J Korean Acad Nurs.  2017 Dec;47(6):817-827. 10.4040/jkan.2017.47.6.817.

Identifying Latent Classes of Risk Factors for Coronary Artery Disease

• Ju E
• Choi J

• KMID: 2408147
• DOI: http://doi.org/10.4040/jkan.2017.47.6.817

Abstract

PURPOSE
This study aimed to identify latent classes based on major modifiable risk factors for coronary artery disease.
METHODS
This was a secondary analysis using data from the electronic medical records of 2,022 patients, who were newly diagnosed with coronary artery disease at a university medical center, from January 2010 to December 2015. Data were analyzed using SPSS version 20.0 for descriptive analysis and Mplus version 7.4 for latent class analysis.
RESULTS
Four latent classes of risk factors for coronary artery disease were identified in the final model: "˜smoking-drinking', "˜high-risk for dyslipidemia', "˜high-risk for metabolic syndrome', and "˜high-risk for diabetes and malnutrition'. The likelihood of these latent classes varied significantly based on socio-demographic characteristics, including age, gender, educational level, and occupation.
CONCLUSION
The results showed significant heterogeneity in the pattern of risk factors for coronary artery disease. These findings provide helpful data to develop intervention strategies for the effective prevention of coronary artery disease. Specific characteristics depending on the subpopulation should be considered during the development of interventions.

Keyword

Coronary artery disease; Dyslipidemia; Metabolic syndrome; Risk factors; Statistical models

MeSH Terms

Academic Medical Centers
Coronary Artery Disease*
Coronary Vessels*
Dyslipidemias
Electronic Health Records
Humans
Models, Statistical
Occupations
Population Characteristics
Risk Factors*

Figure

• 

  Figure 1. Class membership probability of latent classes.

Reference

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