脂蛋白(a)动脉粥样硬化性心血管疾病风险评分的发展和预测来自真实世界数据的一级预防。

IF 6 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Wenjun Fan, Chuyue Wu, Nathan D Wong
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引用次数: 0

摘要

背景:Lp (a;脂蛋白[a])是动脉粥样硬化性心血管疾病(ASCVD)的预测因子;然而,很少有算法结合Lp(a),特别是在现实世界中。我们开发了一种基于电子健康记录(EHR)的风险预测算法,包括Lp(a)。方法:利用大型电子病历数据库,我们将Lp(a)切割点分类为25、50和75 mg/dL,并构建了包含Lp(a)的10年ASCVD风险预测模型,并在4项美国前瞻性研究的合并队列中进行了外部验证。在临界-中等危险患者中确定净重分类改善。结果:我们纳入5902例年龄≥18岁的患者(平均年龄48.7±16.7岁,51.2%为女性,7.7%为黑人)。我们的EHR模型包括Lp(a)、年龄、性别、黑人种族/民族、收缩压、总脂蛋白和高密度脂蛋白胆固醇、糖尿病、吸烟和高血压药物。在平均6.8年的随访中,Lp(a)组的ASCVD事件发生率(每1000人-年)从8.7到16.7不等。对于复合ASCVD, Lp(A)每增加25 mg/dL,校正风险比为1.23 (95% CI, 1.10-1.37)。Lp(a)≥75 mg/dL的患者发生ASCVD的风险增加88%(风险比1.88 [95% CI, 1.30-2.70]),发生卒中的风险增加一倍以上(风险比2.55 [95% CI, 1.54-4.23])。在我们的EHR训练数据集中,我们的EHR和EHR+Lp(a)模型的c统计量分别为0.7475和0.7556,在我们的合并队列(n=21 864)中,外部验证分别为0.7350和0.7368。在处于临界/中等风险的患者中,净重分类改善率为21.3%。结论:我们展示了基于现实世界成人临床人群的Lp(a)改进ASCVD风险预测模型的可行性。将Lp(a)纳入ASCVD预测模型可以对患者的风险进行重新分类,这些患者可能从加强ASCVD预防工作中获益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lipoprotein(a) Atherosclerotic Cardiovascular Disease Risk Score Development and Prediction in Primary Prevention From Real-World Data.

Background: Lipoprotein(a) [Lp(a)] is a predictor of atherosclerotic cardiovascular disease (ASCVD); however, there are few algorithms incorporating Lp(a), especially from real-world settings. We developed an electronic health record (EHR)-based risk prediction algorithm including Lp(a).

Methods: Utilizing a large EHR database, we categorized Lp(a) cut points at 25, 50, and 75 mg/dL and constructed 10-year ASCVD risk prediction models incorporating Lp(a), with external validation in a pooled cohort of 4 US prospective studies. Net reclassification improvement was determined among borderline-intermediate risk patients.

Results: We included 5902 patients aged ≥18 years (mean age 48.7±16.7 years, 51.2% women, and 7.7% Black). Our EHR model included Lp(a), age, sex, Black race/ethnicity, systolic blood pressure, total and high-density lipoprotein cholesterol, diabetes, smoking, and hypertension medication. Over a mean follow-up of 6.8 years, ASCVD event rates (per 1000 person-years) ranged from 8.7 to 16.7 across Lp(a) groups. A 25 mg/dL increment in Lp(a) was associated with an adjusted hazard ratio of 1.23 (95% CI, 1.10-1.37) for composite ASCVD. Those with Lp(a) ≥75 mg/dL had an 88% higher risk of ASCVD (hazard ratio, 1.88 [95% CI, 1.30-2.70]) and more than double the risk of incident stroke (hazard ratio, 2.55 [95% CI, 1.54-4.23]). C-statistics for our EHR and EHR+Lp(a) models in our EHR training data set were 0.7475 and 0.7556, respectively, with external validation in our pooled cohort (n=21 864) of 0.7350 and 0.7368, respectively. Among those at borderline/intermediate risk, the net reclassification improvement was 21.3%.

Conclusions: We show the feasibility of developing an improved ASCVD risk prediction model incorporating Lp(a) based on a real-world adult clinic population. The inclusion of Lp(a) in ASCVD prediction models can reclassify risk in patients who may benefit from more intensified ASCVD prevention efforts.

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来源期刊
Circulation: Genomic and Precision Medicine
Circulation: Genomic and Precision Medicine Biochemistry, Genetics and Molecular Biology-Genetics
CiteScore
9.20
自引率
5.40%
发文量
144
期刊介绍: Circulation: Genomic and Precision Medicine is a distinguished journal dedicated to advancing the frontiers of cardiovascular genomics and precision medicine. It publishes a diverse array of original research articles that delve into the genetic and molecular underpinnings of cardiovascular diseases. The journal's scope is broad, encompassing studies from human subjects to laboratory models, and from in vitro experiments to computational simulations. Circulation: Genomic and Precision Medicine is committed to publishing studies that have direct relevance to human cardiovascular biology and disease, with the ultimate goal of improving patient care and outcomes. The journal serves as a platform for researchers to share their groundbreaking work, fostering collaboration and innovation in the field of cardiovascular genomics and precision medicine.
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