1. LIPOPROTEIN(A) ATHEROSCLEROTIC CARDIOVASCULAR DISEASE RISK SCORE DEVELOPMENT AND PREDICTION IN PRIMARY PREVENTION FROM REAL-WORLD DATA
- Author
-
Wenjun Fan, MD, PhD
- Subjects
Diseases of the circulatory (Cardiovascular) system ,RC666-701 ,Public aspects of medicine ,RA1-1270 - Abstract
Therapeutic Area: ASCVD/CVD Risk Assessment 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 four US prospective studies. Net reclassification improvement (NRI) was determined among borderline-intermediate risk patients. Results: We included 5,902 patients (mean age 48.7±16.7 years, 51.2% female, and 7.7% Black). Over a mean follow-up of 5.5 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 23% higher risk of composite ASCVD. Those with Lp(a) >75 had 88% higher risk of ASCVD (HR = 1.88, 95% CI: 1.30-2.70) and more than double the risk of incident stroke (HR = 2.55, 95% CI: 1.54-4.23). C-statistics for our EHR and EHR+Lp(a) models in our EHR training dataset were 0.7475 and 0.7556, respectively, with external validation of 0.7350 and 0.7368, respectively. Among those at borderline/intermediate risk, the NRI 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.
- Published
- 2024
- Full Text
- View/download PDF