Laney K. Jones

Genetic identification of familial hypercholesterolemia within a single U.S. health care system

Unleashing the power of precision medicine Precision medicine promises the ability to identify risks and treat patients on the basis of pathogenic genetic variation. Two studies combined exome sequencing results for over 50,000 people with their electronic health records. Dewey et al. found that ∼3.5% of individuals in their cohort had clinically actionable genetic variants. Many of these variants affected blood lipid levels that could influence cardiovascular health. Abul-Husn et al. extended these findings to investigate the genetics and treatment of familial hypercholesterolemia, a risk factor for cardiovascular disease, within their patient pool. Genetic screening helped identify at-risk patients who could benefit from increased treatment. Science, this issue p. 10.1126/science.aaf6814, p. 10.1126/science.aaf7000 Genomic screening can prompt the diagnosis of familial hypercholesterolemia patients, the majority of whom are receiving inadequate lipid-lowering therapy. INTRODUCTION Familial hypercholesterolemia (FH) is a public health genomics priority but remains underdiagnosed and undertreated despite widespread cholesterol screening. This represents a missed opportunity to prevent FH-associated cardiovascular morbidity and mortality. Pathogenic variants in three genes (LDLR, APOB, and PCSK9) account for the majority of FH cases. We assessed the prevalence and clinical impact of FH-associated genomic variants in 50,726 individuals from the MyCode Community Health Initiative at Geisinger Health System who underwent exome sequencing as part of the DiscovEHR human genetics collaboration with the Regeneron Genetics Center. RATIONALE Genetic testing for FH is uncommon in clinical practice in the United States, and the prevalence of FH variants in U.S. populations has not been well established. We sought to evaluate FH prevalence in a large integrated U.S. health care system using genomic sequencing and electronic health record (EHR) data. We determined the impact of FH variants on low-density lipoprotein cholesterol (LDL-C) levels and coronary artery disease (CAD) risk. We assessed the likelihood of FH variant carriers achieving a presequencing EHR-based FH diagnosis according to established clinical diagnostic criteria. Finally, we examined the rates of statin medication use and outcomes in FH variant carriers. RESULTS Thirty-five known and predicted pathogenic variants in LDLR, APOB, and PCSK9 were identified in 229 individuals. The estimated FH prevalence was 1:256 in unselected participants and 1:118 in participants ascertained via the cardiac catheterization laboratory. FH variants were found in only 2.5% of individuals with severe hypercholesterolemia (maximum EHR-documented LDL-C ≥ 190 mg/dl) in the cohort, and a maximum LDL-C of ≥190 mg/dl was absent in 45% of FH variant carriers. Overall, FH variant carriers had 69 ± 3 mg/dl greater maximum LDL-C than sequenced noncarriers (P = 1.8 × 10−20) and had significantly increased odds of general and premature CAD [odds ratio (OR), 2.6 (P = 4.3 × 10−11) and 3.7 (P = 5.5 × 10−14), respectively]. The increased odds of general and premature CAD were most pronounced in carriers of LDLR predicted loss-of-function variants [OR, 5.5 (P = 7.7 × 10−13) and 10.3 (P = 9.8 × 10−19), respectively]. Fourteen FH variant carriers were deceased; chart review revealed that none of these individuals had a clinical diagnosis of FH. Before genetic testing, only 15% of FH variant carriers had an ICD-10 (International Classification of Diseases, 10th revision) diagnosis code for pure hypercholesterolemia or had been seen in a lipid clinic, suggesting that few had been previously diagnosed with FH. Retrospectively applying Dutch Lipid Clinic Network diagnostic criteria to EHR data, we found presequencing criteria supporting a probable or definite clinical diagnosis of FH in 24% of FH variant carriers, highlighting the limitations of using existing clinical criteria for EHR-based screening in the absence of genetic testing. Active statin use was identified in 58% and high-intensity statin use in 37% of FH variant carriers. Only 46% of carriers currently on statin therapy had a most recent LDL-C level below 100 mg/dl compared to 77% of noncarriers. CONCLUSION In summary, we show that large-scale genomic screening in patients with longitudinal EHR data has the ability to detect FH, uncover and characterize novel pathogenic variants, determine disease prevalence, and enhance overall knowledge of clinical impact and outcomes. The 1:256 prevalence of FH variants in this predominantly European-American cohort is in line with prevalence estimates from recent work in European cohorts. Our findings highlight the undertreatment of FH variant carriers and demonstrate a potential clinical benefit for large-scale sequencing initiatives in service of precision medicine. Prevalence and clinical impact of FH variants in a large U.S. clinical care cohort. (A) Distribution of 229 heterozygous carriers of an FH variant in the DiscovEHR cohort by FH gene. (B) Prevalence of an FH variant in the DiscovEHR cohort and according to recruitment site

