Xi Chunfeng, Wang Caimei, Rong Guihong, Deng Jinhuan
Department of Laboratory Medicine, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
Int J Endocrinol. 2021 Apr 8;2021:6672444. doi: 10.1155/2021/6672444. eCollection 2021.
To construct a novel nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in Chinese patients with type 2 diabetes mellitus (T2DM).
Questionnaire surveys, physical examinations, routine blood tests, and biochemical index evaluations were conducted on 1095 patients with T2DM from Guilin. A least absolute contraction selection operator (LASSO) regression and multivariable logistic regression analysis were used to screen out DN risk factors. A logistic regression analysis incorporating the screened risk factors was used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using the C-index, an area under the receiver operating characteristic curve (AUC), calibration plots, and a decision curve analysis. Bootstrapping was applied for internal validation.
Independent predictors for DN incidence risk included gender, age, hypertension, medicine use, duration of diabetes, body mass index, blood urea nitrogen level, serum creatinine level, neutrophil to lymphocyte ratio, and red blood cell distribution width. The nomogram model exhibited moderate prediction ability with a C-index of 0.819 (95% confidence interval (CI): 0.783-0.853) and an AUC of 0.813 (95%CI: 0.778-0.848). The C-index from internal validation reached 0.796 (95%CI: 0.763-0.829). The decision curve analysis displayed that the DN risk nomogram was clinically applicable when the risk threshold was between 1 and 83%.
Our novel and simple nomogram containing 10 factors may be useful in predicting DN incidence risk in T2DM patients.
构建一种新型列线图模型，用于预测中国2型糖尿病（T2DM）患者发生糖尿病肾病（DN）的风险。
对来自桂林的1095例T2DM患者进行问卷调查、体格检查、血常规检查和生化指标评估。采用最小绝对收缩选择算子（LASSO）回归和多变量逻辑回归分析筛选出DN危险因素。将筛选出的危险因素纳入逻辑回归分析，建立预测列线图模型。采用C指数、受试者工作特征曲线下面积（AUC）、校准曲线和决策曲线分析对列线图模型的性能进行评估。采用自抽样法进行内部验证。
DN发病风险的独立预测因素包括性别、年龄、高血压、用药情况、糖尿病病程、体重指数、血尿素氮水平、血清肌酐水平、中性粒细胞与淋巴细胞比值以及红细胞分布宽度。列线图模型具有中等预测能力，C指数为0.819（95%置信区间（CI）：0.783-0.853），AUC为0.813（95%CI：0.778-0.848）。内部验证的C指数达到0.796（95%CI：0.763-0.829）。决策曲线分析显示，当风险阈值在1%至83%之间时，DN风险列线图在临床上适用。
我们的新型简单列线图包含10个因素，可能有助于预测T2DM患者发生DN的风险。
