Liu Yong, Wei Kai, Cao Xinyi, Jiang Lijuan, Gu Nannan, Feng Lei, Li Chunbo
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Clinical Neurocognitive Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Aging Neurosci. 2021 Apr 16;13:618833. doi: 10.3389/fnagi.2021.618833. eCollection 2021.
To develop and validate a prediction nomogram based on motoric cognitive risk syndrome for cognitive impairment in healthy older adults.
Using two longitudinal cohorts of participants (aged ≥ 60 years) with 4-year follow-up, we developed ( = 1,177) and validated ( = 2,076) a prediction nomogram. LASSO (least absolute shrinkage and selection operator) regression model and multivariable Cox regression analysis were used for variable selection and for developing the prediction model, respectively. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness.
The individualized prediction nomogram was assessed based on the following: motoric cognitive risk syndrome, education, gender, baseline cognition, and age. The model showed good discrimination [Harrell's concordance index (C-index) of 0.814; 95% confidence interval, 0.782-0.835] and good calibration. Comparable results were also seen in the validation cohort, which includes good discrimination (C-index, 0.772; 95% confidence interval, 0.776-0.818) and good calibration. Decision curve analysis demonstrated that the prediction nomogram was clinically useful.
This prediction nomogram provides a practical tool with all necessary predictors, which are accessible to practitioners. It can be used to estimate the risk of cognitive impairment in healthy older adults.
开发并验证一种基于运动认知风险综合征的预测列线图,用于预测健康老年人的认知障碍。
利用两个纵向队列的参与者(年龄≥60岁),进行4年随访,我们开发了一个预测列线图(n = 1177)并进行了验证(n = 2076)。分别使用LASSO(最小绝对收缩和选择算子)回归模型和多变量Cox回归分析进行变量选择和构建预测模型。通过校准、区分度和临床实用性来评估列线图的性能。
基于以下因素评估个体化预测列线图:运动认知风险综合征、教育程度、性别、基线认知和年龄。该模型显示出良好的区分度[Harrell一致性指数(C指数)为0.814;95%置信区间为0.782 - 0.835]和良好的校准。在验证队列中也观察到了类似的结果,包括良好的区分度(C指数,0.772;95%置信区间,0.776 - 0.818)和良好的校准。决策曲线分析表明该预测列线图具有临床实用性。
这种预测列线图提供了一个实用工具,包含所有必要的预测因素,可供从业者使用。它可用于估计健康老年人认知障碍的风险。