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NDT Advance Access originally published online on November 17, 2008
Nephrology Dialysis Transplantation 2009 24(4):1242-1247; doi:10.1093/ndt/gfn610
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© The Author [2008].
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org


Risk stratification for progression of IgA nephropathy using a decision tree induction algorithm

Masashi Goto1, Takashi Kawamura1, Kenji Wakai2, Masahiko Ando1, Masayuki Endoh3 and Yasuhiko Tomino4

1 Kyoto University Health Service, Kyoto 2 Department of Preventive Medicine/Biostatistics and Medical Decision Making, Nagoya University Graduate School of Medicine, Nagoya 3 Division of Nephrology and Metabolism, Department of Internal Medicine, Tokai University School of Medicine, Isehara 4 Division of Nephrology, Department of Internal Medicine, Juntendo University School of Medicine, Tokyo, Japan

Correspondence and offprint requests to: Masashi Goto, Kyoto University Health Service, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan. Tel: +81-75-753-2400; Fax: +81-75-753-2424; E-mail: goto{at}msa.biglobe.ne.jp



  Abstract

Background. Immunoglobulin A nephropathy (IgAN) is the most common form of glomerulonephritis, and many patients are at risk of at least slow progression. However, prediction of the renal outcome in individual patients remains difficult.

Methods. To develop a practical and user-friendly scheme for risk stratification of IgAN patients, data were extracted from a prospective cohort study conducted in 97 clinical units in Japan from 1995. Specifically, we examined deterioration in renal function, defined as doubling of serum creatinine, within 10 years of follow-up in 790 adult IgAN patients without substantial renal dysfunction at baseline using a decision tree induction algorithm.

Results. Recursive partitioning indicated that the best single predictor of renal deterioration was severe proteinuria on urine dipstick testing, followed by hypoalbuminaemia and the presence of mild haematuria for patients with and without severe proteinuria, respectively. Serum total protein levels, diastolic blood pressure and histological grade were placed in the third tier of the decision tree model. With these six variables, patients can be readily stratified into seven risk groups whose incidence of renal deterioration within 10-year follow-up ranges from 1.0% to 51.4%. Logistic regression also identified severe proteinuria, hypoalbuminaemia and mild haematuria as significant predictors of deterioration. Areas under the receiver-operating characteristic curve for the prediction were comparable between the decision tree model and the logistic regression model [0.830 (95% confidence interval, 0.777–0.883) versus 0.808 (95% confidence interval, 0.754–0.861)].

Conclusion. Risk of substantial renal deterioration in IgAN patients can be validly estimated using six predictors obtained from clinical routine.

Keywords: cohort studies; disease progression; IgA nephropathy; prognosis; risk factors

Received for publication: 17. 2.08
Accepted in revised form: 7.10.08


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