Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Stratenum 6.131, PO BOX 85500, 3508, GA, Utrecht, The Netherlands.
Department of Neurology, College of Physicians and Surgeons, Taub Institute for Research On Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA.
Eur J Epidemiol. 2021 Oct;36(10):1025-1041. doi: 10.1007/s10654-021-00785-x. Epub 2021 Jul 25.
We aimed to evaluate the external performance of prediction models for all-cause dementia or AD in the general population, which can aid selection of high-risk individuals for clinical trials and prevention. We identified 17 out of 36 eligible published prognostic models for external validation in the population-based AGES-Reykjavik Study. Predictive performance was assessed with c statistics and calibration plots. All five models with a c statistic > .75 (.76-.81) contained cognitive testing as a predictor, while all models with lower c statistics (.67-.75) did not. Calibration ranged from good to poor across all models, including systematic risk overestimation or overestimation for particularly the highest risk group. Models that overestimate risk may be acceptable for exclusion purposes, but lack the ability to accurately identify individuals at higher dementia risk. Both updating existing models or developing new models aimed at identifying high-risk individuals, as well as more external validation studies of dementia prediction models are warranted.
我们旨在评估一般人群中全因痴呆或 AD 的预测模型的外部表现，这有助于为临床试验和预防选择高危个体。我们在基于人群的 AGES-Reykjavik 研究中确定了 17 项符合条件的已发表预后模型进行外部验证。使用 c 统计量和校准图评估预测性能。所有五个 c 统计量>0.75（0.76-0.81）的模型都包含认知测试作为预测因子，而所有 c 统计量较低的模型（0.67-0.75）都没有。所有模型的校准范围从良好到较差不等，包括系统性风险高估或对特定最高风险组的高估。对于排除目的，高估风险的模型可能是可以接受的，但缺乏准确识别痴呆风险较高个体的能力。有必要更新现有的识别高危个体的模型或开发新的模型，以及对痴呆预测模型进行更多的外部验证研究。
