Performance of the FIND-FH machine learning algorithm for the identification of individuals with suspected familial hypercholesterolemia.

Background: Familial hypercholesterolemia (FH) is an inherited cholesterol disorder that is markedly underdiagnosed.

Objective: This study evaluated the real-world performance of the Find, Identify, Network, Deliver-FH (FIND-FH) score, a novel machine learning algorithm, in identifying individuals with high likelihood of FH.

Methods: The FIND-FH model was applied to electronic health record (EHR) data from UT Southwestern Medical Center. Manual chart review was performed on those deemed high probability of FH (score >0.35) to assess accuracy of FH diagnosis by modified Simon-Broome and Dutch Lipid Clinic Network (DLCN) criteria. Individual characteristics were compared across quintiles of the FIND-FH score. Individuals deemed suitable for FH outreach were identified using predetermined clinical criteria denoting adequate clinical probability of FH.

Results: Of the 93,418 individuals in the EHR dataset, the FIND-FH algorithm identified 340 with high probability of FH. These 340 individuals had a mean age of 49.8 years, were 59% male, and had a highest low-density lipoprotein cholesterol (LDL-C) of 168.4 mg/dL (±51.9). A total of 20-32% met modified Simon-Broome or DLCN criteria for at least possible FH based on available EHR data. Several variables differed significantly by FIND-FH score quintile, including Simone-Broome and DLCN probability. In the reviewed cohort, 191 (56%) had enough clinical suspicion for FH to warrant outreach. Among these, 101 (53%) had highest LDL-C <190 mg/dL and would be missed by LDL-C-based FH screening strategies.

Conclusion: In a large healthcare system EHR cohort, most individuals identified as higher risk for FH by the FIND-FH algorithm were deemed appropriate for further evaluation, despite the majority not meeting FH diagnostic criteria using available EHR data.