| Title: | Evaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems |
| Journal: | Circulation Genomic and Precision Medicine |
| Published: | 23 Jan 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39846171/ |
| DOI: | https://doi.org/10.1161/circgen.124.004632 |
| URL: | https://doi.org/10.1101/2024.03.02.24303582 |
Publication 14924
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Abstract
BACKGROUND: While universal screening for Lipoprotein(a) [Lp(a)] is increasingly recommended, <0.5% of patients undergo Lp(a) testing. Here, we assessed the feasibility of deploying Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), a validated machine learning tool, to health system electronic health records to increase the yield of Lp(a) testing.</p>
METHODS: We randomly sampled 100 000 patients from the Yale-New Haven Health System to evaluate the feasibility of ARISE deployment. We also evaluated Lp(a)-tested populations in the Yale-New Haven Health System (n=7981) and the Vanderbilt University Medical Center (n=10 635) to assess the association of ARISE score with elevated Lp(a). To compare the representativeness of the Lp(a)-tested population, we included 456 815 participants from the UK Biobank and 23 280 from 3 US-based cohorts of Atherosclerosis Risk in Communities, Coronary Artery Risk Development in Young Adults, and Multi-Ethnic Study of Atherosclerosis.</p>
RESULTS: Among 100 000 randomly selected Yale-New Haven Health System patients, 413 (0.4%) had undergone Lp(a) measurement. ARISE score could be computed for 31 586 patients based on existing data, identifying 2376 (7.5%) patients with a high probability of elevated Lp(a). A positive ARISE score was associated with significantly higher odds of elevated Lp(a) in the Yale-New Haven Health System (odds ratio, 1.87 [95% CI, 1.65-2.12]) and the Vanderbilt University Medical Center (odds ratio, 1.41 [95% CI, 1.24-1.60]). The Lp(a)-tested population significantly differed from other study cohorts in terms of ARISE features.</p>
CONCLUSIONS: We demonstrate the feasibility of deployment of ARISE in US health systems to define the risk of elevated Lp(a), enabling a high-yield testing strategy. We also confirm the markedly low adoption of Lp(a) testing, which is also being restricted to a highly selected population.</p>
12 Keywords
- Adult
- Aged
- Atherosclerosis
- Electronic Health Records
- Female
- Humans
- Lipoprotein(a)
- Machine Learning
- Male
- Mass Screening
- Middle Aged
- Risk Factors
4 Authors
- Arya Aminorroaya
- Lovedeep S Dhingra
- Evangelos K Oikonomou
- 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