Kelly D. Myers

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.

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: Familial hypercholesterolemia (FH) is the most common cardiovascular genetic disorder and, if left untreated, is associated with increased risk of premature atherosclerotic cardiovascular disease, the leading cause of preventable death in the United States. Although FH is common, fatal, and treatable, it is underdiagnosed and undertreated due to a lack of systematic methods to identify individuals with FH and limited uptake of cascade testing. Methods and Results: This mixed-method, multi-stage study will optimize, test, and implement innovative approaches for both FH identification and cascade testing in 3 aims. To improve identification of individuals with FH, in Aim 1, we will compare and refine automated phenotype-based and genomic approaches to identify individuals likely to have FH. To improve cascade testing uptake for at-risk individuals, in Aim 2, we will use a patient-centered design thinking process to optimize and develop novel, active family communication methods. Using a prospective, observational pragmatic trial, we will assess uptake and effectiveness of each family communication method on cascade testing. Guided by an implementation science framework, in Aim 3, we will develop a comprehensive guide to identify individuals with FH. Using the Conceptual Model for Implementation Research, we will evaluate implementation outcomes including feasibility, acceptability, and perceived sustainability as well as health outcomes related to the optimized methods and tools developed in Aims 1 and 2. Conclusions: Data generated from this study will address barriers and gaps in care related to underdiagnosis of FH by developing and optimizing tools to improve FH identification and cascade testing.

Background: Atherosclerotic cardiovascular disease remains a major cause of death and disability, especially for high-risk familial hypercholesterolemia individuals. PCSK9i (proprotein convertase subtilisin kexin type 9 inhibitors) reduce low-density lipoprotein cholesterol levels and cardiovascular event rates. However, PCSK9i prescriptions are rejected at high rates by payers, and use is often delayed or eventually abandoned as a treatment option. We tested the hypothesis that acute coronary syndromes, coronary interventions, stroke, and cardiac arrest are more prevalent in patients with rejected or abandoned PCSK9i prescriptions than for those with paid PCSK9i prescriptions. Methods and Results: We identified 139 036 individuals aged ≥18 years who met the following 3 criteria: prescribed PCSK9i between August 2015 and December 2017, had claims history, and had an established date of exposure for paid, rejected, or abandoned status. To compare the effects of rejected versus paid and abandoned versus paid status, propensity score matching was performed to minimize confounding because of baseline differences in patient groups. Cox regression analyses and incidence density rates for cardiovascular events were estimated on the propensity score-matched cohorts. Patients who received 168 or more days of paid PCSK9i medication within a 12-month period were defined as paid. The hazard ratios for composite cardiovascular events outcome in propensity score-matched analyses were 1.10 (95% CI, 1.01–1.19; P =0.02) for rejected versus paid and 1.12 (95% CI, 1.01–1.24; P =0.03) for abandoned versus paid. In a stricter analysis where paid patients were defined by receiving 338 or more days of therapy within 12-months, hazard ratio was 1.16 (95% CI, 1.02–1.30; P =0.04) for rejected versus paid and 1.21 (95% CI, 1.04–1.38; P =0.03) for the abandoned versus paid status. Higher PCSK9i rejection rates were observed with women, racial minorities, and lower-income groups. Conclusions: Individuals in the rejected and abandoned cohorts had significantly increased risk of cardiovascular events compared with those in the paid cohort. Rejection, abandonment, and disparities related to PCSK9i prescriptions are related to higher cardiovascular outcome rates.

Familial hypercholesterolemia (FH) is an underdiagnosed dominant genetic condition affecting approximately 0.4% of the population and has up to a 20-fold increased risk of coronary artery disease if untreated. Simple screening strategies have false positive rates greater than 95%. As part of the FH Foundation′s FIND FH initiative, we developed a classifier to identify potential FH patients using electronic health record (EHR) data at Stanford Health Care. We trained a random forest classifier using data from known patients (n = 197) and matched non-cases (n = 6590). Our classifier obtained a positive predictive value (PPV) of 0.88 and sensitivity of 0.75 on a held-out test-set. We evaluated the accuracy of the classifier′s predictions by chart review of 100 patients at risk of FH not included in the original dataset. The classifier correctly flagged 84% of patients at the highest probability threshold, with decreasing performance as the threshold lowers. In external validation on 466 FH patients (236 with genetically proven FH) and 5000 matched non-cases from the Geisinger Healthcare System our FH classifier achieved a PPV of 0.85. Our EHR-derived FH classifier is effective in finding candidate patients for further FH screening. Such machine learning guided strategies can lead to effective identification of the highest risk patients for enhanced management strategies.

The Crohn’s and Colitis Foundation of America Partners Patient-Powered Research Network (PPRN) seeks to advance and accelerate comparative effectiveness and translational research in inflammatory bowel diseases (IBDs). Our IBD-focused PCORnet PPRN has been designed to overcome the major obstacles that have limited patient-centered outcomes research in IBD by providing the technical infrastructure, patient governance, and patient-driven functionality needed to: 1) identify, prioritize, and undertake a patient-centered research agenda through sharing person-generated health data; 2) develop and test patient and provider-focused tools that utilize individual patient data to improve health behaviors and inform health care decisions and, ultimately, outcomes; and 3) rapidly disseminate new knowledge to patients, enabling them to improve their health. The Crohn’s and Colitis Foundation of America Partners PPRN has fostered the development of a community of citizen scientists in IBD; created a portal that will recruit, retain, and engage members and encourage partnerships with external scientists; and produced an efficient infrastructure for identifying, screening, and contacting network members for participation in research.