Prediction of the 10-year risk of atherosclerotic cardiovascular disease in the Korean population
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OBJECTIVES: Proper risk assessment is important for the primary prevention of atherosclerotic cardiovascular disease (ASCVD). However, no validated risk prediction tools are currently in use in Korea. This study sought to develop a 10-year risk prediction model for incident ASCVD. METHODS: Using the National Sample Cohort of Korea, 325,934 subjects aged 20-80 years without previous ASCVD were enrolled. ASCVD was defined as a composite of cardiovascular death, myocardial infarction, and stroke. The Korean atherosclerotic cardiovas cular disease risk prediction (K-CVD) model was developed separately for men and women using the development dataset and validated in the validation dataset. Furthermore, the model performance was compared with the Framingham risk score (FRS) and pooled cohort equation (PCE). RESULTS: Over 10 years of follow-up, 4,367 ASCVD events occurred in the overall population. The predictors of ASCVD included in the model were age, smoking status, diabetes, systolic blood pressure, lipid profiles, urine protein, and lipid-lowering and blood pressure-lowering treatment. The K-CVD model had good discrimination and strong calibration in the validation dataset (time-dependent area under the curve=0.846; 95% confidence interval, 0.828 to 0.864; calibration χ2=4.73, goodness-of-fit p=0.32). Compared with our model, both FRS and PCE showed worse calibration, overestimating ASCVD risk in the Korean population. CONCLUSIONS: Through a nationwide cohort, we developed a model for 10-year ASCVD risk prediction in a contemporary Korean population. The K-CVD model showed excellent discrimination and calibration in Koreans. This population-based risk prediction tool would help to appropriately identify high-risk individuals and provide preventive interventions in the Korean population.
Abstract
OBJECTIVES: Proper risk assessment is important for the primary prevention of atherosclerotic cardiovascular disease (ASCVD). However, no validated risk prediction tools are currently in use in Korea. This study sought to develop a 10-year risk prediction model for incident ASCVD. METHODS: Using the National Sample Cohort of Korea, 325,934 subjects aged 20-80 years without previous ASCVD were enrolled. ASCVD was defined as a composite of cardiovascular death, myocardial infarction, and stroke. The Korean atherosclerotic cardiovas cular disease risk prediction (K-CVD) model was developed separately for men and women using the development dataset and validated in the validation dataset. Furthermore, the model performance was compared with the Framingham risk score (FRS) and pooled cohort equation (PCE). RESULTS: Over 10 years of follow-up, 4,367 ASCVD events occurred in the overall population. The predictors of ASCVD included in the model were age, smoking status, diabetes, systolic blood pressure, lipid profiles, urine protein, and lipid-lowering and blood pressure-lowering treatment. The K-CVD model had good discrimination and strong calibration in the validation dataset (time-dependent area under the curve=0.846; 95% confidence interval, 0.828 to 0.864; calibration χ2=4.73, goodness-of-fit p=0.32). Compared with our model, both FRS and PCE showed worse calibration, overestimating ASCVD risk in the Korean population. CONCLUSIONS: Through a nationwide cohort, we developed a model for 10-year ASCVD risk prediction in a contemporary Korean population. The K-CVD model showed excellent discrimination and calibration in Koreans. This population-based risk prediction tool would help to appropriately identify high-risk individuals and provide preventive interventions in the Korean population.
