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 from Europe 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.

中文翻译：

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普通人群痴呆风险预测模型的开发和验证：三项纵向研究的分析。

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。作者开发并验证了预测模型，以计算人口老龄化导致痴呆的10年绝对风险。方法在1995年至2011年之间，对来自2710位60岁以上老年痴呆症患者的人口统计学，临床，神经心理学，遗传和神经影像学参数进行了收集，收集了基本模型和扩展模型以在考虑其他原因导致的死亡竞争风险的同时，预测痴呆症的10年风险。使用乐观校正后的C统计量和校准图评估模型的性能，并且该模型已在荷兰基于Zoetermeer的流行病学预防研究和阿尔茨海默氏病神经影像学倡议研究队列1（ADNI-1）中进行了外部验证。结果在20,324人年的随访中，有181名参与者患了痴呆症。使用年龄，中风病史，主观记忆力下降以及需要财政或药物协助的基本痴呆风险模型得出的C统计量为0.78（95％CI = 0.75，0.81）。随后，结合了基本模型和其他认知，遗传和影像学预测因子的扩展模型得出的C统计量为0.86（95％CI = 0.83，0.88）。该模型在来自欧洲和美国的外部验证队列中表现良好。结论在社区居民中，通过结合初级保健机构中随时可用的预测因素的信息，可以准确预测10年痴呆症的风险。通过使用有关认知表现，基因分型和脑成像的数据，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罹患痴呆症的高风险人士，并能够为试验设计提供信息。