Evidence map›Paper›PMID 39867200›Full record

ArticleReviews in cardiovascular medicine2025

Construction and Validation of a Major Depression Risk Predictive Model for Patients with Coronary Heart Disease: Insights from NHANES 2005-2018.

Li-Xiang Zhang, Shan-Bing Hou, Fang-Fang Zhao, Ting-Ting Wang, Ying Jiang, Xiao-Juan Zhou, Jiao-Yu Cao

Abstract read
In one paragraph

Article in Reviews in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Li-Xiang ZhangDepartment of Cardiology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, 230001 Hefei, Anhui, China.ORCID https://orcid.org/0000-0002-9139-1064
Shan-Bing HouDepartment of Emergency, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, 230001 Hefei, Anhui, China.ORCID https://orcid.org/0000-0003-1253-6155
Fang-Fang ZhaoDepartment of Rehabilitation Medicine, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, 230001 Hefei, Anhui, China.ORCID https://orcid.org/0000-0003-4737-6462
Ting-Ting WangDepartment of Cardiology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, 230001 Hefei, Anhui, China.ORCID https://orcid.org/0000-0002-5372-6433
Ying JiangDepartment of Emergency, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, 230001 Hefei, Anhui, China.ORCID https://orcid.org/0009-0005-1141-7762
Xiao-Juan ZhouDepartment of Cardiology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, 230001 Hefei, Anhui, China.ORCID https://orcid.org/0000-0002-1197-3751
Jiao-Yu CaoDepartment of Cardiology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, 230001 Hefei, Anhui, China.ORCID https://orcid.org/0009-0004-1503-3005

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aimed to develop and validate a predictive model for major depression risk in adult patients with coronary heart disease (CHD), offering evidence for targeted prevention and intervention. Methods: Using data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018, 1098 adults with CHD were included. A weighted logistic regression model was applied to construct and validate a nomogram-based prediction tool for major depression in this population. Results: The weighted prevalence of major depression among these patients was 13.95%. Multivariate weighted logistic regression revealed that waist circumference, smoking status, arthritis, sleep disorders, and restricted work capacity were independent risk factors for major depression (odds ratio (OR) >1, Conclusions: With a weighted prevalence of 13.95%, this nomogram model shows excellent predictive performance and clinical relevance for predicting major depression risk in patients with CHD. Thus, the model can be applied to aid healthcare professionals in identifying high-risk individuals and implementing targeted preventive strategies, potentially lowering the incidence of major depression in this patient population.

Indexed as

coronary heart diseasemajor depressionNHANESpredictive modelrisk factors

Identifiers

PMID39867200
PMCPMC11759964

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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.