참고문헌 (30)
- Ohira T, "Cardiovascular disease epidemiology in Asia : an overview", Circ J, 77, pp.1646~1652 (2013)
- Lloyd-Jones DM, "Cardiovascular risk prediction : basic concepts, current status, and future directions", Circulation, 121, pp.1768~1777 (2010)
- Visseren FL, "2021 ESC Guidelines on cardiovascular disease prevention in clinical practice", Eur Heart J, 42, pp.3227~3337 (2021)
- Arnett DK, "2019 ACC", AHA guideline on the primary prevention of cardiovascular disease : a report of the American College of Cardiology, Circulation, pp.2019, 140[DOI]
- D’Agostino RB Sr, "General cardiovascular risk profile for use in primary care : the Framingham Heart Study", Circulation, 117, pp.743~753 (2008)
- Goff DC Jr, "2013 ACC", AHA guideline on the assessment of cardiovascular risk : a report of the American College of Cardiology, Circulation, pp.2014, 129(25 Suppl 2)[DOI]
- Conroy RM, "Estimation of ten-year risk of fatal cardiovascular disease in Europe : the SCORE project", Eur Heart J, 24, pp.987~1003 (2003)
- Kim HC, "Epidemiology of cardiovascular disease and its risk factors in Korea", Glob Health Med, 3, pp.134~141 (2021)
- Jung KJ, "The ACC", AHA 2013 pooled cohort equations compared to a Korean Risk Prediction Model for atherosclerotic cardiovascular disease, 2015, pp.242, 367~375
- Jee SH, "A coronary heart disease prediction model : the Korean Heart Study", BMJ Open, 4(5), pp.e005025~ (2014)
- Hae-Young Lee, "2018 Korean Society of Hypertension Guidelines for the management of hypertension: part II-diagnosis and treatment of hypertension", Clinical Hypertension, 25(4), pp.51~74 (2019), 대한고혈압학회[DOI]
- 이은정, "2018 Guidelines for the management of dyslipidemia", The Korean Journal of Internal Medicine, 34(4), pp.723~771 (2019), 대한내과학회[DOI]
- Lee J, "Cohort profile : the National Health Insurance Service-National Sample Cohort(NHIS-NSC), South Korea", Int J Epidemiol, 46, pp.e15~ (2017)
- Seong SC, "Cohort profile : the National Health Insurance Service-National Health Screening Cohort(NHIS-HEALS)in Korea", BMJ Open, 7, pp.e016640~ (2017)
- Park GM, "Model for assessing cardiovascular risk in a Korean population", Circ Cardiovasc Qual Outcomes, 7, pp.944~951 (2014)
- Breslow NE, "Analysis of survival data under the proportional hazards model", Int Stat Rev, 43, pp.45~57 (1975)
- Demler OV, "Tests of calibration and goodness-of-fit in the survival setting", Stat Med, 34, pp.1659~1680 (2015)
- Mogensen UB, "Evaluating random forests for survival analysis using prediction error curves", J Stat Softw, 50, pp.1~23 (2012)
- Moons KG, "Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD) : explanation and elaboration", Ann Intern Med, 162, pp.W1~W73 (2015)
- DeFilippis AP, "Risk score overestimation : the impact of individual cardiovascular risk factors and preventive therapies on the performance of the American Heart Association-American College of Cardiology-Atherosclerotic Cardiovascular Disease risk score in a modern multi-ethnic cohort", Eur Heart J, 38, pp.598~608 (2017)
- Pylypchuk R, "Cardiovascular risk prediction in type 2 diabetes before and after widespread screening : a derivation and validation study", Lancet, 397, pp.2264~2274 (2021)
- Currie G, "Proteinuria and its relation to cardiovascular disease", Int J Nephrol Renovasc Dis, 7, pp.13~24 (2013)
- Manjunath G, "Level of kidney function as a risk factor for atherosclerotic cardiovascular outcomes in the community", J Am Coll Cardiol, 41, pp.47~55 (2003)
- Groenwold RH, "Explicit inclusion of treatment in prognostic modeling was recommended in observational and randomized settings", J Clin Epidemiol, 78, pp.90~100 (2016)
- Liew SM, "Cardiovascular risk scores do not account for the effect of treatment : a review", Heart, 97, pp.689~697 (2011)
- Damen JA, "Prediction models for cardiovascular disease risk in the general population : systematic review", BMJ, 353, pp.i2416~ (2016)
- Zhang Y, "Cardiovascular risk assessment tools in Asia", J Clin Hypertens(Greenwich), 24, pp.369~377 (2022)
- Jee SH, "Stroke risk prediction model : a risk profile from the Korean study", Atherosclerosis, 197, pp.318~325 (2008)
- 배재현, "Validation of Risk Prediction Models for Atherosclerotic Cardiovascular Disease in a Prospective Korean Community-Based Cohort", Diabetes and Metabolism Journal, 44(3), pp.458~469 (2020), 대한당뇨병학회
- Son JS, "Association of blood pressure classification in Korean young adults according to the 2017 American College of Cardiology", American Heart Association Guidelines with subsequent cardiovascular disease events, 2018, pp.320, 1783~1792