Evidence map›Paper›PMID 39015518›Full record

ReviewDementia and geriatric cognitive disorders extra

What's New in Dementia Risk Prediction Modelling? An Updated Systematic Review.

Jacob Brain, Aysegul Humeyra Kafadar, Linda Errington, Rachael Kirkley, Eugene Y H Tang, Ralph K Akyea, Manpreet Bains, Carol Brayne, Grazziela Figueredo, Leanne Greene and 11 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Dementia and geriatric cognitive disorders extra. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  7. Exploring the views of key stakeholders on dementia risk prediction in primary care in areas of socioeconomic deprivation: a qualitative study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026
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  15. Public attitudes toward dementia risk prediction: A mixed-methods study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
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  18. Review
  19. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Jacob BrainInstitute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK.
Aysegul Humeyra KafadarInstitute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK.
Linda ErringtonWalton Library, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
Rachael KirkleyPopulation Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
Eugene Y H TangPopulation Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
Ralph K AkyeaPRISM Group, Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK.
Manpreet BainsNottingham Centre for Public Health and Epidemiology, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK.
Carol BrayneCambridge Public Health, University of Cambridge, Cambridge, UK.
Grazziela FigueredoSchool of Computer Science, University of Nottingham, Nottingham, UK.
Leanne GreeneExeter Clinical Trials Unit, Department of Health and Community Sciences, University of Exeter Medical School, Exeter, UK.
Jennie LouiseWomen's and Children's Hospital Research Centre and South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
Catharine MorganDivision of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK.
Eduwin PakpahanDepartment of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne, UK.
David ReevesSchool for Health Sciences, University of Manchester, Manchester, UK.
Louise RobinsonPopulation Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
Amy SalterSchool of Public Health, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia.
Mario SiervoSchool of Population Health, Curtin University, Perth, WA, Australia.
Phillip J TullyFreemasons Foundation Centre for Men's Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia.
Deborah TurnbullFreemasons Foundation Centre for Men's Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia.
Nadeem QureshiPRISM Group, Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK.
Blossom C M StephanInstitute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 was 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.

Indexed as

Alzheimer diseaseDementiaIncidenceRisk predictionStatistical model

Identifiers

PMID39015518
PMCPMC11250535

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.