The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care.

Polygenic risk scores (PRSs) hold promise for disease risk assessment and prevention. The Genomic Medicine at Veterans Affairs (GenoVA) Study is addressing three main challenges to the clinical implementation of PRSs in preventive care: defining and determining their clinical utility, implementing them in time-constrained primary care settings, and countering their potential to exacerbate healthcare disparities. The study processes used to test patients, report their PRS results to them and their primary care providers (PCPs), and promote the use of those results in clinical decision-making are modeled on common practices in primary care. The following diseases were chosen for their prevalence and familiarity to PCPs: coronary artery disease; type 2 diabetes; atrial fibrillation; and breast, colorectal, and prostate cancers. A randomized clinical trial (RCT) design and primary outcome of time-to-new-diagnosis of a target disease bring methodological rigor to the question of the clinical utility of PRS implementation. The study's pragmatic RCT design enhances its relevance to how PRS might reasonably be implemented in primary care. Steps the study has taken to promote health equity include the thoughtful handling of genetic ancestry in PRS construction and reporting and enhanced recruitment strategies to address underrepresentation in research participation. To date, enhanced recruitment efforts have been both necessary and successful: participants of underrepresented race and ethnicity groups have been less likely to enroll in the study than expected but ultimately achieved proportional representation through targeted efforts. The GenoVA Study experience to date offers insights for evaluating the clinical utility of equitable PRS implementation in adult primary care.

Design and pilot results from the Million Veteran Program Return Of Actionable Results (MVP-ROAR) Study.

BACKGROUND: As a mega-biobank linked to a national healthcare system, the Million Veteran Program (MVP) can directly improve the health care of participants. To determine the feasibility and outcomes of returning medically actionable genetic results to MVP participants, the program launched the MVP Return of Actionable Results (MVP-ROAR) Study, with familial hypercholesterolemia (FH) as an exemplar actionable condition. METHODS: The MVP-ROAR Study consists of a completed single-arm pilot phase and an ongoing randomized clinical trial (RCT), in which MVP participants are recontacted and invited to receive clinical confirmatory gene sequencing testing and a telegenetic counseling intervention. The primary outcome of the RCT is 6-month change in low-density lipoprotein cholesterol (LDL-C) between participants receiving results at baseline and those receiving results after 6 months. RESULTS: The pilot developed processes to identify and recontact participants nationally with probable pathogenic variants in low-density lipoprotein receptor (LDLR) on the MVP genotype array, invite them to clinical confirmatory gene sequencing, and deliver a telegenetic counseling intervention. Among participants in the pilot phase, 8 (100%) had active statin prescriptions after 6 months. Results were shared with 16 first-degree family members. Six-month ΔLDL-C (low-density lipoprotein cholesterol) after the genetic counseling intervention was -37 mg/dL (95% CI: -12 to -61; P = .03). The ongoing RCT will determine between-arm differences in this primary outcome. CONCLUSION: While underscoring the importance of clinical confirmation of research results, the pilot phase of the MVP-ROAR Study marks a turning point in MVP and demonstrates the feasibility of returning genetic results to participants and their providers. The ongoing RCT will contribute to understanding how such a program might improve patient health care and outcomes. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov ID NCT04178122.

Returning Individual Research Results from Digital Phenotyping in Psychiatry.

Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants' locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant's real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas.

Primary care physician use of patient race and polygenic risk scores in medical decision-making.

PURPOSE: The use of patient race in medicine is controversial for its potential either to exacerbate or address health disparities. Polygenic risk scores (PRSs) have emerged as a tool for risk stratification models used in preventive medicine. We examined whether PRS results affect primary care physician (PCP) medical decision-making and whether that effect varies by patient race. METHODS: Using an online survey with a randomized experimental design among PCPs in a national database, we ascertained decision-making around atherosclerotic cardiovascular disease prevention and prostate cancer screening for case scenario patients who were clinically identical except for randomized reported race. RESULTS: Across 369 PCPs (email open rate = 10.8%, partial completion rate = 93.7%), recommendations varied with PRS results in expected directions (low-risk results, no available PRS results, and high-risk results). Still, physicians randomized to scenarios with Black patients were more likely to recommend statin therapy than those randomized to scenarios with White patients (odds ratio = 1.74, 95% CI = 1.16-2.59, P = .007) despite otherwise identical clinical profiles and independent of PRS results. Similarly, physicians were more likely to recommend prostate cancer screening for Black patients than for White patients (odds ratio = 1.58, 95% CI = 1.06-2.35, P = .025) despite otherwise identical clinical and genetic profiles. CONCLUSION: Despite advances in precision risk stratification, physicians will likely continue to use patient race implicitly or explicitly in medical decision-making.

The Responsibility to Recontact Research Participants after Reinterpretation of Genetic and Genomic Research Results.

The evidence base supporting genetic and genomic sequence-variant interpretations is continuously evolving. An inherent consequence is that a variant's clinical significance might be reinterpreted over time as new evidence emerges regarding its pathogenicity or lack thereof. This raises ethical, legal, and financial issues as to whether there is a responsibility to recontact research participants to provide updates on reinterpretations of variants after the initial analysis. There has been discussion concerning the extent of this obligation in the context of both research and clinical care. Although clinical recommendations have begun to emerge, guidance is lacking on the responsibilities of researchers to inform participants of reinterpreted results. To respond, an American Society of Human Genetics (ASHG) workgroup developed this position statement, which was approved by the ASHG Board in November 2018. The workgroup included representatives from the National Society of Genetic Counselors, the Canadian College of Medical Genetics, and the Canadian Association of Genetic Counsellors. The final statement includes twelve position statements that were endorsed or supported by the following organizations: Genetic Alliance, European Society of Human Genetics, Canadian Association of Genetic Counsellors, American Association of Anthropological Genetics, Executive Committee of the American Association of Physical Anthropologists, Canadian College of Medical Genetics, Human Genetics Society of Australasia, and National Society of Genetic Counselors.

