Fan Wenjun, Wu Chuyue, Wong Nathan D
Mary and Steve Wen Cardiovascular Division, Department of Medicine (W.F., N.D.W.), University of California, Irvine.
Department of Epidemiology and Biostatistics (W.F., N.D.W.), University of California, Irvine.
Circ Genom Precis Med. 2025 Feb;18(1):e004631. doi: 10.1161/CIRCGEN.124.004631. Epub 2025 Jan 24.
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).
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.
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%.
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.
脂蛋白(a)[Lp(a)]是动脉粥样硬化性心血管疾病(ASCVD)的一个预测指标；然而，纳入Lp(a)的算法很少，尤其是来自真实世界的数据。我们开发了一种基于电子健康记录(EHR)的风险预测算法，其中包括Lp(a)。
利用一个大型电子健康记录数据库，我们将Lp(a)切点分为25、50和75mg/dL，并构建了包含Lp(a)的10年ASCVD风险预测模型，并在美国4项前瞻性研究的汇总队列中进行了外部验证。在临界-中度风险患者中确定净重新分类改善情况。
我们纳入了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)。我们的电子健康记录模型包括Lp(a)、年龄、性别、黑人种族/族裔、收缩压、总胆固醇和高密度脂蛋白胆固醇、糖尿病、吸烟和高血压用药情况。在平均6.8年的随访中，各Lp(a)组的ASCVD事件发生率(每1000人年)在8.7至16.7之间。Lp(a)每增加25mg/dL，复合ASCVD的校正风险比为1.23(95%CI，1.10-1.37)。Lp(a)≥75mg/dL的患者发生ASCVD的风险高88%(风险比，1.88[95%CI，1.30-2.70])，发生中风的风险增加一倍多(风险比，2.55[95%CI，1.54-4.23])。在我们的电子健康记录训练数据集中，我们的电子健康记录模型和电子健康记录+Lp(a)模型的C统计量分别为0.7475和0.7556，在我们的汇总队列(n=21 864)中的外部验证分别为0.7350和0.73真68。在临界/中度风险患者中，净重新分类改善率为21.3%。
我们证明了基于真实世界的成人门诊人群开发一种纳入Lp(a)的改进的ASCVD风险预测模型的可行性。在ASCVD预测模型中纳入Lp(a)可以对可能从更强化的ASCVD预防措施中获益的患者进行风险重新分类。
