External validation of SCORE2-Diabetes in the Netherlands across various Socioeconomic levels in native-Dutch and non-Dutch populations.

IF 8.4 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Sukainah A Alfaraj, Janet M Kist, Rolf H H Groenwold, Marco Spruit, Dennis Mook-Kanamori, Rimke C Vos
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引用次数: 0

Abstract

Aims: Adults with type 2 diabetes have an increased risk of cardiovascular events (CVE), the world's leading cause of mortality. The SCORE2-Diabetes model is a tool designed to estimate the 10-year risk of CVE specifically in individuals with type 2 diabetes. However, the performance of such models may vary across different demographic and socioeconomic groups, necessitating validation and assessment in diverse populations. This study aims to externally validate SCORE2-Diabetes and assess its performance across various socioeconomic and migration origins in the Netherlands.

Methods: We selected adults with type 2 diabetes, aged 40-79 years and without previous CVE from the Extramural LUMC Academic Network (ELAN) primary care data cohort from 2007 to 2023. ELAN data were linked with Statistics Netherlands registry data to obtain information about the country of origin and socioeconomic status (SES). CVE was defined as myocardial infarction, stroke, or CV mortality. Non-CV mortality was considered a competing event. Analyses were stratified by sex, Dutch versus other non-Dutch countries of origin, and quintiles of SES.

Results: Of the 26,544 included adults with type 2 diabetes, 2,518 developed CVE. SCORE2-Diabetes showed strong predictive accuracy for CVE in the Dutch population (observed-to-expected ratio (OE)=1.000, 95% CI=0.990-1.008 for men, and OE=1.050, 95% CI=1.042-1.057 for women). For non-Dutch individuals, the model underestimated CVE risk (OE=1.121, 95% CI=1.108-1.131 for men, and OE=1.100, 95% CI=1.092-1.111 for women). The model also underestimated the CVE risk (OE>1) in low SES groups and overestimated the risk (OE<1) in high SES groups. Discrimination was moderate across subgroups with c-indices between 0.6 and 0.7.

Conclusions: SCORE2-Diabetes accurately predicted the risk of CVE in the Dutch population. However, it underpredicted the risk of CVE in the low SES groups and non-Dutch origins, while overpredicting the risk in high SES men and women. Additional clinical judgment must be considered when using SCORE2-Diabetes for different SES and countries of origin.

SCORE2-Diabetes 在荷兰不同社会经济水平的本土荷兰人和非荷兰人中的外部验证。
目的:成人 2 型糖尿病患者发生心血管事件 (CVE) 的风险增加,而心血管事件是世界上导致死亡的主要原因。SCORE2-Diabetes 模型是一种专门用于估算 2 型糖尿病患者 10 年 CVE 风险的工具。然而,在不同的人口和社会经济群体中,此类模型的性能可能会有所不同,因此有必要在不同的人群中进行验证和评估。本研究旨在对 SCORE2-Diabetes 进行外部验证,并评估其在荷兰不同社会经济和移民来源人群中的表现:我们从校外 LUMC 学术网络(ELAN)2007 年至 2023 年的初级保健数据队列中选取了年龄在 40-79 岁之间、既往无 CVE 的 2 型糖尿病成人患者。ELAN数据与荷兰统计局的登记数据相链接,以获得有关原籍国和社会经济地位(SES)的信息。CVE定义为心肌梗死、中风或CV死亡率。非 CV 死亡率被视为竞争事件。按照性别、荷兰原籍国与其他非荷兰原籍国以及社会经济地位五分位数进行了分层分析:结果:在纳入的 26,544 名 2 型糖尿病成人患者中,有 2,518 人罹患 CVE。在荷兰人群中,SCORE2-Diabetes 对 CVE 的预测准确性很高(男性的观察值与预期值之比 (OE)=1.000, 95% CI=0.990-1.008; 女性的观察值与预期值之比 (OE)=1.050, 95% CI=1.042-1.057 )。对于非荷兰人,该模型低估了CVE风险(男性OE=1.121,95% CI=1.108-1.131;女性OE=1.100,95% CI=1.092-1.111)。该模型还低估了低社会经济地位群体的 CVE 风险(OE>1),并高估了风险(OEC结论:SCORE2-Diabetes 可准确预测荷兰人口的 CVE 风险。然而,它低估了低社会经济地位群体和非荷兰裔人群的 CVE 风险,而高估了高社会经济地位男性和女性的 CVE 风险。在针对不同的社会经济地位和原籍国使用 SCORE2-Diabetes 时,必须考虑额外的临床判断。
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来源期刊
European journal of preventive cardiology
European journal of preventive cardiology CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
12.50
自引率
12.00%
发文量
601
审稿时长
3-8 weeks
期刊介绍: European Journal of Preventive Cardiology (EJPC) is an official journal of the European Society of Cardiology (ESC) and the European Association of Preventive Cardiology (EAPC). The journal covers a wide range of scientific, clinical, and public health disciplines related to cardiovascular disease prevention, risk factor management, cardiovascular rehabilitation, population science and public health, and exercise physiology. The categories covered by the journal include classical risk factors and treatment, lifestyle risk factors, non-modifiable cardiovascular risk factors, cardiovascular conditions, concomitant pathological conditions, sport cardiology, diagnostic tests, care settings, epidemiology, pharmacology and pharmacotherapy, machine learning, and artificial intelligence.
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