AI Article Synopsis
- * A machine learning model, called ARISE, was developed to improve screening for elevated Lp(a) levels using data from the UK Biobank and validated in three other large studies.
- * ARISE significantly reduces the number of tests needed to identify someone with high Lp(a), making it easier to utilize common clinical data and potentially implement in electronic health records for better screening outcomes.
Article Abstract
Elevated lipoprotein (a) (Lp(a)) is associated with premature atherosclerotic cardiovascular disease. However, fewer than 0.5% of individuals undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under development. Here we describe the development of a machine learning model for targeted screening for elevated Lp(a) (≥150 nmol l) in the UK Biobank (N = 456,815), the largest cohort with protocolized Lp(a) testing. We externally validated the model in 3 large cohort studies, ARIC (N = 14,484), CARDIA (N = 4,124) and MESA (N = 4,672). The model, Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), reduced the number needed to test to find one individual with elevated Lp(a) by up to 67.3%, based on the probability threshold, with consistent performance across external validation cohorts. ARISE could be used to optimize screening for elevated Lp(a) using commonly available clinical features, with the potential for its deployment in electronic health records to enhance the yield of Lp(a) testing in real-world settings.
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| http://dx.doi.org/10.1038/s44161-024-00469-1 | DOI Listing |
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