建立中国老年人轻度认知障碍风险预测模型。
Establishment of a Risk Prediction Model for Mild Cognitive Impairment among Elderly Chinese.
机构信息
Xiaoping Wang, Department of Neurology, Shanghai General Hospital of Nanjing Medical University, Tongren Hospital Shanghai Jiao Tong University School of Medicine, No.1111 Xianxia Road, Changning District, Shanghai 200336, P.R China, Tel.: +86 021 52039999, Fax: +86 021 63243755, E-mail:
出版信息
J Nutr Health Aging. 2020;24(3):255-261. doi: 10.1007/s12603-020-1335-2.
BACKGROUND
Mild cognitive impairment (MCI) is a transitional stage of cognitive function between normal aging and dementia. Substantial variations in the prevalence of MCI in different countries have been studied including China. In this study, we established a prediction system to assess the risk of MCI among the elderly in China.
METHODS
The Rothman-Keller model was conducted on the basis of the risk factors of MCI obtained by the combined results of a meta-analysis. The accuracy of the model was verified using actual population data.
RESULTS
A total of 1826 subjects as a verification set were enrolled in this study in February 2019. There were statistically significant differences in the combined results of 10 risk factors including hypertension, diabetes, educational level, hyperlipidemia, smoking, physical exercise, living alone, stroke, drinking and heart disease (P<0.05). The area under the curve (AUC) of the actual data and the predictive results of this model was 0.859 (95%CI: 0.812-0.906, P<0.05), the sensitivity was 86.6% and the specificity was 76.5%.
CONCLUSIONS
This model performs an effective prediction that may be applied to the primary prevention for patients with MCI, helping to reduce the risk of MCI.
背景
轻度认知障碍（MCI）是认知功能从正常衰老到痴呆的过渡阶段。包括中国在内的许多国家都对 MCI 的患病率存在较大差异进行了研究。本研究旨在建立一个预测系统，以评估中国老年人患 MCI 的风险。
方法
在荟萃分析综合结果获得的 MCI 危险因素的基础上，采用 Rothman-Keller 模型。使用实际人群数据验证模型的准确性。
结果
2019 年 2 月，共纳入 1826 名作为验证集的受试者。10 个危险因素（高血压、糖尿病、教育水平、高血脂、吸烟、体育锻炼、独居、中风、饮酒和心脏病）的综合结果存在统计学差异（P<0.05）。该模型实际数据和预测结果的曲线下面积（AUC）为 0.859（95%CI：0.812-0.906，P<0.05），灵敏度为 86.6%，特异性为 76.5%。
结论
该模型可有效预测 MCI，有助于降低 MCI 的发病风险，可能应用于 MCI 患者的一级预防。
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