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

Area of Science:

• Cardiology
• Clinical Medicine
• Patient Outcomes

Background:

• Heart failure (HF) presents complex symptoms impacting patient quality of life.
• Effective symptom management in HF remains a significant clinical challenge.
• Understanding symptom interactions is key to improving HF care.

Purpose of the Study:

• Investigate HF symptom incidence and severity.
• Construct a HF symptom network.
• Identify core symptoms for targeted interventions.

Main Methods:

• 1051 HF patients assessed using the Memorial Heart Failure Symptom Assessment Scale.
• Regularized partial correlation network analysis applied.
• Symptom centrality and network structure analyzed.

Main Results:

• Palpitations were most common; lack of energy and depression were most severe.
• Chest pain identified as the core symptom in the network.
• Chest pain demonstrated the highest predictability within the symptom network.

Conclusions:

• Intervening in chest pain can reduce overall symptom severity in HF.
• Targeting core and early symptoms like fatigue can enhance management.
• Findings guide precise, effective symptom management strategies for HF patients.