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Machine learning models can identify individuals with elevated Lipoprotein(a) (Lp(a)) using routine data. These AI strategies improve screening efficiency and equity for atherosclerotic cardiovascular disease risk assessment.

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Area of Science:

  • Cardiovascular Medicine
  • Artificial Intelligence
  • Genetics

Background:

  • Lipoprotein(a) (Lp(a)) is a genetically determined, lifelong risk factor for atherosclerotic cardiovascular disease (ASCVD).
  • Despite guidelines recommending universal testing, Lp(a) measurement is infrequently performed in clinical practice.
  • There is a need for efficient and equitable methods to screen for elevated Lp(a).

Purpose of the Study:

  • To review recent advances in machine learning (ML)-based strategies for Lp(a) screening.
  • To highlight how ML can enhance the efficiency, yield, and equity of identifying individuals with elevated Lp(a).

Main Methods:

  • Review of studies developing and validating ML models for Lp(a) identification.
  • Analysis of ML models using routinely available clinical variables.
  • Examination of frameworks like ARISE and their validation across diverse cohorts.

Main Results:

  • Three studies have developed and validated ML models to identify individuals with elevated Lp(a) using routine clinical data.
  • The ARISE framework reduced the number needed to test by over 50% and showed consistent performance across demographic subgroups.
  • Decision-tree and neural network models demonstrate feasibility for improving case finding in clinical and population settings.

Conclusions:

  • ML-based strategies offer a scalable approach to implement universal Lp(a) testing recommendations.
  • When developed with unbiased data, externally validated, and assessed for fairness, ML models can systematically identify individuals with elevated Lp(a).
  • These models can facilitate the integration of Lp(a) measurement into routine cardiovascular risk assessment.