Prediction of 10-Year Cardiovascular Disease Risk, by Diabetes status and Lipoprotein-a levels; the HellenicSCORE II+

医学 置信区间 内科学 糖尿病 逻辑回归 人口 血压 优势比 体质指数 人口学 内分泌学 环境卫生 社会学
作者
Demosthenes B. Panagiotakos,Christina Chrysohoou,Christos Pitsavos,Konstantinos Tsioufis
出处
期刊:Hellenic Journal of Cardiology [Elsevier BV]
卷期号:79: 3-14 被引量:4
标识
DOI:10.1016/j.hjc.2023.10.001
摘要

The aim of this study was to develop an updated model to predict10-year cardiovascular disease (CVD) risk for Greek adults, i.e., the HellenicSCORE II+, based on smoking, systolic blood pressure (SBP), total and High-Density-Lipoprotein-(HDL) cholesterol levels, and stratified by age group, sex, history of diabetes, and Lipoprotein (Lp)-a levels. Individual CVD risk scores were calculated through logit-function models, using the beta-coefficients derived from SCORE2. The Attica Study data were used for the calibration (3,042 participants, aged 45(14) years; 49.1% men). Discrimination ability of the HellenicSCORE II+ was assessed using C-index (range 0-1), adjusted for competing risks. The mean HellenicSCORE II+ score was 6.3% (95% Confidence Interval (CI) 5.9% to 6.6%) for men and 3.7% (95% CI 3.5% to 4.0%) for women (p<0.001), and were higher compared to the relevant SCORE2; 23.5% of men were classified as low risk, 40.2% as moderate and 36.3% as high risk, whereas the corresponding percentages for women were 56.2%, 18.6% and 25.2%. C-statistic index was 0.88 for women and 0.79 for men, when the HellenicSCORE II+ was applied to the ATTICA Study data, suggesting very good accuracy. Stratified analysis by Lp(a) levels led to a 4% improvement in correct classification among participants with high Lp(a). HellenicSCORE II+ values were higher than SCORE2, confirming that the Greek population is at moderate-to-high CVD risk. Stratification by Lp(a) levels may assist to better identify individuals at high CVD risk.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
WANG发布了新的文献求助10
1秒前
王哈哈完成签到,获得积分10
1秒前
1秒前
pantio完成签到,获得积分10
1秒前
深情安青应助香蕉幻桃采纳,获得10
2秒前
英姑应助lraqis采纳,获得10
2秒前
2秒前
南风知我意完成签到,获得积分0
3秒前
joe55667788发布了新的文献求助10
3秒前
乐乐应助开心的大船采纳,获得10
4秒前
王哈哈发布了新的文献求助10
4秒前
anffortan发布了新的文献求助10
5秒前
5秒前
galaxy发布了新的文献求助10
5秒前
6秒前
jimandbob完成签到,获得积分10
6秒前
CipherSage应助LYDZ2采纳,获得10
6秒前
会飞的猪发布了新的文献求助10
6秒前
7秒前
11秒前
hyperion完成签到,获得积分10
11秒前
12秒前
see发布了新的文献求助10
12秒前
14秒前
天然发布了新的文献求助10
15秒前
贪玩定帮发布了新的文献求助10
15秒前
unique444完成签到 ,获得积分10
16秒前
momo完成签到,获得积分10
16秒前
lw发布了新的文献求助30
16秒前
16秒前
HSTrigger应助WANG采纳,获得10
18秒前
van完成签到,获得积分20
18秒前
19秒前
元元圈圈完成签到 ,获得积分10
21秒前
彭于晏应助亓亓采纳,获得30
21秒前
White Night发布了新的文献求助30
21秒前
兴奋泽娴完成签到,获得积分10
22秒前
慕青应助林少玮采纳,获得10
22秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7584040
求助须知:如何正确求助?哪些是违规求助? 9162784
关于积分的说明 19608034
捐赠科研通 7165941
什么是DOI,文献DOI怎么找? 3266349
关于科研通互助平台的介绍 2431328
邀请新用户注册赠送积分活动 2257917