Symptom Burden in Patients with Acute Coronary Syndrome After Percutaneous Coronary Intervention: A Network and Latent Profile Analysis

Purpose: To describe the network structure and heterogeneity of symptom burden in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI), and to examine factors associated with different symptom burden profiles to inform risk-stratified management after PCI. Patients and Methods: A convenience sample of 261 patients with ACS who underwent PCI at a tertiary hospital in Chongqing between November 2024 and August 2025 was recruited. Data were collected using a demographic questionnaire, the Cardiac Symptom Survey, and the Seattle Angina Questionnaire. Network analysis was conducted to identify inter-symptom associations and the structural characteristics of the symptom network. Latent profile analysis (LPA) was performed to classify symptom burden patterns, and multinomial logistic regression analysis was used to explore factors associated with profile membership. Results: Network analysis indicated that depression was the most central symptom (strength rs = 0.886). Latent profile analysis identified three distinct symptom burden profiles: Low Symptom Burden (64.0%), Moderate Symptom Burden with Prominent Access-Site Pain (12.3%), and High Symptom Burden (23.8%). Body mass index (BMI) was a significant independent predictor of symptom burden; compared with patients with normal BMI, those who were overweight or obese were significantly more likely to belong to the High ( OR = 2.47, 95% CI : 1.31– 4.67) or Moderate ( OR = 2.41, 95% CI : 1.04– 5.59) burden groups. Furthermore, higher symptom burden was significantly associated with reduced health-related quality of life, particularly regarding angina frequency and disease perception ( P < 0.05). Conclusion: Symptom burden in patients with ACS after PCI demonstrates substantial individual heterogeneity. Depression occupies a central position within the symptom network, and BMI is associated with moderate and high symptom burden profiles. These findings suggest that integrating symptom network characteristics and BMI status into post–PCI assessment may facilitate risk-stratified management and targeted psychological and weight-related interventions to improve recovery outcomes. Keywords: acute coronary syndrome, percutaneous coronary intervention, network analysis, latent profile analysis, symptoms, symptom management