Dynamic Prediction of Alzheimer's Disease Risk based on Random Survival Forests with Multivariate Longitudinal Endogenous Covariates
10.11783/j.issn.1002-3674.2025.01.005
- VernacularTitle:基于具有多元纵向内生协变量的随机生存森林动态预测阿尔茨海默病的发病风险
- Author:
Jiahao CHEN
1
;
Chunxia LI
1
;
Bingbing FAN
1
Author Information
1. 山东大学齐鲁医院公共卫生学院生物统计学系(250012);山东大学健康医疗大数据研究院
- Publication Type:Journal Article
- Keywords:
Alzheimer's disease;
Neuropsychological scale;
Dynamic prediction;
Random survival forest;
Multivariate longitudinal data
- From:
Chinese Journal of Health Statistics
2025;42(1):26-32
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a dynamic prediction model for the risk of developing Alzheimer's disease(AD)based on baseline clinical data and longitudinal neuropsychological scores in patients with mild cognitive impairment(MCI).Methods A total of 380 MCI patients from the Alzheimer's Disease Neuroimaging Initiative 1 study from 2005 to 2011 were selected and were randomly divided into a training set and a test set in a 7∶3 randomization.The Alzheimer's disease assessment scale-cognitive 13 items(ADAS-Cog13),Rey auditory verbal learning test immediate score(RAVLT Immediate),functional activities questionnaire(FAQ),and mini-mental state examination(MMSE)were used as longitudinal neuropsychological score metrics.Random survival forests with multivariate longitudinal endogenous covariates were used to construct a dynamic prediction model of the risk of developing AD in patients with MCI in the training set.The predictive performance of the model was evaluated in the test set using time-dependent areas under the receiver operator characteristic curve(AUC)and Brier score(BS).Results For the prediction of the risk of developing AD in patients with MCI,longitudinal neuropsychological scores were more important predictors than baseline clinical data,with FAQ being the strongest predictor.The dynamic prediction model had high predictive performance in the test set,with AUC ranging from 0.7695 to 0.8987 and BS ranging from 0.1369 to 0.2184.Conclusion Random survival forests with multivariate longitudinal endogenous covariates can be used to combine multivariate longitudinal neuropsychological scores to construct a prediction model for the risk of developing AD in patients with MCI,with high predictive performance and the ability to achieve individual dynamic prediction.
