中国全科医学 ›› 2024, Vol. 27 ›› Issue (09): 1054-1061.DOI: 10.12114/j.issn.1007-9572.2023.0571

所属专题: 内分泌代谢性疾病最新文章合集 泌尿系统疾病最新文章合集

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2型糖尿病患者并发糖尿病肾病风险的列线图预测模型与验证研究

韩俊杰1, 武迪2, 陈志胜3, 肖扬1, 森干1,*()   

  1. 1.830017 新疆维吾尔自治区乌鲁木齐市,新疆医科大学医学工程技术学院
    2.830017 新疆维吾尔自治区乌鲁木齐市,新疆医科大学公共卫生学院
    3.835099 新疆维吾尔自治区伊犁哈萨克自治州伊宁市,伊犁州疾病预防控制中心
  • 收稿日期:2023-08-21 修回日期:2023-11-06 出版日期:2024-03-20 发布日期:2023-12-19
  • 通讯作者: 森干

  • 作者贡献:韩俊杰提出研究思路和设计方案,负责论文起草;武迪负责数据整理、清洗、统计学分析及临床预测模型的构建;陈志胜负责数据采集;肖扬提出对变量筛选的方法,负责数据预处理及论文修改;森干负责研究构思与设计,负责调查对象的选取,对论文负责;所有作者阅读并修订了最终稿件。
  • 基金资助:
    新疆维吾尔自治区自然科学基金资助项目(2022D01A311,2022D01C184)

A Nomogram Prediction Model and Validation Study on the Risk of Complicated Diabetic Nephropathy in Type 2 Diabetes Patients

HAN Junjie1, WU Di2, CHEN Zhisheng3, XIAO Yang1, SEN Gan1,*()   

  1. 1. School of Medical Engineering Technology, Xinjiang Medical University, Urumqi 830017, China
    2. School of Public Health, Xinjiang Medical University, Urumqi 830017, China
    3. Ili Prefecture Center for Disease Prevention and Control, Yining 835099, China
  • Received:2023-08-21 Revised:2023-11-06 Published:2024-03-20 Online:2023-12-19
  • Contact: SEN Gan

摘要: 背景 糖尿病肾病(DN)是糖尿病患者常见的并发症,对其发生风险进行预测与验证,有助于提前识别高风险患者并采取干预措施,以避免或延缓肾脏疾病的进展。 目的 分析影响2型糖尿病(T2DM)患者并发DN的风险因素,构建T2DM患者发生DN风险的预测模型并进行验证。 方法 选取2016年1月—2021年6月在新疆医科大学第一附属医院住院的5 810例T2DM患者为研究对象,根据是否并发DN将患者分为DN组(481例)和非DN组(5 329例)。对其中481例DN患者和非DN患者依据性别、年龄(±2岁)进行1∶1病例对照匹配,将匹配后的962例T2DM患者根据2∶1比例随机分为训练组(n=641)和验证组(n=321)。收集患者的基础数据,如临床特征、实验室检查结果及其他相关数据。采用LASSO回归优化筛选变量,利用多因素Logistic回归分析建立列线图预测模型。分别采用受试者工作特征(ROC)曲线、Hosmer-Lemeshow校准曲线和决策曲线分析(DCA)评价预测模型的区分度、校准度以及预测模型的临床有效性。 结果 DN组与非DN组患者性别、年龄、BMI、糖尿病病程、白细胞计数、总胆固醇、三酰甘油、低密度脂蛋白胆固醇、血清肌酐、高血压、收缩压、舒张压、糖化血红蛋白载脂蛋白B、24 h尿微量总蛋白、定性尿蛋白比较,差异有统计学意义(P<0.05)。采用LASSO回归分析方法,筛选出5个与T2DM患者发生DN风险相关的预测变量,结合多因素Logistic回归分析结果显示,糖尿病病程、总胆固醇、血清肌酐、高血压、定性尿蛋白是T2DM患者并发DN的危险因素(P<0.05)。训练组DN发生风险的ROC曲线下的面积(AUC)为0.866(95%CI=0.839~0.894),验证组DN发生风险的AUC为0.849(95%CI=0.804~0.889)。Hosmer-Lemeshow校准曲线拟合度较好(训练组P=0.748;验证组P=0.986)。DCA显示当患者的阈值概率为0.15~0.95时,使用列线图预测模型预测T2DM患者发生DN风险更有益。 结论 本研究发现糖尿病病程、总胆固醇、血清肌酐、高血压、定性尿蛋白可能是T2DM患者并发DN的危险因素,建立了包含该5个危险因素的列线图预测模型,可用于预测T2DM患者发生DN的风险。

关键词: 糖尿病,2型, 糖尿病肾病, 危险因素, 列线图, 预测模型, 决策曲线分析

Abstract:

Background

Diabetes nephropathy (DN) is a common complication of diabetes patients. The prediction and validation of its risk will help identify high-risk patients in advance and take intervention measures to avoid or delay the progress of nephropathy.

Objective

To analyze the risk factors affecting the complication of DN in patients with type 2 diabetes mellitus (T2DM) , construct a risk prediction model for the risk of DN in T2DM patients and validate it.

Methods

A total of 5 810 patients with T2DM admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2016 to June 2021 were selected as the study subjects and divided into the DN group (n=481) and non-DN group (n=5 329) according to the complication of DN. A 1∶1 case-control matching was performed on 481 of these DN patients and non-DN patients by gender and age (±2 years) , and the matched 962 T2DM patients were randomly divided into the training group (n=641) and validation group (n=321) based on a 2∶1 ratio. Basic data of patients, such as clinical characteristics, laboratory test results and other related data, were collected. LASSO regression was applied to optimize the screening variables, and a nomogram prediction model was developed using multivariate Logistic regression analysis. The discriminability, calibration and clinical validity of the prediction model were evaluated by using the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow calibration curve, and decision curve analysis (DCA) , respectively.

Results

There were significant differences in gender, age, BMI, course of diabetes, white blood cell count, total cholesterol, triacylglycerol, low-density lipoprotein cholesterol, serum creatinine, hypertension, systolic blood pressure, diastolic blood pressure, glycosylated hemoglobin, apolipoprotein B, 24-hour urinary micro total protein, qualitative urinary protein between the DN and non-DN group (P<0.05) . Five predictor variables associated with the risk of DN in patients with T2DM were screened using LASSO regression analysis, and the results combined with multivariate Logistic regression analysis showed that duration of diabetes, total cholesterol, serum creatinine, hypertension, and qualitative urinary protein were risk factors for the complication of DN in T2DM patients (P<0.05) . The area under the ROC curve (AUC) for the risk of DN in the training group of the model was 0.866 (95%CI=0.839-0.894) , and the AUC for predicting the risk of DN in the validation group was 0.849 (95%CI=0.804-0.889) based on the predictor variables. The Hosmer-Lemeshow calibration curve fit was good (P=0.748 for the training group; P=0.986 for the validation group) . DCA showed that the use of nomogram prediction model was more beneficial in predicting DN when the threshold probability of patients was 0.15 to 0.95.

Conclusion

The nomogram prediction model containing five predictor variables (diabetes duration, total cholesterol, serum creatinine, hypertension, qualitative urinary protein) developed in this study can be used to predict the risk of DN in patients with T2DM.

Key words: Diabetes mellitus, type 2, Diabetic nephropathies, Risk factors, Nomogram, Prediction model, Decision curve analysis