Contemporary patterns of lipoprotein(a) testing and associated clinical care and outcomes

Evaluation of a Machine Learning-guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems

“…18 Despite the clinical significance of elevated Lp(a) and current guideline recommendations for once-in-a-lifetime universal screening for this condition, only 0.4% have undergone Lp(a) testing which aligns with the reported measures from other health systems and claims databases. [15][16][17] Adherence to guideline recommendations in realworld clinical practice remains a challenge in different domains of medicine and cardiology with implementation science being an active area of interest and research. 19,20 The feasibility of deploying ARISE within health systems suggests its potential as an effective strategy to bridge this gap and promote widespread Lp(a) testing and case identification, thereby enhancing care quality and patient outcomes on a larger scale.…”

“…This could stem from healthcare providers preferentially ordering Lp(a) tests for high-risk patients or those with recurrent cardiovascular events. [15][16][17] Moreover, the gradual dissemination of new medical practices and the potential future availability of targeted therapeutics may catalyze the broader adoption of universal Lp(a) screening. However, there is a wide gap between existing knowledge and its practical application, wherein ARISE implementation within health systems represents a promising avenue to address this disconnect.…”

“…These individuals should subsequently undergo rigorous cardiovascular risk management, e.g., statins, proprotein convertase subtilisin/kexin type 9 inhibitors, and lifestyle modifications. 6,[15][16][17][21][22][23][24][25] Additionally, novel Lp(a)-targeted medications are being developed and tested in randomized clinical trials. [9][10][11][12][13][14] Nevertheless, inadequate testing challenges the timely recruitment of the participants.…”

“…[7][8][9][10][11][12][13][14] Despite these developments, fewer than 0.5% of the patients undergo Lp(a) testing. [15][16][17] To address the limited testing of Lp(a) in contemporary practice, we have developed and validated a novel machine learning model to increase the yield of Lp(a) testing using six features, including ASCVD history, use of statins, use of anti-hypertensive medications, serum low-density lipoprotein cholesterol (LDL-C), serum high-density lipoprotein cholesterol (HDL-C), and serum triglycerides. 18 The model, Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), demonstrated a consistent reduction of the number-needed-to-test to find one case with elevated Lp(a) by more than 50% in four multinational cohorts.…”

Development and Multinational Validation of a Novel Algorithmic Strategy for High Lp(a) Screening

“…[8][9][10][11][12] However, fewer than 0.5% of patients undergo Lp(a) testing based on contemporary real-world assessments. [13][14][15] The underrecognition of those with an elevated Lp(a) is a pressing concern due to the rapidly evolving landscape for the Lp(a)-associated cardiovascular risk and its management.…”