The MEST score provides earlier risk prediction in lgA nephropathy
2015; Elsevier BV; Volume: 89; Issue: 1 Linguagem: Inglês
10.1038/ki.2015.322
ISSN1523-1755
AutoresSean J. Barbour, Gabriela Espino-Hernández, Heather N. Reich, Rosanna Coppo, Stephen A. Roberts, John Feehally, Andrew M. Herzenberg, Daniel C. Cattran, Nüket Bavbek, Terry Cook, S. Troyanov, Charles E. Alpers, Alfonso Amore, Jonathan Barratt, F. Berthoux, Stephen M. Bonsib, Jan A. Bruijn, Vivette D. D’Agati, Giuseppe D’Amico, Steven N. Emancipator, F. Emmal, Franco Ferrario, Fernando C. Fervenza, Sandrine Florquin, Agnes B. Fogo, Colin Geddes, Hermann-Josef Groene, Mark Haas, P. Hill, Ronald J. Hogg, Stephen I‐Hong Hsu, Tracy E. Hunley, Michelle Hladunewich, Caroline E. Jennette, Kensuke Joh, Bruce A. Julian, Takeshi Kawamura, F M Lai, Chi Bon Leung, L. Li, P. Li, Zhihong Liu, Alfonso Eirin, Bruce Mackinnon, Sergio Mezzano, Francesco Paolo Schena, Yasuhiko Tomino, Patrick D. Walker, H. Wang, Jan J. Weening, Nori Yoshikawa, H. Zhang, Rosanna Coppo, S. Troyanov, Daniel C. Cattran, H. Terence Cook, John Feehally, Stephen A. Roberts, Vladimı́r Tesař, Dita Maixnerová, Sigrid Lundberg, Loreto Gesualdo, Francesco Emma, Laura Fuiano, G. Beltrame, Cristiana Rollino, Rc, Alfonso Amore, Roberta Camilla, Licia Peruzzi, Manuel Praga, Sandro Feriozzi, Rosaria Polci, Giuseppe Segoloni, Loredana Colla, Antonello Pani, Andrea Angioi, Lisa Adele Piras, Jf, Giovanni Cancarini, S. Ravera, Magdalena Durlik, Elisabetta Moggia, José Ballarín, S. Di Giulio, Francesco Pugliese, I. Serriello, Yaşar Çalışkan, Mehmet Şükrü Sever, İşın Kiliçaslan, Francesco Locatelli, Lucia Del Vecchio, Jack F.M. Wetzels, Harm Peters, U. Berg, Fernanda Carvalho, A.C. da Costa Ferreira, M. Maggio, Andrzej Więcek, Mai Ots-Rosenberg, Riccardo Magistroni, Rezan Topaloğlu, Yelda Bilginer, Marco DʼAmico, Μaria Stangou, F Giacchino, Dimitrios Goumenos, Pantelitsa Kalliakmani, Miltiadis Gerolymos, Kres̆imir Gales̃ić, Colin Geddes, Konstantinos Siamopoulos, Olga Balafa, Marco Galliani, Piero Stratta, Marco Quaglia, R Bergia, Raffaella Cravero, Maurizio Salvadori, Lino Cirami, Bengt Fellström, Hilde Kloster Smerud, Franco Ferrario, T. Stellato, Jesús Egido, Carina Aguilar Martín, Jürgen Floege, Frank Eitner, Antonio Lupo, Patrizia Bernich, Paolo Menè, Massimo Morosetti, Cees van Kooten, Ton J. Rabelink, Marlies E. J. Reinders, J.M. Boria Grinyo, Stefano Cusinato, Luisa Benozzi, Silvana Savoldi, C. Licata, Małgorzata Mizerska-Wasiak, G Martina, A Messuerotti, Antonio Dal Canton, Ciro Esposito, C. Migotto, G Triolo, F. Mariano, Claudio Pozzi, R Boero, Shubha S. Bellur, Gianna Mazzucco, C. Giannakakis, E Honsová, B. Sundelin, Anna Maria Di Palma, Franco Ferrario, Ester Gutiérrez, A.M. Asunis, Jonathan Barratt, Regina Tardanico, Agnieszka Perkowska‐Ptasińska, J. Arce Terroba, M. Fortunato, Afroditi Pantzaki, Yasemin Özlük, E. J. Steenbergen, Magnus Söderberg, Živile Riispere, Luciana Furci, Dıclehan Orhan, David Kipgen, Donatella Casartelli, Danica Galešić Ljubanović, Hariklia Gakiopoulou, E. Bertoni, Pablo Cannata Ortiz, Henryk Karkoszka, Hermann-Josef Groene, Antonella Stoppacciaro, Ingeborg M. Bajema, Jan A. Bruijn, Xavier Fulladosa, Jadwiga Małdyk, E. Ioachim,
Tópico(s)Systemic Sclerosis and Related Diseases
ResumoThe 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 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. Until recently, there has not been a reproducible and validated histologic classification of IgA nephropathy (IgAN). The MEST score as part of the Oxford Classification overcomes these obstacles, and various components of its score have been validated in multiple studies worldwide to be associated with hard renal outcomes independent of kidney function, blood pressure, and proteinuria both at presentation and over time.1Cattran D.C. Coppo R. Cook H.T. et al.The Oxford classification of IgA nephropathy: rationale, clinicopathological correlations, and classification.Kidney Int. 2009; 76: 534-545Abstract