PDF(Pubmed)
Abstract：
UNASSIGNED: Identifying individuals at high risk of dementia is critical to optimized clinical care, formulating effective preventative strategies, and determining eligibility for clinical trials. Since our previous systematic reviews in 2010 and 2015, there has been a surge in dementia risk prediction modelling. The aim of this study was to update our previous reviews to explore, and critically review, new developments in dementia risk modelling.
UNASSIGNED: MEDLINE, Embase, Scopus, and Web of Science were searched from March 2014 to June 2022. Studies were included if they were population- or community-based cohorts (including electronic health record data), had developed a model for predicting late-life incident dementia, and included model performance indices such as discrimination, calibration, or external validation.
UNASSIGNED: In total, 9,209 articles were identified from the electronic search, of which 74 met the inclusion criteria. We found a substantial increase in the number of new models published from 2014 (>50 new models), including an increase in the number of models developed using machine learning. Over 450 unique predictor (component) variables have been tested. Nineteen studies (26%) undertook external validation of newly developed or existing models, with mixed results. For the first time, models have also been developed in low- and middle-income countries (LMICs) and others validated in racial and ethnic minority groups.
UNASSIGNED: The literature on dementia risk prediction modelling is rapidly evolving with new analytical developments and testing in LMICs. However, it is still challenging to make recommendations about which one model is the most suitable for routine use in a clinical setting. There is an urgent need to develop a suitable, robust, validated risk prediction model in the general population that can be widely implemented in clinical practice to improve dementia prevention.
UNASSIGNED: MEDLINE, Embase, Scopus, and Web of Science were searched from March 2014 to June 2022. Studies were included if they were population- or community-based cohorts (including electronic health record data), had developed a model for predicting late-life incident dementia, and included model performance indices such as discrimination, calibration, or external validation.
UNASSIGNED: In total, 9,209 articles were identified from the electronic search, of which 74 met the inclusion criteria. We found a substantial increase in the number of new models published from 2014 (>50 new models), including an increase in the number of models developed using machine learning. Over 450 unique predictor (component) variables have been tested. Nineteen studies (26%) undertook external validation of newly developed or existing models, with mixed results. For the first time, models have also been developed in low- and middle-income countries (LMICs) and others validated in racial and ethnic minority groups.
UNASSIGNED: The literature on dementia risk prediction modelling is rapidly evolving with new analytical developments and testing in LMICs. However, it is still challenging to make recommendations about which one model is the most suitable for routine use in a clinical setting. There is an urgent need to develop a suitable, robust, validated risk prediction model in the general population that can be widely implemented in clinical practice to improve dementia prevention.
摘要：
■识别痴呆症高危人群对于优化临床护理至关重要，制定有效的预防策略，并确定临床试验的资格。自我们在2010年和2015年进行系统评价以来，痴呆症风险预测模型激增。这项研究的目的是更新我们以前的评论，批判性审查，痴呆症风险建模的新进展。
■MEDLINE,Embase,Scopus,和WebofScience于2014年3月至2022年6月进行了搜索。如果研究是基于人群或社区的队列（包括电子健康记录数据），开发了一个预测晚期痴呆的模型，并包括模型性能指数，如歧视，校准,或外部验证。
■总共，从电子搜索中识别出9209篇文章，其中74人符合纳入标准。我们发现，自2014年以来，发布的新车型数量大幅增加(>50款新车型)，包括使用机器学习开发的模型数量的增加。已经测试了450多个独特的预测变量(分量)。19项研究(26%)对新开发或现有模型进行了外部验证，结果喜忧参半。第一次,还在低收入和中等收入国家(LMICs)开发了模型，并在种族和少数族裔群体中验证了其他模型。
■关于痴呆风险预测模型的文献随着新的分析发展和LMIC的测试而迅速发展。然而,就哪一种模型最适合临床常规使用提出建议仍具有挑战性.迫切需要开发一种合适的,健壮,在普通人群中验证的风险预测模型，可以在临床实践中广泛实施，以提高痴呆的预防。
■MEDLINE,Embase,Scopus,和WebofScience于2014年3月至2022年6月进行了搜索。如果研究是基于人群或社区的队列（包括电子健康记录数据），开发了一个预测晚期痴呆的模型，并包括模型性能指数，如歧视，校准,或外部验证。
■总共，从电子搜索中识别出9209篇文章，其中74人符合纳入标准。我们发现，自2014年以来，发布的新车型数量大幅增加(>50款新车型)，包括使用机器学习开发的模型数量的增加。已经测试了450多个独特的预测变量(分量)。19项研究(26%)对新开发或现有模型进行了外部验证，结果喜忧参半。第一次,还在低收入和中等收入国家(LMICs)开发了模型，并在种族和少数族裔群体中验证了其他模型。
■关于痴呆风险预测模型的文献随着新的分析发展和LMIC的测试而迅速发展。然而,就哪一种模型最适合临床常规使用提出建议仍具有挑战性.迫切需要开发一种合适的,健壮,在普通人群中验证的风险预测模型，可以在临床实践中广泛实施，以提高痴呆的预防。
