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

Abstract

Background: While universal screening for Lp(a; lipoprotein[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.

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 scores 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.

Results: Among 100 000 randomly selected Yale-New Haven Health System patients, 413 (0.4%) had undergone Lp(a) measurement. ARISE scores 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.

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 low adoption of Lp(a) testing, which is also being restricted to a highly selected population.