Full Text Full Text PDF PubMed Scopus (896) Google Scholar, 2Coppo R. Troyanov S. Bellur S. et al.Validation of the Oxford classification of IgA nephropathy in cohorts with different presentations and treatments.Kidney Int. 2014; 86: 828-836Abstract Full Text Full Text PDF PubMed Scopus (297) Google Scholar, 3Herzenberg A.M. Fogo A.B. Reich H.N. et al.Validation of the Oxford classification of IgA nephropathy.Kidney Int. 2011; 80: 310-317Abstract Full Text Full Text PDF PubMed Scopus (152) Google Scholar, 4Roberts I.S. Cook H.T. Troyanov S. et al.The Oxford classification of IgA nephropathy: pathology definitions, correlations, and reproducibility.Kidney Int. 2009; 76: 546-556Abstract Full Text Full Text PDF PubMed Scopus (757) Google Scholar, 5Lv J. Shi S. Xu D. et al.Evaluation of the Oxford Classification of IgA nephropathy: a systematic review and meta-analysis.Am J Kidney Dis. 2013; 62: 891-899Abstract Full Text Full Text PDF PubMed Scopus (124) Google Scholar However, it remains largely unknown whether the MEST score can quantitatively improve the prediction of individual patient prognosis and guide management decisions at the time of biopsy. The current approach to determining the risk of renal progression in IgAN using clinical data alone is challenging owing to the highly variable nature of the disease. Previous studies suggest that 2 years or longer of follow-up proteinuria and blood pressure data is needed before a clinically meaningful prediction can be achieved.6Reich H.N. Troyanov S. Scholey J.W. et al.Remission of proteinuria improves prognosis in IgA nephropathy.J Am Soc Nephrol. 2007; 18: 3177-3183Crossref PubMed Scopus (410) Google Scholar, 7Tanaka S. Ninomiya T. Katafuchi R. et al.Development and validation of a prediction rule using the Oxford classification in IgA nephropathy.Clin J Am Soc Nephrol. 2013; 8: 2082-2090Crossref PubMed Scopus (80) Google Scholar, 8Le W. Liang S. Hu Y. et al.Long-term renal survival and related risk factors in patients with IgA nephropathy: results from a cohort of 1155 cases in a Chinese adult population.Nephrol Dial Transplant. 2012; 27: 1479-1485Crossref PubMed Scopus (229) Google Scholar, 9Barbour S.J. Reich H.N. Risk stratification of patients with IgA nephropathy.Am J Kidney Dis. 2012; 59: 865-873Abstract Full Text Full Text PDF PubMed Scopus (128) Google Scholar, 10Bartosik L.P. Lajoie G. Sugar L. et al.Predicting progression in IgA nephropathy.Am J Kidney Dis. 2001; 38: 728-735Abstract Full Text Full Text PDF PubMed Scopus (308) Google Scholar, 11Mackinnon B. Fraser E.P. Cattran D.C. et al.Validation of the Toronto formula to predict progression in IgA nephropathy.Nephron Clin Pract. 2008; 109: c148-c153Crossref PubMed Scopus (27) Google Scholar This approach has limited utility in clinical practice given current guidelines that recommend treatment decisions based mostly on clinical features near the time of biopsy.12Kidney Disease: Improving Global Outcomes (KDIGO) Glomerulonephritis Work GroupKDIGO Clinical Practice Guideline for Glomerulonephritis.Kidney Int Suppl. 2012; 2: 139-274Abstract Full Text Full Text PDF Scopus (789) Google Scholar We hypothesize that by adding the MEST score from the Oxford Classification to clinical data available at the time of biopsy, we can improve risk stratification earlier in the course of disease and predict the risk of renal outcome to the same degree as using longitudinal blood pressure and proteinuria over 2 years of follow-up. If the MEST score can achieve accurate risk stratification 2 years sooner than methods used in current clinical practice, it would allow earlier modification of patient treatment, which in turn may help preserve functioning nephron mass. To address our hypothesis, we pooled cohorts from the VALIGA, Oxford, and North American validation studies in IgAN to compare the prediction of a hard renal outcome using the combination of renal function, blood pressure, and proteinuria at biopsy with and without the MEST