使用基于西班牙人群的CARDIANA队列对2型糖尿病患者心血管风险评分进行外部验证

IF 8.4 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Mónica Enguita-Germán, Asier Ballesteros-Domínguez, Ibai Tamayo, Julián Librero, Ignacio Oscoz-Villanueva, Lluis Forga, Maria José Goñi-Iriarte, Javier Lafita, Oscar Lecea, Naiara Parraza, Berta Ibáñez-Beroiz
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引用次数: 0

摘要

目的:目前适用于2型糖尿病(T2D)患者的心血管疾病(CVD)风险预测模型过多,但大多数模型仍需要外部验证。我们的目的是评估西班牙T2D患者队列中18项CVD风险评分的表现。方法与结果:纳瓦拉(CARDIANA)队列中糖尿病患者的心血管风险,包括20793例T2D患者,无CVD病史,用于外部验证在T2D患者中开发的13个模型[糖尿病和血管疾病的作用(ADVANCE),社区动脉粥样硬化风险,巴斯克地区前瞻性并发症和死亡率研究风险引擎,心血管健康研究,糖尿病队列研究,DIAL2, DIAL2扩展,Fremantle, Kaasenbrood,瑞典国家糖尿病登记(NDR)、PREDICT1-diabetes、SCORE2- Diabetes和Wan]和在普通人群中开发的5种模型(ASCVD、prevention -basic、prevention -full、QRISK2和SCORE2)。采用Harrell’sc -统计量和校正图分别作为判别和校正措施。随访5年共发生991例CVD事件,累计发病率为5.0%(95%可信区间4.7-5.3)。所有模型的判别能力均为中等,其中SCORE2-diabetes、NDR、PREDICT1-diabetes、prevention -full、Wan、ADVANCE和两个DIAL2模型的c指数值最高。除了ADVANCE-、DIAL2-和score2相关的模型外,所有模型都显示出良好的校准,尽管大多数模型需要重新校准。结论:在我们的研究背景下,为糖尿病患者衍生或适应的模型,以及在普通人群中衍生但纳入糖尿病相关代谢指标(如Hb1Ac)作为预测指标的模型,表现出比其他模型更好的性能。DIAL2、DIAL2-extended、SCORE2-diabetes和ADVANCE在无需重新校准的情况下也显示出最佳的校准效果,这意味着更大的适用性,特别是SCORE2-diabetes和ADVANCE,因为它们简单。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
External validation of cardiovascular risk scores in patients with Type 2 diabetes using the Spanish population-based CARDIANA cohort.

Aims: There is an overabundance of cardiovascular disease (CVD) risk-prediction models applicable to patients with Type 2 diabetes (T2D), but most of them still require external validation. Our aim was to assess the performance of 18 CVD risk scores in a Spanish cohort of patients with T2D.

Methods and results: The CARdiovascular Risk in patients with DIAbetes in Navarra (CARDIANA) cohort, which includes 20 793 individuals with T2D and no history of CVD, was used to externally validate 13 models developed in patients with T2D [Action in Diabetes and Vascular Disease (ADVANCE), Atherosclerosis Risk in Communities, Basque Country Prospective Complications and Mortality Study risk engine, Cardiovascular Healthy Study, Diabetes Cohort Study, DIAL2, DIAL2-extended, Fremantle, Kaasenbrood, Swedish National Diabetes Register (NDR), PREDICT1-diabetes, SCORE2-diabetes, and Wan] and 5 models developed in the general population (ASCVD, PREVENT-basic, PREVENT-full, QRISK2, and SCORE2). Harrell's C-statistic and calibration plots were used as measures of discrimination and calibration, respectively. There were 991 incident CVD events within 5 years of follow-up, resulting in a cumulative incidence of 5.0% (95% confidence interval 4.7-5.3). Discrimination ability was moderate for all the models, with SCORE2-diabetes, NDR, PREDICT1-diabetes, PREVENT-full, Wan, ADVANCE, and both DIAL2 models showing the highest C-index values. All models showed good calibration, although most of them required recalibration, with the exception of ADVANCE-, DIAL2-, and SCORE2-related models.

Conclusion: In our context, models derived for or adapted to diabetes patients, as well as models derived in the general population but incorporating diabetes-related metabolic measures (such as Hb1Ac) as predictors, demonstrated better performance than the others. DIAL2, DIAL2-extended, SCORE2-diabetes, and ADVANCE showed optimal calibration even without recalibration, which implies greater applicability, especially for SCORE2-diabetes and ADVANCE because of their simplicity.

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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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