Bangash, Hana

Abstract Objective To describe the development and implementation of an automated platform for genomic risk prediction that integrates multiple data types. Materials and methods Using the REDCap infrastructure, we constructed the R4 (Recruitment, Results, and Risk Reduction) platform to intake data from clinical sites, partner laboratories, participant surveys, and electronic health record (EHR) data across 13 institutions. Results The R4 Portal successfully integrated data to generate genome-informed risk assessments (GIRAs) across 11 conditions for a 23 840 person cohort. Testing phases and quality control led to network-wide protocols ensuring consistency and accuracy. Discussion As the science of estimating disease risk evolves, standardized and high-throughput methods of collecting and manipulating complex data are required. Platforms should be open-source, modular, and reusable, ensuring flexibility, security, and integration across healthcare environments. Conclusion The electronic MEdical Records and GEnomics (eMERGE) network successfully generated and returned comprehensive risk profiles using logic and data specific to 11 conditions in a secure and semi-automated fashion employing a customized REDCap database.

Introduction: We validated the “Familial Hypercholesterolemia (FH) Decision Aid” to promote shared decision making (SDM) about lipid lowering therapy, based on patient-clinician encounters. Methods: Validation of the DA included an assessment of whether the content and design of the tool facilitated SDM and patient engagement and to determine how well the tool integrated with clinical workflow. Validation was conducted during patient-clinician encounters in the Mayo preventive cardiology clinic. As inclusion criteria we considered an upcoming appointment in the clinic, an LDL-C level ≥ ~160 mg/dL, and no prior diagnosis of FH. Prior to the encounters, each clinician viewed a demonstration of the online, interactive DA. During the encounters, the DA was shared with patients using the clinic room computer screen, either in-person or virtually. Patient-clinician encounters were video recorded for further analysis by two design researchers and experts in SDM. Results: Ten patients were enrolled in the study (7 male; average age 51 years). The average patient LDL-C level was 207 mg/dL, range 157 - 248 mg/dL. Review and qualitative analysis of the encounter recordings yielded 4 key findings: i) clinicians primarily used the cardiovascular risk estimate in the DA to guide conversation with patients; ii) highly trained specialists needed to change their usual approach to care and conversation to integrate the DA with their clinical workflows; iii) as clinicians became more familiar with the DA, they moved through the tool at a more efficient pace; and iv) each clinician developed his/her own style of navigating the DA, either moving through it one screen at a time or focusing on one specific section for the duration of the conversation. The DA effectively guided conversation and facilitated active patient engagement in 6 of 10 encounters. Social distancing and use of telemedicine encounters, due to the COVID-19 pandemic, limited patient engagement in 2 of 10 encounters. Overall, the DA promoted SDM regarding FH management in 9 of 10 encounters. Conclusion: Using patient-clinician encounters, we found the FH DA to facilitate SDM regarding lipid-lowering therapy. The tool has potential to improve care of FH patients and increase patient engagement and knowledge.

Introduction. We assessed the effects of a polygenic risk score (PRS), monogenic variants (Mono + ), and family history (FamHx) on the risk of coronary heart disease (CHD) and whether incorporating a PRS into the Pooled Cohort Equations (PCE) improves risk prediction. Method. In 199,997 UK Biobank participants, we calculated a PRS CHD (metaGRS, based on ~1.6 million single nucleotide variants), identified individuals with monogenic etiology (presence of a pathogenic variant in LDLR , APOB , or PCSK9 ), and ascertained family history of CHD (CHD in a first-degree relative). Odds of CHD (myocardial infarction/ coronary revascularization/ cardiovascular death) associated with PRS CHD , Mono + , and FamHx at age 55 were computed using logistic regression analyses adjusted for age, sex, and first 4 principal components of ancestry. We combined PRS CHD and PCE into an integrated score (IS) and assessed net reclassification using an actionable risk threshold of 7.5%. Results. The odds ratio (OR) for CHD in the top 5 th percentile of PRS CHD (PRS P95 ), Mono + , and FamHx were 3.37 (2.98 - 3.81), 2.68 (1.67 - 4.07), and 1.61 (1.47 - 1.76), respectively (all p<0.001). These effects were independent and similar in different subgroups. ( Figure 1-A ) Compared to PRS Q2-4 Mono - (3 middle quintiles of PRS CHD and no monogenic variants), the OR for CHD ranged from 1.73 (0.28 - 5.48, p = 0.446) in PRS Q1 Mono + to 5.10 (2.14 - 10.24, p <0.001) in PRS Q5 Mono + . A similar pattern was seen for other combinations, indicating additive effects ( Figure 1-B ). Incorporating PRS CHD into PCE resulted in a net reclassification improvement of 4.56%. Compared to those with high PCE and low IS, the hazard ratio for CHD in those with low PCE and high IS was 1.48 (1.34 - 1.64, p <0.001). Conclusion. The CHD risk associated with PRS CHD , monogenic variants, and family history is independent and additive. Combining PRS CHD and PCE improved risk prediction, that could improve further by adding monogenic variants and family history.

