Postgraduate student, Department of Nursing, Jinzhou Medical University, Jinzhou 121001, Liaoning, China.
Postgraduate student, Department of Nursing, Jinzhou Medical University, Jinzhou 121001, Liaoning, China.
Geriatr Nurs. 2024 Jul-Aug;58:119-126. doi: 10.1016/j.gerinurse.2024.05.018. Epub 2024 May 25.
The prevalence of mild cognitive impairment (MCI) is steadily increasing among elderly people with type 2 diabetes (T2DM). This study aimed to create and validate a predictive model based on a nomogram.
This cross-sectional study collected sociodemographic characteristics, T2DM-related factors, depression, and levels of social support from 530 older adults with T2DM. We used LASSO regression and multifactorial logistic regression to determine the predictors of the model. The performance of the nomogram was evaluated using calibration curves, receiver operating characteristics (ROC), and decision curve analysis (DCA).
The nomogram comprised age, smoking, physical activity, social support, depression, living alone, and glycosylated hemoglobin. The AUC for the training and validation sets were 0.914 and 0.859. The DCA showed good clinical applicability.
This predictive nomogram has satisfactory accuracy and discrimination. Therefore, the nomogram can be intuitively and easily used to detect MCI in elderly adults with T2DM.
2 型糖尿病（T2DM）老年患者中轻度认知障碍（MCI）的患病率稳步上升。本研究旨在建立和验证一种基于列线图的预测模型。
这项横断面研究收集了 530 名 T2DM 老年患者的社会人口统计学特征、T2DM 相关因素、抑郁和社会支持水平。我们使用 LASSO 回归和多因素逻辑回归来确定模型的预测因素。通过校准曲线、接收者操作特征（ROC）和决策曲线分析（DCA）来评估列线图的性能。
该列线图包含年龄、吸烟、身体活动、社会支持、抑郁、独居和糖化血红蛋白。训练集和验证集的 AUC 分别为 0.914 和 0.859。DCA 显示出良好的临床适用性。
该预测列线图具有较好的准确性和区分度。因此，该列线图可以直观、方便地用于检测 T2DM 老年患者的 MCI。
