Dataset Information

Evaluating a New International Risk-Prediction Tool in IgA Nephropathy.

ABSTRACT:

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.

SUBMITTER: Barbour SJ

PROVIDER: S-EPMC6583088 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and objectivesThe International IgA Nephropathy Network recently developed and externally validated two models to predict the risk of progression of IgA nephropathy: full models without and with race. This study sought to externally validate the International IgA Nephropathy Prediction Tool in a large, independent, and contemporary cohort in China.Design, setting, participants, & measurementsWe included 1373 patients with biopsy-confirmed primary IgA nephropathy from The First Affiliated Hospital of Zhengzhou University from January 2012 to May 2018 and calculated predicted risks for each patient. The outcomes of interest were a 50% decline in eGFR or kidney failure. We assessed the performance of both models using discrimination (concordance statistics and Kaplan-Meier curves between subgroups), calibration (calibration plots), reclassification (net reclassification improvement and integrated discrimination improvement), and clinical utility (decision curve analysis).ResultsThe median follow-up was 29 months (interquartile range, 21-43 months; range, 1-95 months), and 186 (14%) patients reached the kidney outcomes of interest. Both models showed excellent discrimination (concordance statistics >0.85 and well separated survival curves). Overall, the full model without race generally underestimated the risk of primary outcome, whereas the full model with race was well calibrated for predicting 5-year risk. Compared with the full model without race, the full model with race had significant improvement in reclassification, as assessed by the net reclassification improvement (0.49; 95% confidence interval, 0.41 to 0.59) and integrated discrimination improvement (0.06; 95% confidence interval, 0.04 to 0.08). Decision curve analysis showed that both full models had a higher net benefit than default strategies, and the model with race performed better.ConclusionsIn this study, both full models demonstrated remarkable discrimination, acceptable calibration, and satisfactory clinical utility. The relatively short follow-up time may have limited the validation of these models.