Evaluating a New International Risk-Prediction Tool in IgA Nephropathy

Barbour, S.J.; Coppo, R.; Zhang, H.; Liu, Z.H.; Suzuki, Y.; Matsuzaki, K.; ... ; Int IgA Nephropathy Network (2019)

Article / Letter to editor

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.

• All authors: Barbour, S.J.; Coppo, R.; Zhang, H.; Liu, Z.H.; Suzuki, Y.; Matsuzaki, K.; Katafuchi, R.; Er, L.; Espino-Hernandez, G.; Kim, S.J.; Reich, H.N.; Feehally, J.; Cattran, D.C.; Russo, M.L.; Troyanov, S.; Cook, H.T.; Roberts, I.; Tesar, V.; Maixnerova, D.; Lundberg, S.; Gesualdo, L.; Emma, F.; Fuiano, L.; Beltrame, G.; Rollino, C.; Amore, A.; Camilla, R.; Peruzzi, L.; Praga, M.; Feriozzi, S.; Polci, R.; Segoloni, G.; Colla, L.; Pani, A.; Piras, D.; Angioi, A.; Cancarini, G.; Ravera, S.; Durlik, M.; Moggia, E.; Ballarin, J.; Giulio, S. di; Pugliese, F.; Serriello, I.; Caliskan, Y.; Sever, M.; Kilicaslan, I.; Locatelli, F.; Vecchio, L. del; Wetzels, J.F.M.; Peters, H.; Berg, U.; Carvalho, F.; Ferreira, A.C.D.; Maggio, M.; Wiecek, A.; Ots-Rosenberg, M.; Magistroni, R.; Topaloglu, R.; Bilginer, Y.; D'Amico, M.; Stangou, M.; Giacchino, F.; Goumenos, D.; Kalliakmani, P.; Gerolymos, M.; Galesic, K.; Geddes, C.; Siamopoulos, K.; Balafa, O.; Galliani, M.; Stratta, P.; Quaglia, M.; Bergia, R.; Cravero, R.; Salvadori, M.; Cirami, L.; Fellstrorn, B.; Smerud, H.K.; Ferrario, F.; Stellato, T.; Egido, J.; Martin, C.; Floege, J.; Eitner, F.; Lupo, A.; Bernich, P.; Mene, R.; Morosetti, M.; Kooten, C. van; Rabelink, T.; Reinders, M.E.J.; Grinyo, J.M.B.; Cusinato, S.; Benozzi, L.; Savoldi, S.; Licata, C.; Mizerska-Wasiak, M.; Martina, G.; Messuerotti, A.; Canton, A. dal; Esposito, C.; Migotto, C.; Triolo, G.; Mariano, F.; Pozzi, C.; Boero, R.; Bellur, S.; Mazzucco, G.; Giannakakis, C.; Honsova, E.; Sundelin, B.; Palma, A.M. di; Gutierrez, E.; Asunis, A.M.; Barratt, J.; Tardanico, R.; Perkowska-Ptasinska, A.; Terroba, J.A.; Fortunato, M.; Pantzaki, A.; Ozluk, Y.; Steenbergen, E.; Soderberg, M.; Riispere, Z.; Furci, L.; Orhan, D.; Kipgen, D.; Casartelli, D.; Ljubanovic, D.G.; Gakiopoulou, H.; Bertoni, E.; Ortiz, P.C.; Karkoszka, H.; Groene, H.J.; Stoppacciaro, A.; Bajema, I.; Bruijn, J.; Oliveras, X.F.; Maldyk, J.; Loachim, E.; Bavbek, N.; Cook, T.; Alpers, C.; Berthoux, F.; Bonsib, S.; D'Agati, V.; D'Amico, G.; Emancipator, S.; Emmal, F.; Fervenza, F.; Florquin, S.; Fogo, A.; Groene, H.; Haas, M.; Hill, P.; Hogg, R.; Hsu, S.; Hunley, T.; Hladunewich; Jennette, C.; Joh, K.; Julian, B.; Kawamura, T.; Lai, F.; Leung, C.; Li, L.; Li, P.; Liu, Z.; Massat, A.; Mackinnon, B.; Mezzano, S.; Schena, F.; Tomino, Y.; Walker, P.; Wang, H.; Weening, J.; Yoshikawa, N.; Zeng, C.H.; Shi, S.F.; Nogi, C.; Suzuki, H.; Koike, K.; Hirano, K.; Yokoo, T.; Hanai, M.; Fukami, K.; Takahashi, K.; Yuzawa, Y.; Niwa, M.; Yasuda, Y.; Maruyama, S.; Ichikawa, D.; Suzuki, T.; Shirai, S.; Fukuda, A.; Fujimoto, S.; Trimarchi, H.; Int IgA Nephropathy Network
• Date: 2019-07-01
• Journal: JAMA Internal Medicine
• Volume: 179
• Issue: 7
• Pages: 942 - 952
• DOI: doi:10.1001/jamainternmed.2019.0600