Familial Hypercholesterolemia (FH) is underdiagnosed in the United States. Clinical decision support (CDS) could increase FH detection once implemented in clinical workflows. We deployed CDS for FH at an academic medical center and sought clinician insights using an implementation survey. In November 2020, the FH CDS was deployed in the electronic health record at all Mayo Clinic sites in two formats: a best practice advisory (BPA) and an in-basket alert. Over three months, 104 clinicians participated in the survey (response rate 11.1%). Most clinicians (81%) agreed that CDS implementation was a good option for identifying FH patients; 78% recognized the importance of implementing the tool in practice, and 72% agreed it would improve early diagnosis of FH. In comparing the two alert formats, clinicians found the in-basket alert more acceptable (p = 0.036) and more feasible (p = 0.042) than the BPA. Overall, clinicians favored implementing the FH CDS in clinical practice and provided feedback that led to iterative refinement of the tool. Such a tool can potentially increase FH detection and optimize patient management.

Introduction: Current risk equations for coronary heart disease (CHD) do not take into account family history or cumulative exposure to LDL-C. We modified CHD risk equations for use in familial hypercholesterolemia (FH) patients and developed a decision aid (DA) for FH to facilitate shared decision making (SDM). Methods: Literature review, evidence synthesis and expert input were used to adapt the 30-year Framingham Risk Score (FRS) and 10-year ACC/AHA pooled cohort equations to calculate the CHD risk in individuals with FH aged 20-39 years and ≥40 years, respectively. Based on published data, estimated risk was multiplied by 2 for family history of early CHD or 3 in the presence of a pathogenic variant. Treatment benefit of lipid lowering was estimated based on the ‘cumulative exposure to LDL-C’ model ( JACC 2012; 60 ) using the following equations: Post-treatment 30-yr risk in those aged 20-39 years = Baseline Risk (FRS) x 0.54 delta LDL-C (mmol/L) Post-treatment 10-yr risk in those aged ≥40 years = Baseline Risk (ACC/AHA Pooled Cohort) x 0.72 delta LDL-C (mmol/L) To convey CHD risk and treatment benefit, we developed a DA using an iterative, patient-centered design process for use in FH encounters. Results: Using the DA, providers can discuss baseline CHD risk estimates, lipid lowering drug (LLD) options and the expected post-treatment risk reduction with FH patients. The estimated % LDL-C reduction for each LLD and the predicted risk reduction per mmol/L of LDL-C lowered were obtained from literature review. Lowering LDL-C by 1 mmol/L (39 mg/dL) was associated with a 28% CHD risk reduction over 10 years and a 46% reduction over 30 years ( Eur Heart J 2017; 38 ). We describe 2 examples of estimating CHD risk and treatment benefit in FH cases in Table 1 . Conclusion: We adapted CHD risk prediction equations to account for family history of CHD and presence of a pathogenic FH variant and designed a DA to convey personalized CHD risk estimates and treatment benefit to facilitate SDM with FH patients.

