Evidence map›Paper›PMID 39558350›Full record

ArticleBMC nursing2024

The heterogeneous depression trajectory and its predictors in coronary heart disease patients undergoing home-based cardiac rehabilitation: a cohort study.

Zhen Yang, Xutong Zheng, Liyu Xu, Yu Gao, Chunqi Zhang, Aiping Wang

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Article in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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6 citing papers in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Zhen YangThe First Affiliated Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning Province, China.
Xutong ZhengThe First Affiliated Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning Province, China.
Liyu XuThe First Affiliated Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning Province, China.
Yu GaoThe First Affiliated Hospital of Jinzhou Medical University, No.2, Section 5, Renmin Street, Guta District, Jinzhou, Liaoning Province, China.
Chunqi ZhangThe First Affiliated Hospital of Jinzhou Medical University, No.2, Section 5, Renmin Street, Guta District, Jinzhou, Liaoning Province, China.
Aiping WangThe First Affiliated Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning Province, China. jianghaoran88@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPsychological management, particularly addressing depression, is crucial for the effectiveness of home-based cardiac rehabilitation. This study aimed to explore the depression trajectories of coronary heart disease patients during home-based cardiac rehabilitation, identify trajectories associated with cardiovascular readmission, and integrate them into a heterogeneous depression trajectory while examining its predictors.

methodsA prospective cohort study was conducted at a large cardiac rehabilitation center in mainland China. Participants completed the Patient Health Questionnaire-9 to assess depression levels during the 6-month home-based cardiac rehabilitation, with monthly follow-ups. Using latent class growth models to explore depression trajectories. The relationship between different trajectories and cardiovascular readmission was determined using Cox proportional hazards regression, identifying heterogeneous depression trajectory. Logistic regression analysis was employed to explore the influencing factors of heterogeneous depression trajectory.

resultsA total of 346 eligible patients with coronary heart disease participated in the study. Four distinct depression trajectories were identified: sustained no depression (48.0%), delayed onset (15.9%), low U-shaped depression (25.1%), and sustained depression (11.0%). Depression trajectories significantly impacted cardiovascular readmission rates, with higher risks observed in the delayed onset (HR: 4.707, 95% CI: 1.766-12.544) and sustained depression (HR: 8.832, 95% CI: 3.281-23.773) groups. These two groups were combined and termed heterogeneous depression trajectory. Importantly, education level, number of chronic diseases, resilience, social support, and anxiety were independent predictors of heterogeneous depression trajectory.

conclusionsDepression trajectories during home-based cardiac rehabilitation are significantly heterogeneous and influence cardiovascular outcomes. Early identification and management of high-risk factors can enhance psychological health and reduce readmission rates.

Indexed as

Cardiac rehabilitationCoronary artery diseaseDepressionDepression trajectoriesHome-based

Identifiers

PMID39558350
PMCPMC11571892

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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.