Evidence map›Paper›PMID 33995200›Full record

ArticleFrontiers in psychology2021

Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach.

Haiyun Chu, Lu Chen, Xiuxian Yang, Xiaohui Qiu, Zhengxue Qiao, Xuejia Song, Erying Zhao, Jiawei Zhou, Wenxin Zhang, Anam Mehmood and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Refining PREVENT prediction models for 10-year risk of cardiovascular disease using measures of anxiety and depression.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2025
    Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Review
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.

Haiyun ChuDepartment of Medical Psychology, Harbin Medical University, Harbin, China.
Lu ChenDepartment of Endocrinology, Peking Union Medical College Hospital, Beijing, China.
Xiuxian YangDepartment of Medical Psychology, Harbin Medical University, Harbin, China.
Xiaohui QiuDepartment of Medical Psychology, Harbin Medical University, Harbin, China.
Zhengxue QiaoDepartment of Medical Psychology, Harbin Medical University, Harbin, China.
Xuejia SongDepartment of Medical Psychology, Harbin Medical University, Harbin, China.
Erying ZhaoDepartment of Medical Psychology, Harbin Medical University, Harbin, China.
Jiawei ZhouDepartment of Medical Psychology, Harbin Medical University, Harbin, China.
Wenxin ZhangDepartment of Medical Psychology, Harbin Medical University, Harbin, China.
Anam MehmoodDepartment of Medical Psychology, Harbin Medical University, Harbin, China.
Hui PanDepartment of Endocrinology, Peking Union Medical College Hospital, Beijing, China.
Yanjie YangDepartment of Medical Psychology, Harbin Medical University, Harbin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular disease (CVD) is a major complication of type 2 diabetes mellitus (T2DM). In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network (DNN) to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve (ROC) analysis was used to evaluate the model performance. Of participants, 272 (32.6%) were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion.

Indexed as

bio-psycho-social factorscardiovascular diseaseChinamachine learningtype 2 diabetes mellitus

Identifiers

PMID33995200
PMCPMC8113686

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.