Latent Class Analysis of Behavioral and Metabolic Risk Factors Among Patients with Acute Coronary Syndrome

Maryam  Shakiba; Arsalan  Salari; Sima  Masoudi; Salman  Nikfarjam; Marjan  Mahdavi Roshan; Elnaz  Abhari; Yasaman  Borghei

doi:10.18502/jbe.v11i3.21368

Maryam Shakiba Cardiovascular Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
Arsalan Salari Cardiovascular Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
Sima Masoudi Department of Biostatistics and Epidemiology, School of Medicine, Urmia University of Medical Sciences, Urmia , Iran.
Salman Nikfarjam Cardiovascular Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
Marjan Mahdavi Roshan Cardiovascular Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
Elnaz Abhari Cardiovascular Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
Yasaman Borghei Cardiovascular Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.

DOI: https://doi.org/10.18502/jbe.v11i3.21368

Keywords: Latent class analysis; Acute coronary syndrome; Risk factors; Metabolic syndrome; Behavioral risk factors

Abstract

Introduction: The aim of this study was to explore latent classes of risk factors among patients with acute coronary syndrome.

Methods: A cross-sectional study was performed on patients with symptoms of chest pain, unstable angina, or myocardial infarction who had at least one coronary vascular involvement confirmed by angiography. A latent class analysis (LCA) using five categorical risk factors, including metabolic syndrome, physical activity, tobacco use, alcohol, and opium consumption, was conducted on 380 eligible patients. A logistic regression model was used to explore the associations of demographic and clinical variables with latent classes.

Results: The mean age of the patients was 59.05 years (SD= 9.82). A two-class model showed the best fit; Class I (45.1%) was characterized by a high probability of smoking, alcohol, and opium consumption, and Class II was characterized by a high probability of metabolic syndrome (54.9%). There was a significant difference between the two classes in terms of age, sex, job, and educational status. The multiple logistic regression model revealed that age and sex were independent predictors of latent class membership.

Conclusion: This study revealed two distinct latent risk factor patterns among ACS patients emphasizing the need for personalized prevention approaches. Behavioral interventions should be prioritized in younger patients. While, sex-specific metabolic syndrome management strategies should be underscored in older patients