Development and Validation of a Dementia Risk Prediction Model in the General Population: An Analysis of Three Longitudinal Studies
Development and Validation of a Dementia Risk Prediction Model in the General Population: An Analysis of Three Longitudinal Studies
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DOI：
10.1176/appi.ajp.2018.18050566
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发表时间：
2019-07-01
影响因子：
17.7
通讯作者：
Ikram, M. Arfan
中科院分区：
文献类型：
--
作者：
Licher, Silvan;Leening, Maarten J. G.;Ikram, M. Arfan
目的：识别痴呆症高危人群对于制定预防策略至关重要，但缺乏可靠的工具来对人群进行风险分层。作者开发并验证了一个预测模型，以计算老年人10年患痴呆症的绝对风险。方法：在一个大型的前瞻性人群队列中，收集了1995年至2011年间2710名60岁及以上非痴呆个体的人口学、临床、神经心理学、遗传学和神经影像学参数的数据。在考虑其他原因导致的死亡风险的同时，导出了一个基本模型和一个扩展模型来预测10年的痴呆风险。使用乐观校正的c统计量和校准图评估模型的性能，并在荷兰基于人群的Zoetermeer流行病学预防研究和阿尔茨海默病神经影像学倡议队列1 （ADNI-1）中对模型进行外部验证。结果：在20,324人年的随访期间，181名参与者患上了痴呆症。使用年龄、中风史、主观记忆衰退和需要财政或药物援助的基本痴呆风险模型的c统计量为0.78 （95% CI = 0.75, 0.81）。随后，将基本模型和额外的认知、遗传和影像学预测因素纳入扩展模型，c统计量为0.86 （95% CI = 0.83, 0.88）。这些模型在来自欧洲和美国的外部验证队列中表现良好。结论：在社区居住的个体中，通过结合初级保健环境中现成的预测因子信息，可以准确预测10年痴呆风险。通过使用认知表现、基因分型和脑成像数据，可以进一步改善痴呆症的预测。这些模型可用于识别人群中痴呆高风险的个体，并能够为试验设计提供信息。
Objective: Identification of individuals at high risk of dementia is essential for development of prevention strategies, but reliable tools are lacking for risk stratification in the population. The authors developed and validated a prediction model to calculate the 10-year absolute risk of developing dementia in an aging population.Methods: In a large, prospective population-based cohort, data were collected on demographic, clinical, neuropsychological, genetic, and neuroimaging parameters from 2,710 nondemented individuals age 60 or older, examined between 1995 and 2011. A basic and an extended model were derived to predict 10-year risk of dementia while taking into account competing risks from death due to other causes. Model performance was assessed using optimism-corrected C-statistics and calibration plots, and the models were externally validated in the Dutch population-based Epidemiological Prevention Study of Zoetermeer and in the Alzheimer's Disease Neuroimaging Initiative cohort 1 (ADNI-1).Results: During a follow-up of 20,324 person-years, 181 participants developed dementia. A basic dementia risk model using age, history of stroke, subjective memory decline, and need for assistance with finances or medication yielded a C-statistic of 0.78 (95% CI = 0.75, 0.81). Subsequently, an extended model incorporating the basic model and additional cognitive, genetic, and imaging predictors yielded a C-statistic of 0.86 (95% CI = 0.83, 0.88). The models performed well in external validation cohorts fromEurope and the United States.Conclusions: In community-dwelling individuals, 10-year dementia risk can be accurately predicted by combining information on readily available predictors in the primary care setting. Dementia prediction can be further improved by using data on cognitive performance, genotyping, and brain imaging. These models can be used to identify individuals at high risk of dementia in the population and are able to inform trial design.