Evidence map›Paper›PMID 34337134›Full record

ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2021

Development of a dementia prediction model for primary care: The Hisayama Study.

Takanori Honda, Tomoyuki Ohara, Daigo Yoshida, Mao Shibata, Yuki Ishida, Yoshihiko Furuta, Emi Oishi, Yoichiro Hirakawa, Satoko Sakata, Jun Hata and 2 more

Abstract read
In one paragraph

Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

  1. Trial
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  11. Development of a dementia prediction model for primary care: The Hisayama Study.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021
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  13. Alzheimer's & dementia (Amsterdam, Netherlands)
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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

12 authors.

Takanori HondaDepartment of Epidemiology and Public Health Graduate School of Medical Sciences Kyushu University Fukuoka Japan.
Tomoyuki OharaDepartment of Epidemiology and Public Health Graduate School of Medical Sciences Kyushu University Fukuoka Japan.
Daigo YoshidaDepartment of Epidemiology and Public Health Graduate School of Medical Sciences Kyushu University Fukuoka Japan.
Mao ShibataDepartment of Epidemiology and Public Health Graduate School of Medical Sciences Kyushu University Fukuoka Japan.
Yuki IshidaDepartment of Psychology Faculty of Literature Kurume University Fukuoka Japan.
Yoshihiko FurutaDepartment of Epidemiology and Public Health Graduate School of Medical Sciences Kyushu University Fukuoka Japan.
Emi OishiDepartment of Epidemiology and Public Health Graduate School of Medical Sciences Kyushu University Fukuoka Japan.
Yoichiro HirakawaDepartment of Epidemiology and Public Health Graduate School of Medical Sciences Kyushu University Fukuoka Japan.
Satoko SakataDepartment of Epidemiology and Public Health Graduate School of Medical Sciences Kyushu University Fukuoka Japan.
Jun HataDepartment of Epidemiology and Public Health Graduate School of Medical Sciences Kyushu University Fukuoka Japan.
Tomohiro NakaoDepartment of Neuropsychiatry Graduate School of Medical Sciences Kyushu University Fukuoka Japan.
Toshiharu NinomiyaDepartment of Epidemiology and Public Health Graduate School of Medical Sciences Kyushu University Fukuoka Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionWe aimed to develop a risk prediction model for incident dementia using predictors that are available in primary-care settings.

methodsA total of 795 subjects aged 65 years or over were prospectively followed-up from 1988 to 2012. A Cox proportional-hazards regression was used to develop a multivariable prediction model. The developed model was translated into a simplified scoring system based on the beta-coefficient. The discrimination of the model was assessed by Harrell's C statistic, and the calibration was assessed by a calibration plot.

resultsDuring the follow-up period, 364 subjects developed dementia. In the multivariable model, age, female sex, low education, leanness, hypertension, diabetes, history of stroke, current smoking, and sedentariness were selected as predictors. The developed model and simplified score showed good discrimination and calibration. DISCUSSION: The developed risk prediction model is feasible and practically useful in primary-care settings to identify individuals at high risk for future dementia.

Indexed as

dementiageneral populationprimary preventionprospective studyrisk prediction model

Identifiers

PMID34337134
PMCPMC8319663

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Registered trials

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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.