score versus using only renal function and longitudinal changes in blood pressure and proteinuria over 2 years.1Cattran D.C. Coppo R. Cook H.T. et al.The Oxford classification of IgA nephropathy: rationale, clinicopathological correlations, and classification.Kidney Int. 2009; 76: 534-545Abstract Full Text Full Text PDF PubMed Scopus (896) Google Scholar, 3Herzenberg A.M. Fogo A.B. Reich H.N. et al.Validation of the Oxford classification of IgA nephropathy.Kidney Int. 2011; 80: 310-317Abstract Full Text Full Text PDF PubMed Scopus (152) Google Scholar, 4Roberts I.S. Cook H.T. Troyanov S. et al.The Oxford classification of IgA nephropathy: pathology definitions, correlations, and reproducibility.Kidney Int. 2009; 76: 546-556Abstract Full Text Full Text PDF PubMed Scopus (757) Google Scholar Because the use of renin-angiotensin system blockade (RASB) prior to biopsy and immunosuppression use during follow-up have the potential to impact the relationship between pathology and renal outcome, we repeated our analyses in a priori defined subgroups on the basis of the use of these medications. There were 901 patients included in the analysis (Figure 1), and a description of the cohort is provided in Table 1. Overall, RASB was used in 38.4% at the time of biopsy and in 85.8% during follow-up starting a median of 0.6 months after biopsy (interquartile range 0, 11.5). Immunosuppression was used in 35.7% starting a median of 1.9 months after biopsy (interquartile range 0.1, 7.2). The primary renal outcome was a composite of end-stage renal disease (ESRD) or a 50% reduction in estimated glomerular filtration rate (eGFR) compared with baseline. This occurred in 18% (N = 162) of patients and was composed of 21.6% (N = 36) from the Oxford study, 16.1% (N = 14) from the North American validation study, and 17.3% (N = 112) from the VALIGA study. The 5- and 10-year risks of the composite renal outcome were 11.2% and 26.8%, respectively, as shown in the Kaplan–Meier curves in Figure 2.Table 1Description of the cohortTotal N = 901Oxford derivation N =167North American validation N = 87VALIGA N = 647Follow-up (years)5.6 (3.8, 8.8)6.8 (4.8, 9.2)4.9 (3.9, 7.3)5.4 (3.5, 8.8)Age (years)38.1 (29.2, 49.2)36.1 (28.8, 46.8)41.8 (31.3, 47.2)38.4 (28.8, 50.2)Male sex640 (71%)117 (70.1%)51 (58.6%)472 (73%)Race Caucasian780 (86.6%)107 (64.1%)45 (51.7%)628 (97.1%) Black12 (1.3%)4 (2.4%)2 (2.3%)6 (0.9%) Asian76 (8.4%)52 (31.1%)21 (24.1%)3 (0.5%) South Asian9 (1%)0 (0%)0 (0%)9 (1.4%) Other11 (1.2%)4 (2.4%)6 (6.9%)1 (0.2%)Creatinine at biopsy (μmol/l)106.1 (85.0, 140.8)105.6 (81.0, 130.0)98.0 (80.0, 132.0)106.1 (86.6, 147.0)eGFR at biopsy (ml/min per 1.73 m2)68.4 (48.5, 88.6)69.5 (55.3, 93.1)69.8 (56.0, 85.8)67.3 (45.9, 88.4)MAP at biopsy (mm Hg)100.0 (93.3, 106.7)99.3 (90.7, 106.7)98.7 (90.0, 106.0)100 (93.3, 106.7)MAP averaged over 2 years (mm Hg)97.2 (91.3, 103.6)97.0 (90.0, 103.3)93.9 (88.4, 98.4)97.9 (92.2, 104.4)Proteinuria at biopsy (g per day)1.5 (0.8, 2.6)1.7 (1.1, 2.9)1.6 (1.1, 2.8)1.4 (0.7, 2.6)Proteinuria averaged over 2 years (g per day)1.1 (0.6, 2.1)1.4 (0.8, 2.2)1.2 (0.7, 2.0)1.0 (0.5, 2.0)Use of RASB at biopsy346 (38.4%)37 (22.2%)27 (31%)282 (43.6%)Use of RASB during follow-up773 (85.8%)132 (79%)75 (86.2%)566 (87.5%)Use of any immunosuppression during follow-up322 (35.7%)25 (15%)32 (36.8%)265 (41%)Pathology M1383 (42.5%)131 (78.4%)78 (89.7%)174 (26.9%) E1163 (18.1%)62 (37.1%)27 (31%)74 (11.4%) S1676 (75%)132 (79%)57 (65.5%)487 (75.3%) T1161 (17.9%)32 (19.2%)15 (17.2%)114 (17.6%) T238 (4.2%)8 (4.8%)2 (2.3%)28 (4.3%) Crescents154 (17.1%)68 (40.7%)28 (32.2%)58 (9%)eGFR, estimated glomerular filtration rate; IQR, interquartile range; MAP, mean arterial blood pressure; RASB, renin-angiotensin system blockade.Data presented as median (IQR) or count (percentage). Open table in a new tab Figure 2The risk of the individual components that contribute to the primary composite renal outcome (first occurrence of either a 50% reduction in estimated glomerular filtration