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  • Title: External Validation of the International IgA Nephropathy Prediction Tool.
    Author: Zhang J, Huang B, Liu Z, Wang X, Xie M, Guo R, Wang Y, Yu D, Wang P, Zhu Y, Ren J.
    Journal: Clin J Am Soc Nephrol; 2020 Aug 07; 15(8):1112-1120. PubMed ID: 32616496.
    Abstract:
    BACKGROUND AND OBJECTIVES: The 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, & MEASUREMENTS: We 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). RESULTS: The 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. CONCLUSIONS: In 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.
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