实用老年医学

实用老年医学 ›› 2024, Vol. 38 ›› Issue (11): 1136-1141.doi: 10.3969/j.issn.1003-9198.2024.11.012

• 临床研究 • 上一篇    下一篇

基于决策树构建合并认知障碍的老年2型糖尿病病人低血糖风险预测模型

易梦廷, 周怡, 宗前兴, 王雪菲, 苑影影, 陈静, 巫海娣, 莫永珍   

  1. 215006 江苏省苏州市, 苏州大学医学部护理学院(易梦廷, 周怡, 苑影影, 陈静);
    211166 江苏省南京市, 南京医科大学护理学院(宗前兴);
    210024 江苏省南京市, 南京医科大学附属老年医院糖尿病中心(王雪菲, 巫海娣);老年综合评估研究室(莫永珍)
  • 收稿日期:2023-12-14 出版日期:2024-11-20 发布日期:2024-11-21
  • 通讯作者: 莫永珍,Email:moyongzh@sina.com
  • 基金资助:
    江苏省干部保健科研项目(BJ21027)

Establishment of a hypoglycemia risk prediction model based on decision tree for elderly patients with type 2 diabetes combined with cognitive impairment

YI Mengting, ZHOU Yi, ZONG Qianxing, WANG Xuefei, YUAN Yingying, CHEN Jing, WU Haidi, MO Yongzhen   

  1. School of Nursing, Soochow University, Suzhou 215006, China(YI Mengting, ZHOU Yi, YUAN Yingying, CHEN Jing);
    School of Nursing, Nanjing Medical University, Nanjing 211166, China (ZONG Qianxing);
    Diabetes Center(WANG Xuefei, WU Haidi ); Department of Comprehensive Geriatric Assessment(MO Yongzhen), the Affiliated Geriatric Hospital of Nanjing Medical University, Nanjing 210024, China
  • Received:2023-12-14 Online:2024-11-20 Published:2024-11-21
  • Contact: MO Yongzhen, Email:moyongzh@sina.com

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摘要： 目的 分析合并认知障碍的老年2型糖尿病病人发生低血糖的相关危险因素,并基于决策树构建低血糖的预测模型。 方法 采用便利抽样法选取2022年2月至2023年6月南京医科大学附属老年医院261例合并认知障碍的老年2型糖尿病病人作为研究对象。基于CART算法构建决策树模型,采用ROC曲线下面积(AUC)对模型进行评价。 结果 决策树中有低血糖史、HbA1c≥8.3%的组合未来6个月低血糖发生风险最高,达80%,其次是有低血糖史、HbA1c＜8.3%、使用胰岛素、病程≥20年的组合,其低血糖发生风险为74%。训练集的AUC为0.805,灵敏度为0.779,特异度为0.725,准确度为0.840。验证集的AUC为0.722,灵敏度为0.668,特异度为0.766,准确度为0.739。 结论 本研究构建的决策树预测模型可以较好地预测合并认知障碍的老年2型糖尿病病人未来6个月内发生低血糖的风险,可为临床医护人员对病人进行低血糖风险评估提供较为科学、有效的评估工具。

关键词: 老年人, 2型糖尿病, 认知障碍, 低血糖, 决策树, 预测模型

Abstract: Objective To analyze the risk factors for hypoglycemia in the elderly patients with type 2 diabetes mellitus combined with cognitive impairment, and to establish a prediction model for hypoglycemia based on decision tree. Methods A total of 261 elderly patients with type 2 diabetes mellitus combined with cognitive impairment in the Affiliated Geriatric Hospital of Nanjing Medical University from February 2022 to June 2023 were enrolled in this study by convenient sampling method. Decision tree model was established based on CART algorithm. Receiver operating characteristic (ROC) curve was drawn to evaluate the model. Results In the decision tree, the group with a history of hypoglycemia and HbA1c≥8.3% had the highest risk of 80% of hypoglycemia in the next 6 months, followed by the group with a history of hypoglycemia, HbA1c < 8.3%, insulin use and disease duration ≥20 years, with a risk of 74%. The area under ROC curve (AUC) of the training set was 0.805, with the sensitivity of 0.779, the specificity of 0.725, and the accuracy of 0.840. The AUC of the validation set was 0.722, with the sensitivity of 0.668, the specificity of 0.766, and the accuracy of 0.739. Conclusions The decision tree prediction model established in this study can better predict the risk of hypoglycemia in the elderly type 2 diabetic patients with cognitive impairment in the next 6 months, and can provide a scientific and effective tool to assess the risk of hypoglycemia in clinic.

Key words: aged, type 2 diabetes mellitus, cognitive impairment, hypoglycemia, decision tree, prediction model

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