rate [eGFR] or end-stage renal disease [ESRD]). The 5-, 10-, and 15-year risk of survival without the composite outcome was 88.8%, 73.2%, and 55.4%, respectively.View Large Image Figure ViewerDownload (PPT) eGFR, estimated glomerular filtration rate; IQR, interquartile range; MAP, mean arterial blood pressure; RASB, renin-angiotensin system blockade. Data presented as median (IQR) or count (percentage). The risks of the composite renal outcome associated with the combination of MEST plus clinical data at biopsy (eGFR, mean arterial blood pressure [MAP], and proteinuria) or with 2-year clinical data alone (eGFR at biopsy and 2-year averages of MAP and proteinuria) are shown in Table 2. MEST as a group (P < 0.0001) and T1, T2, and M1 scores individually (P < 0.0001 and 0.018) were significantly associated with the renal outcome independent of clinical data at biopsy.Table 2The results of multivariable models for the risk of a 50% decline in eGFR or ESRD that included either the MEST score with clinical data at the time of biopsy, or clinical data over 2 yearsHR95% CIP-valueModel containing clinical data at biopsy eGFR at biopsy0.990.98–0.990.020 Proteinuria at biopsy1.591.29–1.96<0.0001 MAP at biopsy1.021.01–1.03<0.0001 MEST score<0.0001 M1.491.07–2.070.018 E1.150.78–1.710.483 S1.310.81–2.120.267 T12.922.01–4.26<0.0001 T24.212.28–7.78<0.0001Model containing 2-year clinical data eGFR at biopsy0.980.97–0.99<0.0001 Proteinuria over 2 years2.622.14–3.22 80%). These results demonstrate that compared with using 2-year clinical data alone, the combination of MEST with clinical data at biopsy predicts the composite renal outcome with similar model fit and discrimination, and no loss in calibration.Figure 4The calibration plots of predicted versus observed survival without a 50% reduction in estimated glomerular filtration rate (eGFR) or end-stage renal disease (ESRD) at 5 years, as determined using either MEST and clinical data at biopsy, or 2-year clinical data alone. The line depicting perfect calibration is shown in light gray. The hash marks along the top axis represent the number of patients at each level of predicted risk and show that the majority of the cohort had a predicted survival greater than 80%. The calibration plots for the two models are very similar in the range of predicted risk observed in the cohort.View Large Image Figure ViewerDownload (PPT) We performed sensitivity analyses in separate subgroups on the basis of RASB exposure at the time of biopsy, and on the basis of immunosuppression use during follow-up (see Supplementary Tables S1 and S2 online). In multivariable models that included clinical data at biopsy, the MEST score, and interaction terms between each MEST component and either RASB or immunosuppression exposure as appropriate, none of the interaction terms were significant (data not shown). This suggests that after adjusting for clinical data at biopsy, the association between each MEST component and the composite renal outcome did not depend on the prior use of RASB or subsequent use of immunosuppression. We repeated assessments of prediction performance in subgroups on the basis either of RASB or of immunosuppression exposure. In all subgroups, the primary findings regarding risk prediction in the overall cohort did not change (data not shown). Because M1 was associated with the composite renal outcome when added to baseline clinical data (see Table 2), we explored whether mesangial hypercellularity may explain part of the benefit of the MEST score in predicting outcome at the time of biopsy. When we stratified the cohort on the basis of mesangial score, those with M1 compared with M0 had similar MAP but higher proteinuria at biopsy (1.8 vs. 1.2 g per day, P < 0.001), at 2 years (0.9 vs. 0.52 g per day, P < 0.001), and over the entire first 5 years of follow-up (see Supplementary Table S3 online and Supplementary Figure S1 online). In the model in Table 2 with MEST and clinical data at biopsy, the