Korean Circ J
Division of Cardiovascular and Rare Diseases, Korea National Institute of Health, Cheongju, Korea.
Published: September 2016
Background And Objectives: Heart failure (HF) is an important healthcare issue because of its high mortality, morbidity, and healthcare costs. The number of HF patients is increasing worldwide as a consequence of aging of the population. However, there are limited studies on the prevalence of HF in Korea. This study aimed to estimate the prevalence of HF, its comorbidities, and the projected population with HF in the future.
Materials And Methods: The prevalence and comorbidity estimates of HF were determined using data from the 2002-2013 National Sample Cohort based on the National Health Information Database. We calculated the projected prevalence of HF by multiplying the estimated prevalence in 2013 by the projected population counts for 2015-2040.
Results: The prevalence of HF in Korea was estimated to be 1.53% in 2013. The prevalence of HF in Korea is expected to increase by 2-fold, from 1.60% in 2015 to 3.35% in 2040. By 2040, more than 1.7 million Koreans are expected to have HF. In terms of comorbid diseases of HF, ischemic heart disease, hypertension, and diabetes mellitus were common (45.4%, 43.6%, and 49.1% in 2013, respectively). The prevalence rates of arrhythmia, valvular disease, and cardiomyopathy in HF patients were approximately 22.6%, 5.6%, and 3.1% in 2013, respectively.
Conclusion: This is the first nationwide report in Korea to demonstrate the prevalence and comorbidities of HF. These data may be used for the prevention and management of HF in Korea.
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http://dx.doi.org/10.4070/kcj.2016.46.5.658 | DOI Listing |
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College of Nursing, Yonsei University, Seoul, South Korea; Mo-Im Kim Nursing Research Institute, College of Nursing, Yonsei University, Seoul, South Korea. Electronic address:
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Am J Ophthalmol
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Keye Eye Center, Seoul, Republic of Korea. Electronic address:
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Artif Intell Med
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Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland. Electronic address:
Effective representation of medical concepts is crucial for secondary analyses of electronic health records. Neural language models have shown promise in automatically deriving medical concept representations from clinical data. However, the comparative performance of different language models for creating these empirical representations, and the extent to which they encode medical semantics, has not been extensively studied.
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Department of Physical Education, Dong-A University, Busan, South Korea. Electronic address:
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