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Evidence-Based Complementary and Alternative Medicine
Volume 2017 (2017), Article ID 2697560, 11 pages
Research Article

Syndrome Differentiation of IgA Nephropathy Based on Clinicopathological Parameters: A Decision Tree Model

1Renal Division, Second Clinical Medical College, Guangzhou University of Traditional Chinese Medicine, Guangzhou 510006, China
2Renal Division, Shanxi Provincial Hospital of Chinese Medicine, Xi’an 710200, China
3Renal Division, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510120, China
4Renal Division, Peking University Third Hospital, Beijing 100191, China

Correspondence should be addressed to Chuan Zou

Received 30 November 2016; Revised 28 February 2017; Accepted 14 March 2017; Published 26 March 2017

Academic Editor: Fabio Firenzuoli

Copyright © 2017 Yanghui Gu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. IgA nephropathy is the most common cause of primary glomerulonephritis in China, and Traditional Chinese Medicine (TCM) is a vital treatment strategy. However, not all doctors prescribing TCM medicine have adequate knowledge to classify the syndrome accurately. Aim. To explore the feasibility of differentiation of TCM syndrome types among IgA nephropathy patients based on clinicopathological parameters. Materials and Methods. The cross-sectional study enrolled 464 biopsy-proven IgA nephropathy adult patients from 2010 to 2016. The demographic data, clinicopathological features, and TCM syndrome types were collected, and the decision tree models based on classification and regression tree were built to differentiate between the syndrome types. Results. 370 patients of training dataset were 32 years old with serum creatinine of 79 μmol/L, estimated glomerular filtration rate (eGFR) of 97.2 mL/min/1.73 m2, and proteinuria of 1.0 g/day. The scores of Oxford classifications were as follows: M1 = 97.6%, E1 = 14.6%, S1 = 50.0%, and T1 = 52.2%/T2 = 18.4%. The decision trees without or with MEST scores achieved equal precision in training data. However, the tree with MEST scores performed better in validation dataset, especially in classifying the syndrome of qi deficiency of spleen and kidney. Conclusion. A feasible method to deduce TCM syndromes of IgA nephropathy patients by common parameters in routine clinical practice was proposed. The MEST scores helped in the differentiation of TCM syndromes with clinical data.