Jacob Brain, Aysegul Humeyra Kafadar, Linda Errington, Rachael Kirkley, Eugene Y.H. Tang, R. Akyea, Manpreet Bains, Carol Brayne, Grazziela Figueredo, Leanne Greene, Jennie Louise, Catharine Morgan, Eduwin Pakpahan, David Reeves, Louise Robinson, Amy Salter, Mario Siervo, Phillip J. Tully, Deborah Turnbull, Nadeem Qureshi, Blossom Stephan
{"title":"What’s new in dementia risk prediction modelling? An updated systematic review","authors":"Jacob Brain, Aysegul Humeyra Kafadar, Linda Errington, Rachael Kirkley, Eugene Y.H. Tang, R. Akyea, Manpreet Bains, Carol Brayne, Grazziela Figueredo, Leanne Greene, Jennie Louise, Catharine Morgan, Eduwin Pakpahan, David Reeves, Louise Robinson, Amy Salter, Mario Siervo, Phillip J. Tully, Deborah Turnbull, Nadeem Qureshi, Blossom Stephan","doi":"10.1159/000539744","DOIUrl":null,"url":null,"abstract":"Introduction\nIdentifying 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.\nMethods\nMEDLINE, 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.\nResults \nIn 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. \nConclusion\nThe 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.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":"109 13","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1159/000539744","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.