Evidence map›Paper›PMID 36394871›Full record

ArticleJAMA network open2022

Development of a Clinical Risk Score Prediction Tool for 5-, 9-, and 13-Year Risk of Dementia.

Lina Ren, Junxian Liang, Feng Wan, Yongjun Wang, Xi-Jian Dai

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
3.7field-weighted citation impact, top 6% of its field
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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.

  1. Pooled it
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  9. A point-based cognitive impairment scoring system for southeast Asian adults.The journal of prevention of Alzheimer's disease · 2025
    Article
  10. Plasma protein risk scores for mild cognitive impairment and Alzheimer's disease in the Framingham heart study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
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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

5 authors at 4 institutions in 2 countries.

Lina RenShenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, China.
Junxian LiangDepartment of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau, China.
Feng WanDepartment of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau, China.
Yongjun WangShenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, China.
Xi-Jian DaiDepartment of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau, China.
University of Macau · MOAnhui Medical University · CNSecond Affiliated Hospital of Nanchang University · CNShenzhen KangNing Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Although researchers have devoted substantial efforts, money, and time to studying the causes of dementia and the means to prevent it, no effective treatment exists yet. Identifying preclinical risk factors of dementia could help prevent or delay its progression. Objective: To develop a point risk score prediction model of dementia. Design, Setting, and Participants: This study used a large UK population-based prospective cohort study conducted between March 13, 2006, and October 1, 2010. Data analysis was performed from June 7 to September 15, 2021. Individual analyses of time end points were concluded at the first dementia diagnosis during the follow-up period. The data were split into training and testing data sets to separately establish and validate a prediction model. Main Outcomes and Measures: Outcomes of interest included 5-, 9-, and 13-year dementia risk. Least absolute shrinkage and selection operator and multivariate Cox proportional hazards regression models were used to identify available and practical dementia predictors. A point risk score model was developed for the individual prediction of 5-, 9-, and 13-year dementia risk. Results: A total of 502 505 participants were selected; the population after exclusions for missing data and dementia diagnosis at baseline was 444 695 (205 187 men; mean [SD] age, 56.74 [8.18] years; 239 508 women; mean [SD] age, 56.20 [8.01] years). Dementia occurrence during the 13 years of follow-up was 0.7% for men and 0.5% for women. The C statistic of the final multivariate Cox proportional hazards regression model was 0.86 for men and 0.85 for women in the training data set, and 0.85 for men and 0.87 for women in the testing data set. Men and women shared some modifiable risk and protective factors, but they also presented independent risk factors that accounted for 31.7% of men developing dementia and 53.35% of women developing dementia according to the weighted population-attributable fraction. The total point score of the risk score model ranged from -18 to 30 in men and -17 to 30 in women. The risk score model yielded nearly 100% prediction accuracy of 13-year dementia risk both in men and women. Conclusions and Relevance: In this diagnostic study, a practical risk score tool was developed for individual prediction of dementia risk, which may help individuals identify their potential risk profile and provide guidance on precise and timely actions to promote dementia delay or prevention.

Indexed as

DementiaCausalityFemaleHumansMaleMiddle AgedProportional Hazards ModelsProspective StudiesRisk Factors

Identifiers

PMID36394871
PMCPMC9672974
OpenAlexW4309510073

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.