Implementation of a machine learning model and direct-to-patient outreach program for targeted screening for familial hypercholesterolemia

Abstract

OBJECTIVES

Heterozygous familial hypercholesterolemia (FH) is underdiagnosed. This program evaluated the impact of implementing a machine learning model (MLM), expert chart review and clinical consultation in the diagnosis of FH.

METHODS

Flag, Identify, Network and Deliver FH (FIND-FH) was applied to 147,412 unique patient records in the Dartmouth Health system and identified 388 adult patients at risk for FH. Lipidologists and cardiologists performed chart reviews using FH clinical criteria. Patients were excluded from outreach if they had an established diagnosis of FH, a lipidologist, insufficient information to suspect FH, a low likelihood of FH, moved, died, or had alternative medical priorities at the time of review.

RESULTS

Among 388 flagged patients, median age was 50 years (IQR: 39-59 years), 43% were female, and 88% self-identified as white. After expert review, 208 (54%) patients were removed from outreach for meeting exclusion criteria. The majority of those excluded had a low likelihood of having FH (115/208, 55%). The median low-density lipoprotein cholesterol (LDL-C) in excluded patients was 134 mg/dL (IQR: 102-154 mg/dL) compared to 172 mg/dL (IQR: 132-216 mg/dL) in patients selected for outreach. A high-touch, direct-to-patient outreach process yielded 72 clinical visits (19%) and 58 new diagnoses of possible/probable/definite FH (15%).

CONCLUSION

The Find-FH MLM flagged 388 individuals as “at risk” for FH of whom 58 (15%) ultimately received a diagnosis of possible/probable/definite FH. While this represents a substantial improvement on 1:250 (0.4%) expected when screening the general population, it was labor intensive. For scalability, improved accuracy of the MLM and efficiency of chart review and outreach are needed.