Accurate and consistent variant classification is imperative for incorporation of rapidly developing sequencing technologies into genomic medicine for improved patient care. An essential requirement for achieving standardized and reliable variant interpretation is data sharing, facilitated by a centralized open‐source database. Familial hypercholesterolemia (FH) is an exemplar of the utility of such a resource: it has a high incidence, a favorable prognosis with early intervention and treatment, and cascade screening can be offered to families if a causative variant is identified. ClinVar, an NCBI‐funded resource, has become the primary repository for clinically relevant variants in Mendelian disease, including FH. Here, we present the concerted efforts made by the Clinical Genome Resource, through the FH Variant Curation Expert Panel and global FH community, to increase submission of FH‐associated variants into ClinVar. Variant‐level data was categorized by submitter, variant characteristics, classification method, and available supporting data. To further reform interpretation of FH‐associated variants, areas for improvement in variant submissions were identified; these include a need for more detailed submissions and submission of supporting variant‐level data, both retrospectively and prospectively. Collaborating to provide thorough, reliable evidence‐based variant interpretation will ultimately improve the care of FH patients.

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.

Traditional research approaches do not promote timely implementation of evidence-based innovations (EBIs) to benefit patients. Embedding research within health systems can accelerate EBI implementation by blending rigorous methods with practical considerations in real-world settings. A state-of-the-art (SOTA) conference was convened in February 2019 with five workgroups that addressed five facets of embedded research and its potential to impact healthcare. This article reports on results from the workgroup focused on how embedded research programs can be implemented into heath systems for greatest impact. Based on a pre-conference survey, participants indicating interest in accelerating implementation were invited to participate in the SOTA workgroup. Workgroup participants (N = 26) developed recommendations using consensus-building methods. Ideas were grouped by thematic clusters and voted on to identify top recommendations. A summary was presented to the full SOTA membership. Following the conference, the workgroup facilitators (LJD, CDH, NR) summarized workgroup findings, member-checked with workgroup members, and were used to develop recommendations. The workgroup developed 12 recommendations to optimize impact of embedded researchers within health systems. The group highlighted the tension between "ROI vs. R01" goals-where health systems focus on achieving return on their investments (ROI) while embedded researchers focus on obtaining research funding (R01). Recommendations are targeted to three key stakeholder groups: researchers, funders, and health systems. Consensus for an ideal foundation to support optimal embedded research is one that (1) maximizes learning; (2) aligns goals across all 3 stakeholders; and (3) implements EBIs in a consistent and timely fashion. Four cases illustrate a variety of ways that embedded research can be structured and conducted within systems, by demonstrating key embedded research values to enable collaborations with academic affiliates to generate actionable knowledge and meaningfully accelerate implementation of EBIs to benefit patients. Embedded research approaches have potential for transforming health systems and impacting patient health. Accelerating embedded research should be a focused priority for funding agencies to maximize a collective return on investment.