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Evaluating a New International Risk-Prediction Tool in IgA Nephropathy
Univ British Columbia, Div Nephrol, 2775 Laurel St,Fifth Floor, Vancouver, BC V5Z 1M9, Canada;BC Renal, Vancouver, BC, Canada.
Regina Margherita Childrens Univ Hosp, Turin, Italy.
Peking Univ, Inst Nephrol, Beijing, Peoples R China.
Nanjing Univ, Sch Med, Nanjing, Jiangsu, Peoples R China.
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2019 (English)In: JAMA Internal Medicine, ISSN 2168-6106, E-ISSN 2168-6114, Vol. 179, no 7, p. 942-952Article in journal (Refereed) Published
Abstract [en]

Importance  Although 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.

Objective  To 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 Participants  We 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 Measures  Cox 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 R2D measure, Akaike information criterion (AIC), C statistic, continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI), and calibration plots.

Results  The 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 R2D (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 (ΔC, 0.04; 95% CI, 0.03-0.04 and ΔC, 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 R2D (both 35.3%) were similar or better than in the validation cohort, with excellent calibration.

Conclusions and Relevance  In 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.

Place, publisher, year, edition, pages
2019. Vol. 179, no 7, p. 942-952
National Category
Urology and Nephrology
Identifiers
URN: urn:nbn:se:uu:diva-393135DOI: 10.1001/jamainternmed.2019.0600ISI: 000477893300017PubMedID: 30980653OAI: oai:DiVA.org:uu-393135DiVA, id: diva2:1353773
Conference
ISN World Congress of Nephrology, APR 13, 2019, Melbourne, AUSTRALIA
Available from: 2019-09-24 Created: 2019-09-24 Last updated: 2019-09-24Bibliographically approved

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Fellström, BengtSmerud, Hilde Kloster

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