Zhu Yu, Nan Jinhan, Gao Tian, Li Jia, Shi Nini, Wang Yunhang, Wang Xuedan, Ma Yuxia
Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China.
First Clinical School of Medicine, Lanzhou University, 730030, Lanzhou, China.
Neuropsychiatr. 2025 Jul 25. doi: 10.1007/s40211-025-00533-7.
Older adults with depression are at an increased risk of developing cognitive decline. This study aimed to develop and validate a risk prediction model for mild cognitive impairment (MCI) in older adults with depression in China.
This study used 2020 China Health and Retirement Longitudinal Study (CHARLS) data, splitting the cohort (70:30) into training and validation sets. Least absolute shrinkage and selection operator (LASSO) regression with ten-fold cross-validation identified key predictors, and binary logistic regression examined MCI risk factors in older adults with depression. A nomogram was developed, with receiver operating characteristic (ROC) curves assessing discrimination, calibration curves for accuracy, and decision curve analysis (DCA) for clinical benefit.
This study included 3512 older adults with depression, 640 (19.9%) of whom had MCI. Binary logistic regression identified age, education level, marital status, residence, pain, internet use, and social participation as significant predictors of MCI in older adults with depression, and these factors were used to construct a nomogram model with good consistency and predictive accuracy. The area under the curve (AUC) values of the predictive model in the training set and internal validation set were 0.78 (95% confidence interval [CI] 0.75-0.80) and 0.75 (95% CI 0.71-0.78); the Hosmer-Lemeshow test results were P = 0.916 and P = 0.749, respectively. ROC analysis of the prediction model showed strong discriminatory ability, calibration curves demonstrated significant agreement between the nomogram model and actual observations, and DCA confirmed a favorable net benefit.
The nomogram constructed in this study is a promising and convenient tool for evaluating the risk of MCI among older adults with depression, facilitating early identification of high-risk individuals and enabling timely intervention.
患有抑郁症的老年人出现认知衰退的风险增加。本研究旨在开发并验证一个针对中国患有抑郁症的老年人发生轻度认知障碍（MCI）的风险预测模型。
本研究使用了2020年中国健康与养老追踪调查（CHARLS）数据，将队列按70:30分为训练集和验证集。采用带有十折交叉验证的最小绝对收缩和选择算子（LASSO）回归来确定关键预测因素，并通过二元逻辑回归分析患有抑郁症的老年人的MCI风险因素。绘制了列线图，使用受试者工作特征（ROC）曲线评估区分度，校准曲线评估准确性，并通过决策曲线分析（DCA）评估临床获益。
本研究纳入了3512名患有抑郁症的老年人，其中640人（19.9%）患有MCI。二元逻辑回归确定年龄、教育水平、婚姻状况、居住地点、疼痛、互联网使用情况和社会参与度为患有抑郁症的老年人发生MCI的显著预测因素，并使用这些因素构建了一个具有良好一致性和预测准确性的列线图模型。预测模型在训练集和内部验证集的曲线下面积（AUC）值分别为0.78（95%置信区间[CI]0.75 - 0.80）和0.75（95%CI 0.71 - 0.78）；Hosmer-Lemeshow检验结果分别为P = 0.916和P = 0.749。预测模型的ROC分析显示出较强的区分能力，校准曲线表明列线图模型与实际观察结果之间具有显著一致性，DCA证实了良好的净获益。
本研究构建的列线图是评估患有抑郁症的老年人发生MCI风险的一个有前景且便捷的工具，有助于早期识别高危个体并及时进行干预。