hazard ratio (HR) for M1 was 1.49 (95% CI 1.07–2.07, P = 0.018). However, when proteinuria at biopsy was replaced by the average proteinuria over 2 years, the HR for M1 was substantially attenuated to 1.33 (95% CI 0.96–1.84, P = 0.09). In comparison, when MAP at biopsy was replaced MAP over 2 years, the HR for M1 was unchanged (HR = 1.47 95% CI 1.05–2.03, P = 0.02). These results suggest that M1 is associated with changes in proteinuria (but not MAP) over time, and the M1 score explains some of the same risk of the composite renal outcome as the average proteinuria (but not MAP) over 2 years. To illustrate the clinical relevance of our results, we generated subgroups whose initial eGFR was ≥50 ml/min per 1.73 m2 but with different proteinuria levels at biopsy. The addition of M and T scores identified patients at biopsy with similar risk as those with 2-year proteinuria values between 1 and 2 grams per day (who would qualify for steroid treatment in the KDIGO guidelines12Kidney Disease: Improving Global Outcomes (KDIGO) Glomerulonephritis Work GroupKDIGO Clinical Practice Guideline for Glomerulonephritis.Kidney Int Suppl. 2012; 2: 139-274Abstract Full Text Full Text PDF Scopus (789) Google Scholar) (see Figure 5). For example, compared with patients with 2-year proteinuria of 1–2 gram per day, the risk of renal outcome was similar in those with proteinuria at biopsy ≤ 1 gram per day but with M1 (P = 0.56). Conversely, the risk was much lower in patients with proteinuria at biopsy ≤ 1 gram per day but with M0 (P = 0.008), who had a similar risk as patients with proteinuria at biopsy of 1–1.5 gram per day but with M0 and T0 despite the higher degree of proteinuria (P = 0.67). We used a large well-characterized cohort of patients with IgAN to demonstrate that the addition of MEST to baseline eGFR, blood pressure, and proteinuria substantially improves prediction of the patient-level risk of a 50% decline in renal function or ESRD. More particularly, it has comparable accuracy with the currently established method using 2 years of blood pressure and proteinuria measurements to predict the subsequent risk of renal function decline.10Bartosik L.P. Lajoie G. Sugar L. et al.Predicting progression in IgA nephropathy.Am J Kidney Dis. 2001; 38: 728-735Abstract Full Text Full Text PDF PubMed Scopus (308) Google Scholar In addition, the quantitative benefit of using MEST to predict long-term outcomes was unchanged in subgroups based on the prior use of RASB or subsequent use of immunosuppression. This is the first study to demonstrate that the MEST score improves the clinical utility of risk stratification in IgAN by allowing accurate prediction at the time of biopsy. This potentially eliminates the need for 2 years of follow-up data before making patient-related treatment decisions. IgAN is an extremely heterogeneous disease with highly variable risk of kidney function decline, and currently, there is no established prediction tool widely used in clinical practice.6Reich H.N. Troyanov S. Scholey J.W. et al.Remission of proteinuria improves prognosis in IgA nephropathy.J Am Soc Nephrol. 2007; 18: 3177-3183Crossref PubMed Scopus (410) Google Scholar, 7Tanaka S. Ninomiya T. Katafuchi R. et al.Development and validation of a prediction rule using the Oxford classification in IgA nephropathy.Clin J Am Soc Nephrol. 2013; 8: 2082-2090Crossref PubMed Scopus (80) Google Scholar, 8Le W. Liang S. Hu Y. et al.Long-term renal survival and related risk factors in patients with IgA nephropathy: results from a cohort of 1155 cases in a Chinese adult population.Nephrol Dial Transplant. 