Predicting Progression to Clinical Alzheimer's Disease Dementia Using the Random Survival Forest.
Predicting Progression to Clinical Alzheimer's Disease Dementia Using the Random Survival Forest.
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DOI：
10.3233/jad-230208
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发表时间：
2023
影响因子：
4
通讯作者：
Li, Zhigang
中科院分区：
文献类型：
--
作者：
Song, Shangchen;Asken, Breton;Armstrong, Melissa J.;Yang, Yang;Li, Zhigang
通过机器学习生存分析方法评估在阿尔茨海默病中心注册的参与者中发展临床阿尔茨海默病（AD）痴呆的风险对于AD痴呆管理非常重要。利用国家阿尔茨海默病协调中心（NACC）和阿尔茨海默病神经影像学倡议（ADNI）注册队列构建临床AD痴呆发作时间的预测模型。使用随机生存森林（RSF）方法构建模型，并在NACC队列和ADNI队列中进行内部和外部验证。提供了一个R包和一个Shiny应用程序来访问模型。我们建立了一个具有六个预测因子的预测模型：延迟逻辑记忆评分（故事回忆），CDR®痴呆分期仪器-方框总和，CDR®中的一般定向，功能活动问卷中记住日期的能力和支付账单的能力，以及患者年龄。模型的C指数在NACC和ADNI中分别为90.82%（SE = 0.71%）和86.51%（SE = 0.75%）。48个月时NACC的AUC和准确度分别为92.48%（SE = 1.12%）和88.66%（SE = 1.00%），ADNI的AUC和准确度分别为90.16%（SE = 1.12%）和85.00%（SE = 1.14%）。该模型具有良好的预测性能，6个预测因子易于获得，成本效益和非侵入性。该模型可用于告知临床医生和患者在4年内发生临床AD痴呆的概率，具有较高的准确性。
Assessing the risk of developing clinical Alzheimer’s disease (AD) dementia, by machine learning survival analysis approaches, among participants registered in Alzheimer’s Disease Centers is important for AD dementia management. To construct a prediction model for the onset time of clinical AD dementia using the National Alzheimer Coordinating Center (NACC) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) registered cohorts. A model was constructed using the Random Survival Forest (RSF) approach and internally and externally validated on the NACC cohort and the ADNI cohort. An R package and a Shiny app were provided for accessing the model. We built a predictive model having the six predictors: delayed logical memory score (story recall), CDR® Dementia Staging Instrument - Sum of Boxes, general orientation in CDR®, ability to remember dates and ability to pay bills in the Functional Activities Questionnaire, and patient age. The C indices of the model were 90.82% (SE = 0.71%) and 86.51% (SE = 0.75%) in NACC and ADNI respectively. The time-dependent AUC and accuracy at 48 months were 92.48% (SE = 1.12%) and 88.66% (SE = 1.00%) respectively in NACC, and 90.16% (SE = 1.12%) and 85.00% (SE = 1.14%) respectively in ADNI. The model showed good prediction performance and the six predictors were easy to obtain, cost-effective and non-invasive. The model could be used to inform clinicians and patients on the probability of developing clinical AD dementia in 4 years with high accuracy.
影响因子：
3.7
作者：
Whetten AB;Stevens JR;Cann D
通讯作者：
Cann D