Yu Qian, Yu Hongyu
Department of Nursing, Jinzhou Medical University, Jinzhou, Liaoning, China.
J Clin Nurs. 2025 Aug;34(8):3261-3275. doi: 10.1111/jocn.17508. Epub 2025 Jan 14.
This study aimed to develop and validate a risk prediction model for cognitive frailty in elderly patients with Type 2 diabetes mellitus (T2DM).
A cross-sectional design.
From February to November 2023, a convenience sample of 430 older adults with T2DM was enrolled at a tertiary hospital in Jinzhou. The study analysed 22 indicators, including sociodemographic characteristics, behavioural factors, information related to T2DM, nutritional status, instrumental activities of daily living (IADL) and depression. Independent risk factors related to cognitive frailty were identified using LASSO and multivariate logistic regression analysis. A prediction model was created using a nomogram. The calibration curve, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve were used to evaluate model performance. This study was reported using the STARD checklist (Data S1).
The study found that cognitive frailty was prevalent in 30.7% of elderly patients with T2DM. Age, physical activity, glycosylated haemoglobin (HBA1c), duration of diabetes, nutritional status, IADL and depression were predictors of cognitive frailty. The ROC curve shows that the nomogram has good discriminative power. The calibration plots demonstrated a good fit between the observed and ideal curves. Additionally, DCA highlighted the clinical application of the nomogram.
This study provided an effective and convenient approach to evaluating the risk of cognitive frailty among elderly T2DM patients, which can help in the clinical screening of high-risk individuals.
Nurses should emphasise the care of comorbid cognitive frailty in elderly patients with T2DM. The intuitive and noninvasive nomogram can help clinical nurses assess the risk probability of cognitive frailty in this population. Tailored prevention strategies for high-risk populations can be rapidly developed with this tool, significantly improving patients' quality of life.
Some patients were involved in data interpretation. No public contribution.
本研究旨在开发并验证2型糖尿病（T2DM）老年患者认知衰弱的风险预测模型。
横断面设计。
2023年2月至11月，在锦州一家三级医院纳入了430例老年T2DM患者的便利样本。该研究分析了22项指标，包括社会人口学特征、行为因素、与T2DM相关的信息、营养状况、日常生活工具性活动（IADL）和抑郁情况。使用LASSO和多因素逻辑回归分析确定与认知衰弱相关的独立危险因素。使用列线图创建预测模型。采用校准曲线、决策曲线分析（DCA）和受试者工作特征（ROC）曲线评估模型性能。本研究按照STARD清单进行报告（数据S1）。
研究发现，30.7%的老年T2DM患者存在认知衰弱。年龄、身体活动、糖化血红蛋白（HBA1c）、糖尿病病程、营养状况、IADL和抑郁是认知衰弱的预测因素。ROC曲线显示列线图具有良好的区分能力。校准图表明观察曲线与理想曲线拟合良好。此外，DCA突出了列线图的临床应用价值。
本研究提供了一种有效且便捷的方法来评估老年T2DM患者认知衰弱的风险，有助于临床筛查高危个体。
护士应重视老年T2DM患者合并认知衰弱的护理。直观且无创的列线图可帮助临床护士评估该人群认知衰弱的风险概率。利用该工具可快速制定针对高危人群的个性化预防策略，显著提高患者生活质量。
部分患者参与了数据解读。无公众贡献。
