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Implications of Diagnosis Through a Machine Learning Algorithm on Management of People With

Kain Kim1, Samir C Faruque2, Shivani Lam3

  • 1Department of Medicine, Emory School of Medicine, Atlanta, Georgia, USA.

JACC. Advances
|October 7, 2024
PubMed

Insights

A machine learning algorithm (MLA) identified individuals with familial hypercholesterolemia (FH). Those diagnosed with FH received more consistent medical management and cardiovascular monitoring compared to undiagnosed individuals.

Area of Science:

  • Cardiology
  • Genetics
  • Medical Informatics

Background:

  • Familial hypercholesterolemia (FH) is an underdiagnosed genetic disorder causing premature cardiovascular disease.
  • The Flag, Identify, Network, and Deliver (FIND) FH machine learning algorithm (MLA) aids in identifying high-risk individuals through electronic medical records for targeted screening.

Purpose of the Study:

  • To characterize the diagnostic coding status of patients identified by the FIND FH MLA.
  • To assess correlations between FH diagnosis status and medical management patterns, including cardiovascular outcomes.

Main Methods:

  • A retrospective, cross-sectional cohort study was conducted at a large academic medical center.
  • The FIND FH MLA was applied to identify potential FH cases, followed by manual chart review and stratification by diagnosis status.
  • Key variables including demographics, medical history, laboratory values, medications, and cardiovascular outcomes were compared across diagnostic groups.

Main Results:

  • The MLA identified 471 patients with high probability for FH over 5.5 years; 121 (26%) met criteria for "likely FH" without a prior diagnosis.
  • Patients with established FH diagnoses (n=32) showed significantly more lipid panel monitoring, advanced lipid-lowering therapies, specialist visits, and advanced cardiovascular testing (CACS, Lp(a)) compared to those with likely FH.
  • No significant difference in prior major adverse cardiovascular events was observed between the established FH and likely FH groups.

Conclusions:

  • The FIND FH MLA demonstrates feasibility in identifying undiagnosed individuals with FH.
  • This approach can help address treatment disparities in individuals at increased cardiovascular risk due to FH.
Abstract

Keywords:
electronic health recordfamilial hypercholesterolemiamachine learning algorithm

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