Evidence map›Paper›PMID 37524859›Full record

ArticleNPJ digital medicine2023

A gender specific risk assessment of coronary heart disease based on physical examination data.

Hui Yang, Ya-Mei Luo, Cai-Yi Ma, Tian-Yu Zhang, Tao Zhou, Xiao-Lei Ren, Xiao-Lin He, Ke-Jun Deng, Dan Yan, Hua Tang and 1 more

Abstract read
In one paragraph

Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

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  8. Cancer Drug Sensitivity Prediction Based on Deep Transfer Learning.International journal of molecular sciences · 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

11 authors.

Hui YangSchool of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China.ORCID http://orcid.org/0000-0001-7122-1212
Ya-Mei LuoSchool of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China.
Cai-Yi MaSchool of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Tian-Yu ZhangSchool of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Tao ZhouSchool of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Xiao-Lei RenSichuan Chuanjiang Science and Technology Research Institute Co., Ltd, Luzhou, 646000, China.
Xiao-Lin HeSichuan Chuanjiang Science and Technology Research Institute Co., Ltd, Luzhou, 646000, China.
Ke-Jun DengSchool of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Dan YanBeijing Institute of Clinical Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Hua TangSchool of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China. huatang@swmu.edu.cn.
Hao LinSchool of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China. hlin@uestc.edu.cn.ORCID http://orcid.org/0000-0001-6265-2862

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its prevention and management. Physical examination data has the advantages of wide coverage, large capacity, and easy collection. Therefore, here we report a gender-specific cascading system for risk assessment of CHD based on physical examination data. The dataset consists of 39,538 CHD patients and 640,465 healthy individuals from the Luzhou Health Commission in Sichuan, China. Fifty physical examination characteristics were considered, and after feature screening, ten risk factors were identified. To facilitate large-scale CHD risk screening, a CHD risk model was developed using a fully connected network (FCN). For males, the model achieves AUCs of 0.8671 and 0.8659, respectively on the independent test set and the external validation set. For females, the AUCs of the model are 0.8991 and 0.9006, respectively on the independent test set and the external validation set. Furthermore, to enhance the convenience and flexibility of the model in clinical and real-life scenarios, we established a CHD risk scorecard base on logistic regression (LR). The results show that, for both males and females, the AUCs of the scorecard on the independent test set and the external verification set are only slightly lower (<0.05) than those of the corresponding prediction model, indicating that the scorecard construction does not result in a significant loss of information. To promote CHD personal lifestyle management, an online CHD risk assessment system has been established, which can be freely accessed at http://lin-group.cn/server/CHD/index.html .

Identifiers

PMID37524859
PMCPMC10390496

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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.