Tang Eugene Y H, Harrison Stephanie L, Errington Linda, Gordon Mark F, Visser Pieter Jelle, Novak Gerald, Dufouil Carole, Brayne Carol, Robinson Louise, Launer Lenore J, Stephan Blossom C M
Institute of Health and Society, Newcastle University Institute of Ageing, Newcastle University, Newcastle upon Tyne, NE2 4AX, United Kingdom.
Medical School, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom.
PLoS One. 2015 Sep 3;10(9):e0136181. doi: 10.1371/journal.pone.0136181. eCollection 2015.
Accurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model prognostic performance.
Our previous systematic review was updated with a search from January 2009 to March 2014 in electronic databases (MEDLINE, Embase, Scopus, Web of Science). Articles examining risk of dementia in non-demented individuals and including measures of sensitivity, specificity or the area under the curve (AUC) or c-statistic were included.
In total, 1,234 articles were identified from the search; 21 articles met inclusion criteria. New developments in dementia risk prediction include the testing of non-APOE genes, use of non-traditional dementia risk factors, incorporation of diet, physical function and ethnicity, and model development in specific subgroups of the population including individuals with diabetes and those with different educational levels. Four models have been externally validated. Three studies considered time or cost implications of computing the model.
There is no one model that is recommended for dementia risk prediction in population-based settings. Further, it is unlikely that one model will fit all. Consideration of the optimal features of new models should focus on methodology (setting/sample, model development and testing in a replication cohort) and the acceptability and cost of attaining the risk variables included in the prediction score. Further work is required to validate existing models or develop new ones in different populations as well as determine the ethical implications of dementia risk prediction, before applying the particular models in population or clinical settings.
准确识别痴呆高危个体对临床护理、临床试验纳入标准及预防策略的制定具有重要影响。已开发出众多用于预测痴呆的模型。为评估这些模型，我们在2010年进行了一项系统评价，并于2014年进行了更新，因为该领域发表的研究有所增加。在此，我们对所选纳入变量进行了批判性分析，并对模型的预后性能进行了评估。
通过在电子数据库（MEDLINE、Embase、Scopus、Web of Science）中检索2009年1月至2014年3月的文献，对我们之前的系统评价进行更新。纳入研究非痴呆个体痴呆风险且包含敏感性、特异性、曲线下面积（AUC）或c统计量测量值的文章。
检索共识别出1234篇文章；21篇文章符合纳入标准。痴呆风险预测的新进展包括非APOE基因检测、非传统痴呆风险因素的应用、饮食、身体功能和种族因素的纳入，以及在特定人群亚组（包括糖尿病患者和不同教育水平个体）中进行模型开发。有四个模型已在外部得到验证。三项研究考虑了计算模型的时间或成本影响。
在基于人群的环境中，没有一个模型被推荐用于痴呆风险预测。此外，不太可能有一个模型适用于所有人。对新模型最佳特征的考量应聚焦于方法学（设置/样本、在重复队列中进行模型开发和测试）以及获取预测评分中包含的风险变量的可接受性和成本。在将特定模型应用于人群或临床环境之前，需要进一步开展工作以验证现有模型或在不同人群中开发新模型，并确定痴呆风险预测的伦理意义。
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