Li Ming-Lin, Zhang Fei, Huang Le-Tian, Wang Jia-He
Department of Family Medicine, The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, Guangdong Province, China.
Department of Family Medicine, Shengjing Hospital of China Medical University, 39 Huaxiang Street, Tiexi District, Shenyang City, Liaoning Province, China.
Neurol Sci. 2025 Jun 14. doi: 10.1007/s10072-025-08293-6.
The prevalence of cognitive impairment (CI) is progressively rising. Insufficient awareness, complex diagnostic procedures, and the absence of effective treatment methods contribute to the challenge. Therefore, our objective is to develop and validate a CI screening model specifically for the elderly population in China.
The multi-center cross-sectional study included 18 communities in four provinces, and data collection was performed on basic parameters, medical history, physical measurements, cognitive assessments, and blood sample tests. The least absolute shrinkage and selection operator (Lasso) and multiple-factor logistic regression analysis were used to select variables with best predictive performance to build the model that minimizes the mean squared error and build a nomogram of the CI screening model. The Receiver Operating Characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) are utilized to assess the discriminative ability and clinical utility of the model. Both internal and external datasets are utilized for model validation.
The development set of the model included a total of 1,479 participants, among whom 278 were identified as having cognitive impairment (CI), with a prevalence of 18.8%. Eight predictive factors for CI were selected as the final model, including age, falls times in 1-year, educational level, place of residence, hypertension, stroke, protein intake, and subcutaneous fat loss. The area under the curve (AUC) of the model was 0.873, indicating good discriminative ability. The calibration curve demonstrated good consistency between predicted and observed results. DCA and CIC analysis showed that the model had favorable clinical utility. The AUC, calibration curve, DCA, and CIC of both internal and external validation sets showed consistent results with the development set, indicating good consistency.
We developed an accurate and valid nomogram of CI screening with all the required factors easily available and four of them modifiable.
认知障碍（CI）的患病率正在逐步上升。认识不足、诊断程序复杂以及缺乏有效的治疗方法加剧了这一挑战。因此，我们的目标是开发并验证一种专门针对中国老年人群的CI筛查模型。
这项多中心横断面研究纳入了四个省份的18个社区，收集了基本参数、病史、身体测量、认知评估和血液样本检测的数据。使用最小绝对收缩和选择算子（Lasso）以及多因素逻辑回归分析来选择具有最佳预测性能的变量，以构建使均方误差最小的模型，并构建CI筛查模型的列线图。利用受试者工作特征（ROC）曲线、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）来评估模型的判别能力和临床实用性。内部和外部数据集均用于模型验证。
该模型的开发集共有1479名参与者，其中278人被确定为患有认知障碍（CI），患病率为18.8%。八个CI预测因素被选入最终模型，包括年龄、1年内跌倒次数、教育水平、居住地点、高血压、中风、蛋白质摄入量和皮下脂肪减少。该模型的曲线下面积（AUC）为0.873，表明具有良好的判别能力。校准曲线显示预测结果与观察结果之间具有良好的一致性。DCA和CIC分析表明该模型具有良好的临床实用性。内部和外部验证集的AUC、校准曲线、DCA和CIC与开发集结果一致，表明具有良好的一致性。
我们开发了一种准确有效的CI筛查列线图，所有所需因素易于获取，其中四个因素可修改。