AbstractObjective Familial hypercholesterolemia (FH), a prevalent genomic disorder that increases risk of coronary heart disease, remains significantly underdiagnosed. Clinical decision support (CDS) tools have the potential to increase FH detection. We describe our experience in the development and implementation of a genomic CDS for FH at a large academic medical center.Methods CDS development and implementation were conducted in four phases: (1) development and validation of an algorithm to identify “possible FH”; (2) obtaining approvals from institutional committees to develop the CDS; (3) development of the initial prototype; and (4) use of an implementation science framework to evaluate the CDS.Results The timeline for this work was approximately 4 years; algorithm development and validation occurred from August 2018 to February 2020. During this 4-year period, we engaged with 15 stakeholder groups to build and integrate the CDS, including health care providers who gave feedback at each stage of development. During CDS implementation six main challenges were identified: (1) need for multiple institutional committee approvals; (2) need to align the CDS with institutional knowledge resources; (3) need to adapt the CDS to differing workflows; (4) lack of institutional guidelines for CDS implementation; (5) transition to a new institutional electronic health record (EHR) system; and (6) limitations of the EHR related to genomic medicine.Conclusion We identified multiple challenges in different domains while developing CDS for FH and integrating it with the EHR. The lessons learned herein may be helpful in streamlining the development and deployment of CDS to facilitate genomic medicine implementation.

Advances in genomics have the potential to improve human health [...]

Electronic health record (EHR)-based clinical decision support (CDS) can address the low awareness and undertreatment of familial hypercholesterolemia (FH), a disorder associated with a markedly increased risk of coronary heart disease. We aimed to incorporate provider perspectives into the development and implementation of a CDS tool for FH. An implementation science framework and a user-centered design process were used to create a CDS tool for FH. Primary care physicians and specialist physicians participated in qualitative interviews, usability testing and an implementation survey. The CDS was configured in two formats—a best practice alert (BPA) and an in-basket message and subsequently deployed in the EHR in silent mode. The key themes that emerged from the analysis of interview transcripts included understanding and awareness of FH, clinical workflow, physician preferences and value of CDS tools, perspectives on patient needs and values and dissemination and implementation. Recommendations related to usability included preferred CDS format and placement, content, timing and frequency, and level of alert urgency/prioritization. In response to the survey, 84.6% of physicians agreed that the CDS would improve early FH diagnosis and 92.3% agreed that it would help them identify and manage FH patients. Physician feedback led to iterative CDS refinement. In summary, we developed a CDS tool for FH using an implementation science framework and physician feedback. Initial deployment revealed a significant burden of FH and the potential for the CDS tool to have a large impact.

Background: The prevalence of familial hypercholesterolemia is 1 in 250, but <10% of patients are diagnosed. Cascade testing enables early detection of cases through systematic family tracing. Establishment of familial hypercholesterolemia cascade testing programs in the US could be informed by approaches used elsewhere. Methods: We conducted a systematic review of published studies in the English language of cascade testing for familial hypercholesterolemia, which reported the number of index cases and number of relatives tested and specified methods of contacting relatives and testing modalities methods utilized. For each study, we calculated yield (proportion of relatives who test positive) and new cases per index case, to facilitate comparison. Results: We identified 10 studies from the literature that met inclusion criteria; the mean number of probands and relatives per study was 242 and 826, respectively. The average yield was 44.76% with a range of 30% to 60.5%, and the mean new cases per index case was 1.65 with a range of 0.22 to 8.0. New cases per index case tended to be greater in studies that used direct contact versus indirect contact (2.06 versus 0.86), tested beyond first-degree relatives versus only first-degree relatives (3.65 versus 0.80), used active sample collection versus collection at clinic (4.11 versus 1.06), and utilized genetic testing versus biochemical testing (2.47 versus 0.42). Conclusions: New case detection in familial hypercholesterolemia cascade testing programs tended to be higher with direct contact of relatives, testing beyond first-degree relatives, in-home–based sample collection, and genetic testing. These findings should be helpful for establishing cascade testing programs in the United States.