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Composite risk scores for predicting dementia

机译：预测痴呆的综合风险评分

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Purpose of reviewA key priority in dementia research is the development of tools to identify individuals at high risk of dementia. This is important to prevent or delay dementia onset and as we move towards personalized medicine.Recent findingsNumerous models (n>50) for predicting dementia have been developed. These vary in the number (0 to 20+) and type (e.g. demographics, health, neuropsychological, and genetic) of predictor variables used for risk calculation, follow-up length (1-20 years) and age at screening (mid vs laterlife). Evaluation of the models shows that most have moderate-to-poor predictive accuracy. Few have been externally validated, raising questions about their generalizability outside the cohorts from which they were developed. The results highlight that if additional models are proposed the field will be overwhelmed with many competing risk models, making it difficult to reach consensus on which is best.SummaryNumerous models for predicting dementia have been proposed but are limited by a lack of external validation and evaluation of economic impact. Innovative methods and data designs may be needed to improve derivation of dementia risk scores. Having a method for predicting dementia risk could transform medical research and allow for earlier testing of intervention strategies.
机译：审查目的痴呆症研究的一个关键优先事项是开发工具来识别处于痴呆症高风险的人。这对于预防或延缓痴呆的发作非常重要，并且随着我们朝着个性化医学方向发展。最近的发现已经开发出许多预测痴呆的模型（n> 50）。这些变量的数量（0至20+）和类型（例如人口统计学，健康状况，神经心理学和遗传学）用于风险计算，随访时间（1-20岁）和筛查年龄（中年与晚年）之间有所不同）。对模型的评估表明，大多数模型具有中等到较差的预测准确性。很少有经过外部验证的项目，这引发了人们对它们的可推广性的质疑，而这些群体并非来自于它们的起源。结果表明，如果提出其他模型，那么该领域将被许多竞争性风险模型所淹没，从而难以就哪种最佳模型达成共识。总结提出了许多预测痴呆的模型，但由于缺乏外部验证和评估而受到限制经济影响。可能需要创新的方法和数据设计来改善痴呆风险评分的推导。拥有一种预测痴呆症风险的方法可以改变医学研究，并允许更早地测试干预策略。

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