Literature DB >> 25351481

Model for assessing cardiovascular risk in a Korean population.

Gyung-Min Park1, Seungbong Han1, Seon Ha Kim1, Min-Woo Jo1, Sung Ho Her1, Jung Bok Lee1, Moo Song Lee1, Hyeon Chang Kim1, Jung-Min Ahn1, Seung-Whan Lee1, Young-Hak Kim2, Beom-Jun Kim1, Jung-Min Koh1, Hong-Kyu Kim1, Jaewon Choe2, Seong-Wook Park1, Seung-Jung Park1.   

Abstract

BACKGROUND: A model for predicting cardiovascular disease in Asian populations is limited. METHODS AND
RESULTS: In total, 57 393 consecutive asymptomatic Korean individuals aged 30 to 80 years without a prior history of cardiovascular disease who underwent a general health examination were enrolled. Subjects were randomly classified into the train (n=45 914) and validation (n=11 479) cohorts. Thirty-one possible risk factors were assessed. The cardiovascular event was a composite of cardiovascular death, myocardial infarction, and stroke. In the train cohort, the C-index (95% confidence interval) and Akaike Information Criterion were used to develop the best-fitting prediction model. In the validation cohort, the predicted versus the observed cardiovascular event rates were compared by the C-index and Nam and D'Agostino χ(2) statistics. During a median follow-up period of 3.1 (interquartile range, 1.9-4.3) years, 458 subjects had 474 cardiovascular events. In the train cohort, the best-fitting model consisted of age, diabetes mellitus, hypertension, current smoking, family history of coronary heart disease, white blood cell, creatinine, glycohemoglobin, atrial fibrillation, blood pressure, and cholesterol (C-index =0.757 [0.726-0.788] and Akaike Information Criterion =7207). When this model was tested in the validation cohort, it performed well in terms of discrimination and calibration abilities (C-index=0.760 [0.693-0.828] and Nam and D'Agostino χ(2) statistic =0.001 for 3 years; C-index=0.782 [0.719-0.846] and Nam and D'Agostino χ(2) statistic=1.037 for 5 years).
CONCLUSIONS: A risk model based on traditional clinical and biomarkers has a feasible model performance in predicting cardiovascular events in an asymptomatic Korean population.
© 2014 American Heart Association, Inc.

Entities:  

Keywords:  cardiovascular diseases; coronary disease; prevention and control

Mesh:

Year:  2014        PMID: 25351481     DOI: 10.1161/CIRCOUTCOMES.114.001305

Source DB:  PubMed          Journal:  Circ Cardiovasc Qual Outcomes        ISSN: 1941-7713


  20 in total

1.  Current State of Cardiovascular Research in Korea.

Authors:  Jae Il Shin; Jaewon Oh; Hyeon Chang Kim; Donghoon Choi; Young-Sup Yoon
Journal:  Circ Res       Date:  2019-12-05       Impact factor: 17.367

2.  Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis.

Authors:  Bharath Ambale-Venkatesh; Xiaoying Yang; Colin O Wu; Kiang Liu; W Gregory Hundley; Robyn McClelland; Antoinette S Gomes; Aaron R Folsom; Steven Shea; Eliseo Guallar; David A Bluemke; João A C Lima
Journal:  Circ Res       Date:  2017-08-09       Impact factor: 17.367

Review 3.  Model for Predicting Cardiovascular Disease: Insights from a Korean Cardiovascular Risk Model.

Authors:  Gyung-Min Park; Young-Hak Kim
Journal:  Pulse (Basel)       Date:  2015-08-26

4.  Clinical impact and cost-effectiveness of coronary computed tomography angiography or exercise electrocardiogram in individuals without known cardiovascular disease.

Authors:  Gyung-Min Park; Seon Ha Kim; Min-Woo Jo; Sung Ho Her; Seungbong Han; Jung-Min Ahn; Duk-Woo Park; Soo-Jin Kang; Seung-Whan Lee; Young-Hak Kim; Cheol Whan Lee; Beom-Jun Kim; Jung-Min Koh; Hong-Kyu Kim; Jaewon Choe; Seong-Wook Park; Seung-Jung Park
Journal:  Medicine (Baltimore)       Date:  2015-05       Impact factor: 1.889

5.  Trends, Characteristics, and Clinical Outcomes of Patients Undergoing Percutaneous Coronary Intervention in Korea between 2011 and 2015.

Authors:  Seungbong Han; Gyung Min Park; Yong Giun Kim; Mahn Won Park; Sung Ho Her; Seung Whan Lee; Young Hak Kim
Journal:  Korean Circ J       Date:  2018-04       Impact factor: 3.243

6.  A New Prognostic Tool for Korean Patients with Acute Myocardial Infarction.

Authors:  Hyeon Chang Kim
Journal:  Korean Circ J       Date:  2018-06       Impact factor: 3.243

7.  Impact of Diabetes Control on Subclinical Atherosclerosis: Analysis from Coronary Computed Tomographic Angiography Registry.

Authors:  Gyung Min Park; Chang Hoon Lee; Seung Whan Lee; Sung Cheol Yun; Young Hak Kim; Yong Giun Kim; Ki Bum Won; Soe Hee Ann; Shin Jae Kim; Dong Hyun Yang; Joon Won Kang; Tae Hwan Lim; Eun Hee Koh; Woo Je Lee; Min Seon Kim; Joong Yeol Park; Hong Kyu Kim; Jaewon Choe; Sang Gon Lee
Journal:  Diabetes Metab J       Date:  2019-11-22       Impact factor: 5.376

8.  Moderate-intensity versus high-intensity statin therapy in Korean patients with angina undergoing percutaneous coronary intervention with drug-eluting stents: A propensity-score matching analysis.

Authors:  Mahn-Won Park; Gyung-Min Park; Seungbong Han; Yujin Yang; Yong-Giun Kim; Jae-Hyung Roh; Hyun Woo Park; Jon Suh; Young-Rak Cho; Ki-Bum Won; Soe Hee Ann; Shin-Jae Kim; Dae-Won Kim; Sung Ho Her; Sang-Gon Lee
Journal:  PLoS One       Date:  2018-12-07       Impact factor: 3.240

9.  Impact of diabetes mellitus in patients undergoing contemporary percutaneous coronary intervention: Results from a Korean nationwide study.

Authors:  Yujin Yang; Gyung-Min Park; Seungbong Han; Yong-Giun Kim; Jon Suh; Hyun Woo Park; Ki-Bum Won; Soe Hee Ann; Shin-Jae Kim; Dae-Won Kim; Mahn-Won Park; Sung Ho Her; Sang-Gon Lee
Journal:  PLoS One       Date:  2018-12-10       Impact factor: 3.240

10.  Homocysteine is not a risk factor for subclinical coronary atherosclerosis in asymptomatic individuals.

Authors:  Sangwoo Park; Gyung-Min Park; Jinhee Ha; Young-Rak Cho; Jae-Hyung Roh; Eun Ji Park; Yujin Yang; Ki-Bum Won; Soe Hee Ann; Yong-Giun Kim; Shin-Jae Kim; Sang-Gon Lee; Dong Hyun Yang; Joon-Won Kang; Tae-Hwan Lim; Hong-Kyu Kim; Jaewon Choe; Seung-Whan Lee; Young-Hak Kim
Journal:  PLoS One       Date:  2020-04-08       Impact factor: 3.240

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