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

Journal: Circulation. Genomic and precision medicine
PMID: 39846171

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.

Authors

  • Arya Aminorroaya
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Lovedeep S Dhingra
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Evangelos K Oikonomou
    Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK.
  • Rohan Khera
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

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