2012; 27: 1479-1485Crossref PubMed Scopus (229) Google Scholar, 9Barbour S.J. Reich H.N. Risk stratification of patients with IgA nephropathy.Am J Kidney Dis. 2012; 59: 865-873Abstract Full Text Full Text PDF PubMed Scopus (128) Google Scholar Previously, it has been demonstrated that the Lee histology classification was not independently associated with outcome, that baseline clinical data were insufficient to predict the rate of renal function decline, and that at least 2 years of blood pressure and proteinuria measurements were required.10Bartosik L.P. Lajoie G. Sugar L. et al.Predicting progression in IgA nephropathy.Am J Kidney Dis. 2001; 38: 728-735Abstract Full Text Full Text PDF PubMed Scopus (308) Google Scholar, 11Mackinnon B. Fraser E.P. Cattran D.C. et al.Validation of the Toronto formula to predict progression in IgA nephropathy.Nephron Clin Pract. 2008; 109: c148-c153Crossref PubMed Scopus (27) Google Scholar The major limitation of this approach in clinical practice is the 2-year wait time. Earlier prediction of outcomes would be preferable, as it would allow the introduction of effective treatments 2 years sooner, which may improve the preservation of nephron mass and potentially delay the progression of IgAN. To overcome this limitation, newer prediction models have combined pathology features with clinical data at biopsy, but most have used histology classifications that have not been validated or that are not independently correlated with outcome, and none have compared their results with using blood pressure and proteinuria measured over time.7Tanaka S. Ninomiya T. Katafuchi R. et al.Development and validation of a prediction rule using the Oxford classification in IgA nephropathy.Clin J Am Soc Nephrol. 2013; 8: 2082-2090Crossref PubMed Scopus (80) Google Scholar, 13Goto M. Kawamura T. Wakai K. et al.Risk stratification for progression of IgA nephropathy using a decision tree induction algorithm.Nephrol Dial Transplant. 2009; 24: 1242-1247Crossref PubMed Scopus (42) Google Scholar, 14Goto M. Wakai K. Kawamura T. et al.A scoring system to predict renal outcome in IgA nephropathy: a nationwide 10-year prospective cohort study.Nephrol Dial Transplant. 2009; 24: 3068-3074Crossref PubMed Scopus (182) Google Scholar, 15Wakai K. Kawamura T. Endoh M. et al.A scoring system to predict renal outcome in IgA nephropathy: from a nationwide prospective study.Nephrol Dial Transplant. 2006; 21: 2800-2808Crossref PubMed Scopus (126) Google Scholar, 16Berthoux F. Mohey H. Laurent B. et al.Predicting the risk for dialysis or death in IgA nephropathy.J Am Soc Nephrol. 2011; 22: 752-761Crossref PubMed Scopus (249) Google Scholar, 17Walsh M. Sar A. Lee D. et al.Histopathologic features aid in predicting risk for progression of IgA nephropathy.Clin J Am Soc Nephrol. 2010; 5: 425-430Crossref PubMed Scopus (80) Google Scholar Since the publication of the original Oxford Classification, multiple studies have shown an association between some or all of the MEST components and renal outcome independent of blood pressure and proteinuria during follow-up.1Cattran D.C. Coppo R. Cook H.T. et al.The Oxford classification of IgA nephropathy: rationale, clinicopathological correlations, and classification.Kidney Int. 2009; 76: 534-545Abstract Full Text Full Text PDF PubMed Scopus (896) Google Scholar, 2Coppo R. Troyanov S. Bellur S. et al.Validation of the Oxford classification of IgA nephropathy in cohorts with different presentations and treatments.Kidney Int. 2014; 86: 828-836Abstract Full Text Full Text PDF PubMed Scopus (297) Google Scholar, 3Herzenberg A.M. Fogo A.B. Reich H.N. et al.Validation of the Oxford classification of IgA nephropathy.Kidney Int. 2011; 80: 310-317Abstract Full Text Full Text PDF PubMed Scopus (152) Google Scholar, 5Lv J. Shi S. Xu D. et al.Evaluation of the Oxford Classification of IgA nephropathy: a systematic review and meta-analysis.Am J Kidney Dis. 2013; 62: 891-899Abstract Full Text Full Text PDF PubMed Scopus (124) Google Scholar Our results expand upon these observations by using newer statistical techniques to evaluate the prediction of individual patient-level outcomes in a way that is not possible from simple multivariable analysis. We demonstrate that when the MEST score is combined with cross-sectional data available at renal b
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