51. Evaluating a New International Risk-Prediction Tool in IgA Nephropathy
- Author
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Barbour, Sean J. Coppo, Rosanna Zhang, Hong Liu, Zhi-Hong and Suzuki, Yusuke Matsuzaki, Keiichi Katafuchi, Ritsuko Er, Lee Espino-Hernandez, Gabriela Kim, S. Joseph Reich, Heather N. Feehally, John Cattran, Daniel C. Russo, M. L. and Troyanov, S. Cook, H. T. Roberts, I. Tesar, V. and Maixnerova, D. Lundberg, S. Gesualdo, L. Emma, F. and Fuiano, L. Beltrame, G. Rollino, C. Amore, A. Camilla, R. Peruzzi, L. Praga, M. Feriozzi, S. Polci, R. and Segoloni, G. Colla, L. Pani, A. Piras, D. Angioi, A. and Cancarini, G. Ravera, S. Durlik, M. Moggia, E. Ballarin, J. Di Giulio, S. Pugliese, F. Serriello, I. Caliskan, Y. and Sever, M. Kilicaslan, I. Locatelli, F. Del Vecchio, L. and Wetzels, J. F. M. Peters, H. Berg, U. Carvalho, F. and da Costa Ferreira, A. C. Maggio, M. Wiecek, A. and Ots-Rosenberg, M. Magistroni, R. Topaloglu, R. Bilginer, Y. and D'Amico, M. Stangou, M. Giacchino, F. Goumenos, D. and Kalliakmani, P. Gerolymos, M. Galesic, K. Geddes, C. and Siamopoulos, K. Balafa, O. Galliani, M. Stratta, P. and Quaglia, M. Bergia, R. Cravero, R. Salvadori, M. Cirami, L. Fellstrorn, B. Smerud, H. Kloster Ferrario, F. and Stellato, T. Egido, J. Martin, C. Floege, J. Eitner, F. and Lupo, A. Bernich, P. Mene, R. Morosetti, M. van Kooten, C. Rabelink, T. Reinders, M. E. J. Boria Grinyo, J. M. Cusinato, S. Benozzi, L. Savoldi, S. Licata, C. and Mizerska-Wasiak, M. Martina, G. Messuerotti, A. Dal Canton, A. Esposito, C. Migotto, C. Triolo, G. Mariano, F. and Pozzi, C. Boero, R. Bellur, S. Mazzucco, G. Giannakakis, C. Honsova, E. Sundelin, B. Di Palma, A. M. Ferrario, F. and Gutierrez, E. Asunis, A. M. Barratt, J. Tardanico, R. and Perkowska-Ptasinska, A. Arce Terroba, J. Fortunato, M. and Pantzaki, A. Ozluk, Y. Steenbergen, E. Soderberg, M. and Riispere, Z. Furci, L. Orhan, D. Kipgen, D. Casartelli, D. Ljubanovic, D. Galesic Gakiopoulou, H. Bertoni, E. and Cannata Ortiz, P. Karkoszka, H. Groene, H. J. Stoppacciaro, A. Bajema, I. Bruijn, J. Fulladosa Oliveras, X. Maldyk, J. Loachim, E. Bavbek, N. Cook, T. Troyanov, S. and Alpers, C. Amore, A. Barratt, J. Berthoux, F. Bonsib, S. and Bruijn, J. D'Agati, V D'Amico, G. Emancipator, S. and Emmal, F. Ferrario, F. Fervenza, F. Florquin, S. Fogo, A. Geddes, C. Groene, H. Haas, M. Hill, P. Hogg, R. and Hsu, S. Hunley, T. Hladunewich Jennette, C. Joh, K. and Julian, B. Kawamura, T. Lai, F. Leung, C. Li, L. and Li, P. Liu, Z. Massat, A. Mackinnon, B. Mezzano, S. and Schena, F. Tomino, Y. Walker, P. Wang, H. Weening, J. and Yoshikawa, N. Zeng, Cai-Hong Shi, Sufang Nogi, C. and Suzuki, H. Koike, K. Hirano, K. Kawamura, T. Yokoo, T. and Hanai, M. Fukami, K. Takahashi, K. Yuzawa, Y. Niwa, M. Yasuda, Y. Maruyama, S. Ichikawa, D. Suzuki, T. and Shirai, S. Fukuda, A. Fujimoto, S. Trimarchi, H. Int IgA Nephropathy Network
- Abstract
ImportanceAlthough IgA nephropathy (IgAN) is the most common glomerulonephritis in the world, there is no validated tool to predict disease progression. This limits patient-specific risk stratification and treatment decisions, clinical trial recruitment, and biomarker validation. ObjectiveTo derive and externally validate a prediction model for disease progression in IgAN that can be applied at the time of kidney biopsy in multiple ethnic groups worldwide. Design, Setting, and ParticipantsWe derived and externally validated a prediction model using clinical and histologic risk factors that are readily available in clinical practice. Large, multi-ethnic cohorts of adults with biopsy-proven IgAN were included from Europe, North America, China, and Japan. Main Outcomes and MeasuresCox proportional hazards models were used to analyze the risk of a 50% decline in estimated glomerular filtration rate (eGFR) or end-stage kidney disease, and were evaluated using the R-D(2) measure, Akaike information criterion (AIC), C statistic, continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI), and calibration plots. ResultsThe study included 3927 patients; mean age, 35.4 (interquartile range, 28.0-45.4) years; and 2173 (55.3%) were men. The following prediction models were created in a derivation cohort of 2781 patients: a clinical model that included eGFR, blood pressure, and proteinuria at biopsy; and 2 full models that also contained the MEST histologic score, age, medication use, and either racial/ethnic characteristics (white, Japanese, or Chinese) or no racial/ethnic characteristics, to allow application in other ethnic groups. Compared with the clinical model, the full models with and without race/ethnicity had better R-D(2) (26.3% and 25.3%, respectively, vs 20.3%) and AIC (6338 and 6379, respectively, vs 6485), significant increases in C statistic from 0.78 to 0.82 and 0.81, respectively (Delta C, 0.04; 95% CI, 0.03-0.04 and Delta C, 0.03; 95% CI, 0.02-0.03, respectively), and significant improvement in reclassification as assessed by the NRI (0.18; 95% CI, 0.07-0.29 and 0.51; 95% CI, 0.39-0.62, respectively) and IDI (0.07; 95% CI, 0.06-0.08 and 0.06; 95% CI, 0.05-0.06, respectively). External validation was performed in a cohort of 1146 patients. For both full models, the C statistics (0.82; 95% CI, 0.81-0.83 with race/ethnicity; 0.81; 95% CI, 0.80-0.82 without race/ethnicity) and R-D(2) (both 35.3%) were similar or better than in the validation cohort, with excellent calibration. Conclusions and RelevanceIn this study, the 2 full prediction models were shown to be accurate and validated methods for predicting disease progression and patient risk stratification in IgAN in multi-ethnic cohorts, with additional applications to clinical trial design and biomarker research.
- Published
- 2019