Miller, Alexandra

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.

Background: The Clinical Genome Resource Consortium (ClinGen) recommends MaxEntScan (MES) model to predict the effects of LDLR splice variants. We developed “MaxSpliZer”, a software tool to automate the implementation of MES, and validated it using ClinVar and UK-Biobank (UKBB) data. Method: We tested concordance of MaxSpliZer predictions with ClinVar classifications of benign/likely benign (B/LB) and pathogenic/likely pathogenic (P/LP) for LDLR variants with potential effect on splicing. We also annotated LDLR splice variants in 200,618 UKBB participants, categorizing them using MaxSpliZer as deleterious (n=90) and non-deleterious (n=7,404). Low-density lipoprotein cholesterol (LDL-C) levels were compared in these two groups after adjustment for lipid-lowering medication use. Results: MaxSpliZer prediction was concordant with the ClinVar classification in 128 of 138 P/LP variants (sensitivity 93%) and 432 of 436 B/LB variants (specificity 99%). In the UKBB, splice variants predicted as deleterious by MaxSpliZer had higher LDL-C than non-deleterious splice variants (158.7±47.4 vs. 146.0±34.8mg/dL, p-value = 0.014). Compared to manual curation time of 12±7 min per variant, MaxSpliZer took 0.52±0.11 min for single entries and 1.5 s per variant for biobank-scale data. Conclusion: MaxSpliZer, a software tool that implements MES based on the ClinGen guideline, can accurately classify LDLR splice variants in a rapid automated fashion.