Developing Pharmacogenomic Reports: Insights from Patients and Clinicians

Increasingly, for a variety of indications, patients have their genomes sequenced and actionable results returned. A subset of returned results is pharmacogenomic (PGx) variants involved in the metabolism or action of medications. Although the impact of these variants on health is well‐documented, little research exists on how to communicate these findings to patients and clinicians. We conducted semistructured interviews with end users to understand how best to communicate PGx results. Overall, patients and clinicians had similar opinions regarding report content, delivery, and application. Unique concerns specific to each stakeholder group were also expressed. Patients wanted an easy‐to‐understand individualized report that clinicians utilized to guide their care. Clinicians wanted reports that were easy‐to‐use, actionable, and integrated into their workflow. Implementation of these reports in a clinical setting will allow for broader user feedback and iterative improvement.

Generation and Implementation of a Patient-Centered and Patient-Facing Genomic Test Report in the EHR

Context: Communication of genetic laboratory results to patients and providers is impeded by the complexity of results and reports. This can lead to misinterpretation of results, causing inappropriate care. Patients often do not receive a copy of the report leading to possible miscommunication. To address these problems, we conducted patient-centered research to inform design of interpretive reports. Here we describe the development and deployment of a specific patient-centered clinical decision support (CDS) tool, a multi-use patient-centered genomic test report (PGR) that interfaces with an electronic health record (EHR). Implementation Process: A PGR with a companion provider report was configured for implementation within the EHR using locally developed software (COMPASS™) to manage secure data exchange and access. Findings: We conducted semi-structured interviews with patients, family members, and clinicians that showed they sought clear information addressing findings, family implications, resources, prognosis and next steps relative to the genomic result. Providers requested access to applicable, available clinical guidelines. Initial results indicated patients and providers found the PGR contained helpful, valuable information and would provide a basis for result-related conversation between patients, providers and family. Major Themes: Direct patient involvement in the design and development of a PGR identified format and presentation preferences, and delivery of relevant information to patients and providers, prompting the creation of a CDS tool. Conclusions: Research and development of patient-centered CDS tools designed to support improved patient outcomes, are enhanced by early and substantial engagement of patients in contributing to all phases of tool design and development.

Utilizing big data to provide better health at lower cost

PURPOSE The efficient use of big data in order to provide better health at a lower cost is described. SUMMARY As data become more usable and accessible in healthcare, organizations need to be prepared to use this information to positively impact patient care. In order to be successful, organizations need teams with expertise in informatics and data management that can build new infrastructure and restructure existing infrastructure to support quality and process improvements in real time, such as creating discrete data fields that can be easily retrieved and used to analyze and monitor care delivery. Organizations should use data to monitor performance (e.g., process metrics) as well as the health of their populations (e.g., clinical parameters and health outcomes). Data can be used to prevent hospitalizations, combat opioid abuse and misuse, improve antimicrobial stewardship, and reduce pharmaceutical spending. These examples also serve to highlight lessons learned to better use data to improve health. For example, data can inform and create efficiencies in care and engage and communicate with stakeholders early and often, and collaboration is necessary to have complete data. To truly transform care so that it is delivered in a way that is sustainable, responsible, and patient-centered, health systems need to act on these opportunities, invest in big data, and routinely use big data in the delivery of care. CONCLUSION Using data efficiently has the potential to improve the care of our patients and lower cost. Despite early successes, barriers to implementation remain including data acquisition, integration, and usability.