Chen Jiangwei, Fang Qing, Yang Kehua, Pan Jiayu, Zhou Lanlan, Xu Qunli, Shen Yuedi
School of Nursing, Hangzhou Normal University, Hangzhou 311121, China.
Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Healthcare (Basel). 2024 Oct 10;12(20):2015. doi: 10.3390/healthcare12202015.
: The aim was to develop and validate the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk), aiding community healthcare workers in the early identification of individuals at high risk of mild cognitive impairment (MCI). : Based on nationally representative community survey data, backward stepwise regression was employed to screen the variables, and logistic regression was utilized to construct the CGMCI-Risk. Internal validation was conducted using bootstrap resampling, while external validation was performed using temporal validation. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were employed to evaluate the CGMCI-Risk in terms of discrimination, calibration, and net benefit, respectively. : The CGMCI-Risk model included variables such as age, educational level, sex, exercise, garden work, TV watching or radio listening, Instrumental Activity of Daily Living (IADL), hearing, and masticatory function. The AUROC was 0.781 (95% CI = 0.766 to 0.796). The calibration curve showed strong agreement, and the DCA suggested substantial clinical utility. In external validation, the CGMCI-Risk model maintained a similar performance with an AUROC of 0.782 (95% CI = 0.763 to 0.801). : CGMCI-Risk is an effective tool for assessing cognitive function risk within the community. It uses readily predictor variables, allowing community healthcare workers to identify the risk of MCI in older adults over a three-year span.
目的是开发并验证社区老年人轻度认知障碍风险计算器（CGMCI-Risk），以帮助社区医护人员早期识别有轻度认知障碍（MCI）高风险的个体。基于具有全国代表性的社区调查数据，采用向后逐步回归筛选变量，并利用逻辑回归构建CGMCI-Risk。使用自助重抽样进行内部验证，同时使用时间验证进行外部验证。分别采用受试者工作特征曲线下面积（AUROC）、校准曲线和决策曲线分析（DCA）来评估CGMCI-Risk在区分度、校准度和净效益方面的表现。CGMCI-Risk模型纳入了年龄、教育水平、性别、运动、园艺工作、看电视或听广播、日常生活工具性活动（IADL）、听力和咀嚼功能等变量。AUROC为0.781（95%置信区间=0.766至0.796）。校准曲线显示出很强的一致性，DCA表明具有显著的临床实用性。在外部验证中，CGMCI-Risk模型保持了相似的表现，AUROC为0.782（95%置信区间=0.763至0.801）。CGMCI-Risk是评估社区内认知功能风险的有效工具。它使用易于获取的预测变量，使社区医护人员能够在三年时间跨度内识别老年人的MCI风险。
