Supplementary Material for: What’s new in dementia risk prediction modelling? An updated systematic review

Introduction
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 is to update our previous reviews to explore, and critically review, new developments in dementia risk modelling.
Methods
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.
Results
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.
Conclusion
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.

引言
识别痴呆高风险人群，对于优化临床诊疗、制定有效预防策略以及确定临床试验入选资格均至关重要。自2010年与2015年本团队开展系统性综述以来，痴呆风险预测模型领域的研究呈爆发式增长态势。本研究旨在更新既往综述工作，对痴呆风险预测模型的最新进展进行系统探索与批判性评价。
方法
本研究检索了2014年3月至2022年6月期间MEDLINE、Embase、Scopus及Web of Science数据库。纳入研究需满足以下条件：基于人群或社区队列开展（含电子健康档案数据），构建了老年新发痴呆预测模型，并报告了模型性能指标，如区分度、校准度或外部验证结果。
结果
本次电子检索共检出9209篇文献，其中74篇符合纳入标准。研究发现，2014年以来发表的新型痴呆风险预测模型数量大幅增长（新增模型超50个），其中采用机器学习（machine learning）方法构建的模型占比亦有所提升。目前已有超过450种独特的预测（组分）变量被应用于模型测试与验证。共有19项研究（占纳入研究的26%）对新开发或已有的痴呆风险预测模型开展了外部验证，研究结果参差不齐。此外，本领域首次在低收入和中等收入国家（low- and middle-income countries, LMICs）构建了痴呆风险预测模型，另有研究在少数种族与族裔群体中完成了模型验证工作。
结论
痴呆风险预测模型领域的相关研究随分析方法的革新以及在低收入和中等收入国家（LMICs）的实践应用正快速发展。然而，目前仍难以就哪一款模型最适合临床常规应用给出明确推荐意见。当前亟需在普通人群中开发一款适宜、稳健且经过验证的风险预测模型，使其能够在临床实践中广泛落地应用，以助力痴呆预防工作的开展。