Gu Yanghui, Wang Yu, Ji Chunlan, Fan Ping, He Zhiren, Wang Tao, Liu Xusheng, Zou Chuan
Renal Division, Second Clinical Medical College, Guangzhou University of Traditional Chinese Medicine, Guangzhou 510006, China.
Renal Division, Shanxi Provincial Hospital of Chinese Medicine, Xi'an 710200, China.
Evid Based Complement Alternat Med. 2017;2017:2697560. doi: 10.1155/2017/2697560. Epub 2017 Mar 26.
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. To explore the feasibility of differentiation of TCM syndrome types among IgA nephropathy patients based on clinicopathological parameters. 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. 370 patients of training dataset were 32 years old with serum creatinine of 79 mol/L, estimated glomerular filtration rate (eGFR) of 97.2 mL/min/1.73 m, 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. 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.
IgA肾病是中国原发性肾小球肾炎最常见的病因，而中医是一种重要的治疗策略。然而，并非所有开中药的医生都有足够的知识来准确辨证。为了探讨基于临床病理参数对IgA肾病患者进行中医证型辨别的可行性。这项横断面研究纳入了2010年至2016年464例经活检证实的成年IgA肾病患者。收集了人口统计学数据、临床病理特征和中医证型，并建立了基于分类与回归树的决策树模型来区分证型。训练数据集的370例患者年龄为32岁，血清肌酐为79μmol/L，估计肾小球滤过率（eGFR）为97.2mL/min/1.73m²，蛋白尿为1.0g/天。牛津分类的评分如下：M1 = 97.6%，E1 = 14.6%，S1 = 50.0%，T1 = 52.2%/T2 = 18.4%。在训练数据中，不使用或使用MEST评分的决策树具有相同的精度。然而，带有MEST评分的树在验证数据集中表现更好，尤其是在脾肾气虚证型的分类方面。提出了一种在常规临床实践中通过常见参数推断IgA肾病患者中医证候的可行方法。MEST评分有助于结合临床数据区分中医证型。
