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

Kain Kim; Samir C Faruque; Shivani Lam; David Kulp; Xinwei He; Laurence S Sperling; Danny J Eapen

doi:10.1016/j.jacadv.2024.101184

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

JACC Adv. 2024 Aug 21;3(9):101184. doi: 10.1016/j.jacadv.2024.101184. eCollection 2024 Sep.

Authors

Kain Kim¹, Samir C Faruque², Shivani Lam³, David Kulp¹, Xinwei He⁴, Laurence S Sperling⁵, Danny J Eapen¹

Affiliations

¹ Department of Medicine, Emory School of Medicine, Atlanta, Georgia, USA.
² Division of General Medicine, Washington University School of Medicine, St. Louis, Missouri, USA.
³ Department of Biology, Emory University, Wayne Rollins Research Center, Atlanta, Georgia, USA.
⁴ Department of Biostatistics and Bioinformatics, Rollins School of Public Health at Emory University, Atlanta, Georgia, USA.
⁵ Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Emory University, Emory School of Medicine, Atlanta, Georgia, USA.

Abstract

Background: Familial hypercholesterolemia (FH) is an underdiagnosed genetic condition that leads to premature cardiovascular disease. Flag, Identify, Network, and Deliver (FIND) FH is a machine learning algorithm (MLA) developed by the Family Heart Foundation that identifies high-risk individuals in the electronic medical record for targeted FH screening.

Objectives: The purpose of this study was to characterize the FH diagnostic coding status of patients detected by a MLA screening and assess for correlations with patterns in medical management and cardiovascular outcomes.

Methods: We applied the FIND FH MLA to a retrospective, cross-sectional cohort within one large academic medical center. Individual patient charts were manually reviewed and stratified by diagnosis status. Variables including baseline characteristics, medical history, family history, laboratory values, medications, and cardiovascular outcomes were compared across diagnosis status.

Results: The MLA identified 471 patients over 5.5 years with a high probability for FH. 121 (26%) previously undiagnosed patients met criteria for having "likely FH." Those with established FH diagnoses (n = 32) had significantly more lipid panel monitoring, prescriptions for non-statin or combination lipid-lowering agents, visits with a cardiologist, and frequency of coronary artery calcium score (CACS) testing or lipoprotein(a) testing than undiagnosed patients with likely FH. The 2 groups had no significant differences in having had prior major adverse cardiovascular events. The remaining 318 patients were classified as having "suspected FH."

Conclusions: These findings suggest that implementation of a MLA approach such as FIND FH may be feasible for identifying undiagnosed individuals living with FH, as well as addressing treatment disparities in this population at increased cardiovascular risk.

Keywords: electronic health record; familial hypercholesterolemia; machine learning algorithm.