| Title: | Development and multinational validation of an algorithmic strategy for high Lp(a) screening |
| Journal: | Nature Cardiovascular Research |
| Published: | 1 May 2024 |
| DOI: | https://doi.org/10.1038/s44161-024-00469-1 |
Publication 12169
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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−1) 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.</p>
8 Authors
- Arya Aminorroaya
- Lovedeep S. Dhingra
- Evangelos K. Oikonomou
- Seyedmohammad Saadatagah
- Phyllis Thangaraj
- Sumukh Vasisht Shankar
- Erica S. Spatz
- Rohan Khera
1 Application
| Application ID | Title |
|---|---|
| 71033 | A framework to assess causal effects in observational data through deep digital phenotyping and creating digital twins |
Enabling scientific discoveries that improve human health