Background Cardiovascular outcomes for people with familial hypercholesterolaemia can be improved with diagnosis and medical management. However, 90% of individuals with familial hypercholesterolaemia remain undiagnosed in the USA. We aimed to accelerate early diagnosis and timely intervention for more than 1•3 million undiagnosed individuals with familial hypercholesterolaemia at high risk for early heart attacks and strokes by applying machine learning to large health-care encounter datasets. MethodsWe trained the FIND FH machine learning model using deidentified health-care encounter data, including procedure and diagnostic codes, prescriptions, and laboratory findings, from 939 clinically diagnosed individuals with familial hypercholesterolaemia (395 of whom had a molecular diagnosis) and 83 136 individuals presumed free of familial hypercholesterolaemia, sampled from four US institutions. The model was then applied to a national health-care encounter database (170 million individuals) and an integrated health-care delivery system dataset (174 000 individuals). Individuals used in model training and those evaluated by the model were required to have at least one cardiovascular disease risk factor (eg, hypertension, hypercholesterolaemia, or hyperlipidemia). A Health Insurance Portability and Accountability Act of 1996-compliant programme was developed to allow providers to receive identification of individuals likely to have familial hypercholesterolaemia in their practice. Findings Using a model with a measured precision (positive predictive value) of 0•85, recall (sensitivity) of 0•45, area under the precision-recall curve of 0•55, and area under the receiver operating characteristic curve of 0•89, we flagged 1 331 759 of 170 416 201 patients in the national database and 866 of 173 733 individuals in the health-care delivery system dataset as likely to have familial hypercholesterolaemia. Familial hypercholesterolaemia experts reviewed a sample of flagged individuals (45 from the national database and 103 from the health-care delivery system dataset) and applied clinical familial hypercholesterolaemia diagnostic criteria. Of those reviewed, 87% (95% Cl 73-100) in the national database and 77% (68-86) in the health-care delivery system dataset were categorised as having a high enough clinical suspicion of familial hypercholesterolaemia to warrant guideline-based clinical evaluation and treatment.Interpretation The FIND FH model successfully scans large, diverse, and disparate health-care encounter databases to identify individuals with familial hypercholesterolaemia. FundingThe FH Foundation funded this study. Support was received from Amgen, Sanofi, and Regeneron.

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Background Homozygous familial hypercholesterolemia (HoFH) is a rare, treatment‐resistant disorder characterized by early‐onset atherosclerotic and aortic valvular cardiovascular disease if left untreated. Contemporary information on HoFH in the United States is lacking, and the extent of underdiagnosis and undertreatment is uncertain. Methods and Results Data were analyzed from 67 children and adults with clinically diagnosed HoFH from the CASCADE (Cascade Screening for Awareness and Detection) FH Registry. Genetic diagnosis was confirmed in 43 patients. We used the clinical characteristics of genetically confirmed patients with HoFH to query the Family Heart Database, a US anonymized payer health database, to estimate the number of patients with similar lipid profiles in a “real‐world” setting. Untreated low‐density lipoprotein cholesterol levels were lower in adults than children (533 versus 776 mg/dL; P =0.001). At enrollment, atherosclerotic cardiovascular disease and supravalvular and aortic valve stenosis were present in 78.4% and 43.8% and 25.5% and 18.8% of adults and children, respectively. At most recent follow‐up, despite multiple lipid‐lowering treatment, low‐density lipoprotein cholesterol goals were achieved in only a minority of adults and children. Query of the Family Heart Database identified 277 individuals with profiles similar to patients with genetically confirmed HoFH. Advanced lipid‐lowering treatments were prescribed for 18%; 40% were on no lipid‐lowering treatment; atherosclerotic cardiovascular disease was reported in 20%; familial hypercholesterolemia diagnosis was uncommon. Conclusions Only patients with the most severe HoFH phenotypes are diagnosed early. HoFH remains challenging to treat. Results from the Family Heart Database indicate HoFH is systemically underdiagnosed and undertreated. Earlier screening, aggressive lipid‐lowering treatments, and guideline implementation are required to reduce disease burden in HoFH.

Background Data mining of electronic health records to identify patients suspected of familial hypercholesterolemia (FH) has been limited by absence of both phenotypic and genomic data in the same cohort. Methods and Results Using the Geisinger MyCode Community Health Initiative cohort (n=130 257), we ran 2 screening algorithms (Mayo Clinic [Mayo] and flag, identify, network, deliver [FIND] FH) to determine FH genetic and phenotypic diagnostic yields. With 29 243 excluded by Mayo (for secondary causes of hypercholesterolemia, no lipid value in electronic health records), 52 034 excluded by FIND FH (insufficient data to run the model), and 187 excluded for prior FH diagnosis, a final cohort of 59 729 participants was created. Genetic diagnosis was based on presence of a pathogenic or likely pathogenic variant in FH genes. Charts from 180 variant‐negative participants (60 controls, 120 identified by FIND FH and Mayo) were reviewed to calculate Dutch Lipid Clinic Network scores; a score ≥5 defined probable phenotypic FH. Mayo flagged 10 415 subjects; 194 (1.9%) had a pathogenic or likely pathogenic FH variant. FIND FH flagged 573; 34 (5.9%) had a pathogenic or likely pathogenic variant, giving a net yield from both of 197 out of 280 (70%). Confirmation of a phenotypic diagnosis was constrained by lack of electronic health record data on physical findings or family history. Phenotypic FH by chart review was present by Mayo and/or FIND FH in 13 out of 120 versus 2 out of 60 not flagged by either ( P <0.09). Conclusions Applying 2 recognized FH screening algorithms to the Geisinger MyCode Community Health Initiative identified 70% of those with a pathogenic or likely pathogenic FH variant. Phenotypic diagnosis was rarely achievable due to missing data.