Attenzione: i dati modificati non sono ancora stati salvati. Per confermare inserimenti o cancellazioni di voci è necessario confermare con il tasto SALVA/INSERISCI in fondo alla pagina
The Oxford Classification of IgA nephropathy (IgAN) includes the following four histologic components: mesangial (M) and endocapillary (E) hypercellularity, segmental sclerosis (S) and interstitial fibrosis/tubular atrophy (T). These combine to form the MEST score and are independently associated with renal outcome. Current prediction and risk stratification in IgAN requires clinical data over 2 years of follow-up. Using modern prediction tools, we examined whether combining MEST with cross-sectional clinical data at biopsy provides earlier risk prediction in IgAN than current best methods that use 2 years of follow-up data. We used a cohort of 901 adults with IgAN from the Oxford derivation and North American validation studies and the VALIGA study followed for a median of 5.6 years to analyze the primary outcome (50% decrease in eGFR or ESRD) using Cox regression models. Covariates of clinical data at biopsy (eGFR, proteinuria, MAP) with or without MEST, and then 2-year clinical data alone (2-year average of proteinuria/MAP, eGFR at biopsy) were considered. There was significant improvement in prediction by adding MEST to clinical data at biopsy. The combination predicted the outcome as well as the 2-year clinical data alone, with comparable calibration curves. This effect did not change in subgroups treated or not with RAS blockade or immunosuppression. Thus, combining the MEST score with cross-sectional clinical data at biopsy provides earlier risk prediction in IgAN than our current best methods.
The MEST score provides earlier risk prediction in lgA nephropathy / Barbour, Sean J.; Espino Hernandez, Gabriela; Reich, Heather N.; Coppo, Rosanna; Roberts, Ian S. D.; Feehally, John; Herzenberg, Andrew M.; Cattran, Daniel C; Bavbek, N.; Cook, T.; Troyanov, S.; Alpers, C.; Amore, A.; Barratt, J.; Berthoux, F.; Bonsib, S.; Bruijn, J.; D'Agati, V.; D'Amico, G.; Emancipator, S.; Emmal, F.; Ferrario, F.; Fervenza, F.; Florquin, S.; Fogo, A.; Geddes, C.; Groene, H.; Haas, M.; Hill, P.; Hogg, R.; Hsu, S.; Hunley, T.; Hladunewich, M.; 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.; Zhang, H.; Coppo, R.; Troyanov, S.; Cattran, D. C.; Cook, H. T.; Feehally, J.; 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.; Angioi, A.; Piras, L.; Cancarini, G.; Ravera, S.; Durlik, M.; Moggia, E.; Ballarin, J.; Di Giulio, S.; Pugliese, F.; Serriello, I.; Caliskan, Y.; Sever, M.; Kilicaslan, I.; Locatelli, F.; Del Vecchio, L.; Wetzels, J. F. M.; Peters, H.; Berg, U.; Carvalho, F.; Da Costa Ferreira, A. C.; Maggio, M.; Wiecek, A.; Ots Rosenberg, M.; Magistroni, Riccardo; 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.; Fellstrom, B.; Kloster Smerud, H.; Ferrario, F.; Stellato, T.; Egido, J.; Martin, C.; Floege, J.; Eitner, F.; Lupo, A.; Bernich, P.; Menè, P.; Morosetti, M.; Van Kooten, C.; Rabelink, T.; Reinders, M. E. J.; Boria Grinyo, J. M.; Cusinato, S.; Benozzi, L.; Savoldi, S.; Licata, C.; Mizerska Wasiak, M.; Martina, G.; Messuerotti, A.; Dal Canton, A.; Esposito, C.; Migotto, C.; Triolo, G.; Mariano, F.; Pozzi, C.; Boero, R.; Bellur, S.; Mazzucco, G.; Giannakakis, C.; Honsova, E.; Sundelin, B.; Di Palma, A. M.; Ferrario, F.; Gutiérrez, E.; Asunis, A. M.; Barratt, J.; Tardanico, R.; Perkowska Ptasinska, A.; Arce Terroba, J.; Fortunato, M.; Pantzaki, A.; Ozluk, Y.; Steenbergen, E.; Soderberg, M.; Riispere, Z.; Furci, L.; Orhan, D.; Kipgen, D.; Casartelli, D.; Galesic Ljubanovic, D.; Gakiopoulou, H.; Bertoni, E.; Cannata Ortiz, P.; Karkoszka, H.; Groene, H. J.; Stoppacciaro, A.; Bajema, I.; Bruijn, J.; Fulladosa Oliveras, X.; Maldyk, J.; Ioachim, E.. - In: KIDNEY INTERNATIONAL. - ISSN 0085-2538. - 89:1(2016), pp. 167-175. [10.1038/ki.2015.322]
The MEST score provides earlier risk prediction in lgA nephropathy
The Oxford Classification of IgA nephropathy (IgAN) includes the following four histologic components: mesangial (M) and endocapillary (E) hypercellularity, segmental sclerosis (S) and interstitial fibrosis/tubular atrophy (T). These combine to form the MEST score and are independently associated with renal outcome. Current prediction and risk stratification in IgAN requires clinical data over 2 years of follow-up. Using modern prediction tools, we examined whether combining MEST with cross-sectional clinical data at biopsy provides earlier risk prediction in IgAN than current best methods that use 2 years of follow-up data. We used a cohort of 901 adults with IgAN from the Oxford derivation and North American validation studies and the VALIGA study followed for a median of 5.6 years to analyze the primary outcome (50% decrease in eGFR or ESRD) using Cox regression models. Covariates of clinical data at biopsy (eGFR, proteinuria, MAP) with or without MEST, and then 2-year clinical data alone (2-year average of proteinuria/MAP, eGFR at biopsy) were considered. There was significant improvement in prediction by adding MEST to clinical data at biopsy. The combination predicted the outcome as well as the 2-year clinical data alone, with comparable calibration curves. This effect did not change in subgroups treated or not with RAS blockade or immunosuppression. Thus, combining the MEST score with cross-sectional clinical data at biopsy provides earlier risk prediction in IgAN than our current best methods.
Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1116596
Citazioni
83
170
161
social impact
Conferma cancellazione
Sei sicuro che questo prodotto debba essere cancellato?
simulazione ASN
La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. L’Università di Modena e Reggio Emilia non si assume alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione.
