Evidence map›Paper›PMID 39554762›Full record

ArticleAJPM focus2024

Comprehensive Health Assessment Using Risk Prediction for Multiple Diseases Based on Health Checkup Data.

Kosuke Yasuda, Shiori Tomoda, Mayumi Suzuki, Toshikazu Wada, Toshiyuki Fujikawa, Toru Kikutsuji, Shintaro Kato

Abstract read
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Article in AJPM focus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Kosuke YasudaNEC Solution Innovators, Ltd., Tokyo, Japan.
Shiori TomodaNEC Solution Innovators, Ltd., Tokyo, Japan.
Mayumi SuzukiKurashiki Central Hospital Preventive Healthcare Plaza, Okayama, Japan.
Toshikazu WadaKurashiki Central Hospital Preventive Healthcare Plaza, Okayama, Japan.
Toshiyuki FujikawaKurashiki Central Hospital, Okayama, Japan.
Toru KikutsujiKurashiki Central Hospital Preventive Healthcare Plaza, Okayama, Japan.
Shintaro KatoNEC Solution Innovators, Ltd., Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Tools developed to assess individuals' comprehensive health status would be beneficial for personalized prevention and treatment. This study aimed to develop a set of risk prediction models to estimate the risk for multiple diseases such as heart, blood vessel, brain, metabolic, liver, and kidney diseases using health checkup data only. Methods: This is a retrospective study that used health checkup data combined with diagnostic information from electronic health records of Kurashiki Central Hospital in Okayama, Japan. All exposure factors were measured at the first health checkup visit, including demographic characteristics, laboratory test results, lifestyle questionnaires, medication use, and medical history. Primary outcomes were the diagnoses of 15 diseases during the follow-up period. Cox proportional hazard regression was applied to develop risk prediction models for heart, blood vessel, brain, metabolic, liver, and kidney diseases. Area under the curve with 4-year risk assessments were performed to evaluate the models. Results: From January 2012 to September 2022, a total of 92,174 individuals aged 15-96 years underwent general health checkups. The area under the curve of the models in validation datasets was as follows: atrial fibrillation, 0.81; acute myocardial infarction, 0.81; heart failure, 0.76; cardiomyopathy, 0.72; angina pectoris, 0.70; atherosclerosis, 0.82; hypertension, 0.80; cerebral infarction, 0.77; intracerebral hemorrhage, 0.68; subarachnoid hemorrhage, 0.50; type-2 diabetes mellitus, 0.82; hyperlipidemia, 0.70; alcoholic liver disease, 0.91; liver fibrosis, 0.92; and chronic kidney disease, 0.80. Conclusions: A set of prediction models to estimate multi-disease risk simultaneously from health checkup results may help to assess comprehensive individual health status and facilitate personalized prevention and early diagnosis.

Identifiers

PMID39554762
PMCPMC11567062

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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 Socratic graph.