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Identifying Latent Classes of Risk Factors for Coronary Artery Disease
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Eunsil Ju, JiSun Choi
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J Korean Acad Nurs 2017;47(6):817-827. Published online January 15, 2017
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DOI: https://doi.org/10.4040/jkan.2017.47.6.817
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 Abstract
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- 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.
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Citations
Citations to this article as recorded by  - Usefulness Evaluation of Laplacian Filter and Fast Non-Local Means Algorithm in Coronary Angiographic Images
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