-proven primary membranous nephropathy, recruited from January 2010 through December 2017. The primary outcome was patient and renal survival. The secondary outcome was the rate of complete remission and partial remission of proteinuria. Relapse incidence, treatment patterns and adverse events were also assessed. RESULTS: Median (IQR) follow-up was 36 (24-60) months. Overall patient and renal survival were 87.4% after 5 years. At the end of follow-up, 83 patients (40%) had complete remission and 72 patients (35%) had partial remission. Among responders, less than a quarter (23%) relapsed. Most patients (83%) underwent immunosuppressive therapy within 6 months of biopsy. A cyclic regimen of corticosteroid and cytotoxic agents was the most commonly used treatment schedule (63%), followed by rituximab (28%). Multivariable analysis showed that the cyclic regimen significantly correlates with complete remission (odds ratio 0.26; 95% CI 0.08-0.79) when compared to rituximab (p < 0.05). CONCLUSIONS: In our large study, both short- and long-term outcomes were positive and consistent with those published in the literature. Our data suggest that the use of immunosuppressive therapy within the first 6 months after biopsy appears to be a winning strategy, and that the cyclic regimen also warrants a prominent role in primary membranous nephropathy treatment, since definitive proof of rituximab superiority is lacking.
RESUMO
BACKGROUND AND OBJECTIVES: Digital pathology and artificial intelligence offer new opportunities for automatic histologic scoring. We applied a deep learning approach to IgA nephropathy biopsy images to develop an automatic histologic prognostic score, assessed against ground truth (kidney failure) among patients with IgA nephropathy who were treated over 39 years. We assessed noninferiority in comparison with the histologic component of currently validated predictive tools. We correlated additional histologic features with our deep learning predictive score to identify potential additional predictive features. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Training for deep learning was performed with randomly selected, digitalized, cortical Periodic acid-Schiff-stained sections images (363 kidney biopsy specimens) to develop our deep learning predictive score. We estimated noninferiority using the area under the receiver operating characteristic curve (AUC) in a randomly selected group (95 biopsy specimens) against the gold standard Oxford classification (MEST-C) scores used by the International IgA Nephropathy Prediction Tool and the clinical decision supporting system for estimating the risk of kidney failure in IgA nephropathy. We assessed additional potential predictive histologic features against a subset (20 kidney biopsy specimens) with the strongest and weakest deep learning predictive scores. RESULTS: We enrolled 442 patients; the 10-year kidney survival was 78%, and the study median follow-up was 6.7 years. Manual MEST-C showed no prognostic relationship for the endocapillary parameter only. The deep learning predictive score was not inferior to MEST-C applied using the International IgA Nephropathy Prediction Tool and the clinical decision supporting system (AUC of 0.84 versus 0.77 and 0.74, respectively) and confirmed a good correlation with the tubolointerstitial score (r=0.41, P<0.01). We observed no correlations between the deep learning prognostic score and the mesangial, endocapillary, segmental sclerosis, and crescent parameters. Additional potential predictive histopathologic features incorporated by the deep learning predictive score included (1) inflammation within areas of interstitial fibrosis and tubular atrophy and (2) hyaline casts. CONCLUSIONS: The deep learning approach was noninferior to manual histopathologic reporting and considered prognostic features not currently included in MEST-C assessment. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_07_26_CJN01760222.mp3.
