Cardiovascular risk prediction: Can Systematic Coronary Risk Evaluation (SCORE) be improved by adding simple risk markers? Results from the Copenhagen City Heart Study

TY - JOUR

T1 - Cardiovascular risk prediction

T2 - Can Systematic Coronary Risk Evaluation (SCORE) be improved by adding simple risk markers? Results from the Copenhagen City Heart Study

AU - Graversen, Peter

AU - Abildstrøm, Steen Z.

AU - Jespersen, Lasse

AU - Borglykke, Anders

AU - Prescott, Eva

PY - 2016

Y1 - 2016

N2 - Aim European society of cardiology (ESC) guidelines recommend that cardiovascular disease (CVD) risk stratification in asymptomatic individuals is based on the Systematic Coronary Risk Evaluation (SCORE) algorithm, which estimates individual 10-year risk of death from CVD. We assessed the potential improvement in CVD risk stratification of 19 easily available risk markers by adding them to the SCORE algorithm. Methods and results We followed 8476 individuals without prior CVD or diabetes from the Copenhagen City Heart study. The 19 risk markers were: major and minor electrocardiographic (ECG) abnormalities, heart rate, family history (of ischaemic heart disease), body mass index (BMI), waist-hip ratio, walking duration and pace, leisure time physical activity, forced expiratory volume (FEV)1%pred, household income, education, vital exhaustion, high-density lipoprotein (HDL) cholesterol, triglycerides, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), high-sensitive C-reactive protein (hsCRP) and fibrinogen. With the exception of family history, BMI, triglycerides and minor ECG changes, all risk markers remained significantly associated with CVD mortality after adjustment for SCORE variables. However, the addition of the remaining 15 risk markers resulted in only small changes in discrimination calculated by area under the curve (AUC) and integrated discrimination improvement (IDI) and no improvement in net reclassification improvement (NRI). HsCRP improved AUC by 0.006 (p = 0.015) and IDI by 0.012 (p = 0.002); FEV1%pred improved AUC by 0.006 (p = 0.032) and IDI by 0.006 (p = 0.029). In the intermediate risk group FEV1%pred, education, vital exhaustion and ApoA1 all improved NRI but FEV1%pred was the only risk marker to significantly improve both IDI, AUC and NRI. Conclusion The SCORE algorithm predicted CVD mortality in a Danish cohort well. Despite strong association with CVD mortality, the individual addition of 19 easily available risk makers to the SCORE model resulted in small risk stratification improvements.

AB - Aim European society of cardiology (ESC) guidelines recommend that cardiovascular disease (CVD) risk stratification in asymptomatic individuals is based on the Systematic Coronary Risk Evaluation (SCORE) algorithm, which estimates individual 10-year risk of death from CVD. We assessed the potential improvement in CVD risk stratification of 19 easily available risk markers by adding them to the SCORE algorithm. Methods and results We followed 8476 individuals without prior CVD or diabetes from the Copenhagen City Heart study. The 19 risk markers were: major and minor electrocardiographic (ECG) abnormalities, heart rate, family history (of ischaemic heart disease), body mass index (BMI), waist-hip ratio, walking duration and pace, leisure time physical activity, forced expiratory volume (FEV)1%pred, household income, education, vital exhaustion, high-density lipoprotein (HDL) cholesterol, triglycerides, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), high-sensitive C-reactive protein (hsCRP) and fibrinogen. With the exception of family history, BMI, triglycerides and minor ECG changes, all risk markers remained significantly associated with CVD mortality after adjustment for SCORE variables. However, the addition of the remaining 15 risk markers resulted in only small changes in discrimination calculated by area under the curve (AUC) and integrated discrimination improvement (IDI) and no improvement in net reclassification improvement (NRI). HsCRP improved AUC by 0.006 (p = 0.015) and IDI by 0.012 (p = 0.002); FEV1%pred improved AUC by 0.006 (p = 0.032) and IDI by 0.006 (p = 0.029). In the intermediate risk group FEV1%pred, education, vital exhaustion and ApoA1 all improved NRI but FEV1%pred was the only risk marker to significantly improve both IDI, AUC and NRI. Conclusion The SCORE algorithm predicted CVD mortality in a Danish cohort well. Despite strong association with CVD mortality, the individual addition of 19 easily available risk makers to the SCORE model resulted in small risk stratification improvements.

KW - Cardiovascular disease

KW - discrimination

KW - risk markers

KW - risk stratification

U2 - 10.1177/2047487316638201

DO - 10.1177/2047487316638201

M3 - Journal article

C2 - 26976846

AN - SCOPUS:84984905365

SN - 2047-4873

VL - 23

SP - 1546

EP - 1556

JO - European Journal of Preventive Cardiology

JF - European Journal of Preventive Cardiology

IS - 14

ER -