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.

Cardiovascular genetic counseling (CVGC) is recommended for a variety of inherited heart conditions; however, its impact on patient empowerment has not been assessed. The Genetic Counseling Outcome Scale (GCOS) is a validated patient reported outcome tool which measures empowerment to capture the impact of clinical genetics services. As a routine clinical practice at our center, adult patients attending a CVGC appointment complete the 24‐item GCOS survey and a 5‐item survey on knowledge of cardiac surveillance recommendations for relatives prior to the clinic visit. To investigate the effect of CVGC, we contacted participants after the appointment to repeat these surveys prior to genetic test result disclosure. Forty‐two participants completed pre‐ and post‐GC surveys. The mean difference between pre‐ and post‐GC empowerment scores was 17.5 points (mean pre‐GC score = 118.5, mean post‐GC score = 136, p < 0.0001; effect size, d = 0.94). Forty percent of individuals (17/42) were aware of surveillance recommendations for at‐risk family members prior to GC; this increased to 76% of individuals (32/42) post‐GC (p < 0.01). This is the first study to explore patient empowerment before and after GC in a cardiology setting. The results demonstrate a significant increase in empowerment and awareness of recommendations for at‐risk relatives as a result of CVGC. This study demonstrates the utility of CVGC in patient care.

Congenital heart disease (CHD) is an indication which spans multiple specialties across various genetic counseling practices. This practice resource aims to provide guidance on key considerations when approaching counseling for this particular indication while recognizing the rapidly changing landscape of knowledge within this domain. This resource was developed with consensus from a diverse group of certified genetic counselors utilizing literature relevant for CHD genetic counseling practice and is aimed at supporting genetic counselors who encounter this indication in their practice both pre-and postnatally.

We report an active learning session which effectively supported 1st year medical students applying their learning experience in a clinical setting. A team-based learning (TBL) on familial hypercholesterolemia (FH) with a live patient was given to deliver basic genetics knowledge in a clinically relevant context. Subsequently, two participating students applied their learning experience by presenting a differential diagnosis of homozygous FH in a patient at a medical mission in Central America. We propose that combining active learning with clinically relevant scenarios effectively fosters student's clinical reasoning skills and can bridge the perceived gap between basic science and clinical education.

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Genetic counselors are one of the many providers involved in caring for patients with congenital heart defects (CHDs); however, little is known about the cardiovascular genetics training they receive by their graduate programs. To explore the recalled education experiences regarding CHDs by practicing genetic counselors, we surveyed graduates of programs primarily accredited by the American Council on Genetic Counseling (ACGC) about their graduate training in this area, the depth of CHD-

The genetic architecture of inherited cardiomyopathies has mechanistic and potentially therapeutic relevance. We have shown that disease-associated variation in two cardiomyopathy-associated genes clusters in functional protein domains. Here, we extend this hypothesis to a comprehensive list of genes associated with cardiovascular disease. Using ClinVar, we identified 12,043 pathogenic or likely pathogenic (P/LP) variants across 190 cardiovascular disease-associated genes. Only P/LP variants with associated adjudication criteria were included. We interrogated these variants for regional clustering by ranking them by their number of P/LP neighbors within successively larger windows. We found that 87% of variants had at least one nearby variant within a 40 bp neighborhood, and 28% had ≥ 10 neighboring variants (Figure A, p<0.0001 vs. normal distribution). Within variant subtypes, 84% of missense variants and 70% of truncating variants neighbored at least 1 other missense or truncating variant, respectively. 21% of missense and 11% of truncating variants had ≥ 10 neighbors (p<0.0001). LDLR and FBN1 had the highest number of missense P/LP variants within a 40 bp neighborhood. We show that these neighborhoods overlap with functional domains of LDLR and FBN1 (Figure B, C). In summary, we demonstrate that a large portion of P/LP variants are regionally clustered in cardiovascular disease-associated genes, regardless of variant type, and align with known functional domains. Future directions will systematically apply these methods across the genome and explore the potential for novel domain identification and targeting these clusters with genome engineering.