Simultaneous Symptom Network Analysis of Heart Failure Patients: A Cross-Sectional Study.

Authors

Rui Wu, Lu Chen, Yingjie Li, Huiwen Wang, Mengdie Liu, Yingxia Yao, Huiyan Chen, Dan Xiao

Published in

Risk management and healthcare policy. Volume 18. Pages 2493-2503. Epub Jul 26, 2025.

Abstract

Heart failure is a complex clinical syndrome associated with various symptoms that significantly impact patients' quality of life. Effective management of these symptoms remains a major challenge. Identifying and understanding the interactions between these symptoms is crucial for improving symptom control and patient outcomes.
This study aimed to investigate the incidence and severity of symptoms in heart failure patients, construct a symptom network of heart failure patients, and explore the centrality of symptoms in the network. The goal was to identify core symptoms and explore the potential targets for symptom intervention.
A total of 1051 heart failure patients were selected through convenience sampling. The Chinese version of the Memorial Heart Failure Symptom Assessment Scale was used to assess the prevalence and severity of symptoms. Regularized partial correlation network analysis was employed to construct the symptom network and evaluate the centrality of each symptom within the network.
Palpitations were found to be the most common symptom among heart failure patients, while lack of energy and depression were the most severe symptoms. In the symptom network, chest pain emerged as the core symptom with the highest predictability.
Intervening with chest pain as the core symptom can effectively reduce the severity of the entire symptom network. Early intervention for symptoms such as lack of energy can alleviate the burden of symptom management. Identifying predictable symptoms can help guide targeted symptom management strategies. Healthcare professionals can use the symptom patterns identified in this study to develop more precise and effective symptom management plans for heart failure patients.

PMID:
40741487
Bibliographic data and abstract were imported from PubMed on 31 Jul 2025.

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