Should You Recommend Direct-to-Consumer Genetic Testing for This Patient? : Grand Rounds Discussion From Beth Israel Deaconess Medical Center.

In recent years, the number of patients choosing to have direct-to-consumer (DTC) genetic testing without involving their clinicians has increased substantially. For example, the number of subscribers to a commonly used testing site has grown to more than 10 million. These services have been heavily marketed in the United States and often include information about ancestry; genetic traits; and, increasingly, disease risk. In clinical care, genetic testing by a physician is accompanied by both pre- and posttest counseling by a trained genetic counselor. However, there are not enough genetic counselors to meet the needs of all persons contemplating DTC genetic testing. Formal genetic counseling includes preparation of a family pedigree; a discussion about potential benefits, the possibility that some information might be stressful to receive or difficult to understand, and the potential for disclosure of genetic information; and a detailed informed consent process. Some DTC tests for genetic susceptibilities look for only a few known mutations in a particular gene (such as BRCA1); a negative test result does not exclude the possibility of a clinically important mutation. A positive DTC genetic test result that might change clinical management should be followed by a confirmatory test through a genetics laboratory. Here, 2 expert physicians-a general internist and a medical oncologist with genetics experience-discuss an approach to counseling a patient who is considering DTC testing to learn more about his ancestry and his risk for metabolic syndrome.

How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19.

Clinical trials of treatments for coronavirus disease 2019 (COVID-19) draw intense public attention. More than ever, valid, transparent, and intuitive summaries of the treatment effects, including efficacy and harm, are needed. In recently published and ongoing randomized comparative trials evaluating treatments for COVID-19, time to a positive outcome, such as recovery or improvement, has repeatedly been used as either the primary or key secondary end point. Because patients may die before recovery or improvement, data analysis of this end point faces a competing risk problem. Commonly used survival analysis techniques, such as the Kaplan-Meier method, often are not appropriate for such situations. Moreover, almost all trials have quantified treatment effects by using the hazard ratio, which is difficult to interpret for a positive event, especially in the presence of competing risks. Using 2 recent trials evaluating treatments (remdesivir and convalescent plasma) for COVID-19 as examples, a valid, well-established yet underused procedure is presented for estimating the cumulative recovery or improvement rate curve across the study period. Furthermore, an intuitive and clinically interpretable summary of treatment efficacy based on this curve is also proposed. Clinical investigators are encouraged to consider applying these methods for quantifying treatment effects in future studies of COVID-19.

Association of Statin Use With All-Cause and Cardiovascular Mortality in US Veterans 75 Years and Older.

Importance: Data are limited regarding statin therapy for primary prevention of atherosclerotic cardiovascular disease (ASCVD) in adults 75 years and older. Objective: To evaluate the role of statin use for mortality and primary prevention of ASCVD in veterans 75 years and older. Design, Setting, and Participants: Retrospective cohort study that used Veterans Health Administration (VHA) data on adults 75 years and older, free of ASCVD, and with a clinical visit in 2002-2012. Follow-up continued through December 31, 2016. All data were linked to Medicare and Medicaid claims and pharmaceutical data. A new-user design was used, excluding those with any prior statin use. Cox proportional hazards models were fit to evaluate the association of statin use with outcomes. Analyses were conducted using propensity score overlap weighting to balance baseline characteristics. Exposures: Any new statin prescription. Main Outcomes and Measures: The primary outcomes were all-cause and cardiovascular mortality. Secondary outcomes included a composite of ASCVD events (myocardial infarction, ischemic stroke, and revascularization with coronary artery bypass graft surgery or percutaneous coronary intervention). Results: Of 326 981 eligible veterans (mean [SD] age, 81.1 [4.1] years; 97% men; 91% white), 57 178 (17.5%) newly initiated statins during the study period. During a mean follow-up of 6.8 (SD, 3.9) years, a total 206 902 deaths occurred including 53 296 cardiovascular deaths, with 78.7 and 98.2 total deaths/1000 person-years among statin users and nonusers, respectively (weighted incidence rate difference [IRD]/1000 person-years, -19.5 [95% CI, -20.4 to -18.5]). There were 22.6 and 25.7 cardiovascular deaths per 1000 person-years among statin users and nonusers, respectively (weighted IRD/1000 person-years, -3.1 [95 CI, -3.6 to -2.6]). For the composite ASCVD outcome there were 123 379 events, with 66.3 and 70.4 events/1000 person-years among statin users and nonusers, respectively (weighted IRD/1000 person-years, -4.1 [95% CI, -5.1 to -3.0]). After propensity score overlap weighting was applied, the hazard ratio was 0.75 (95% CI, 0.74-0.76) for all-cause mortality, 0.80 (95% CI, 0.78-0.81) for cardiovascular mortality, and 0.92 (95% CI, 0.91-0.94) for a composite of ASCVD events when comparing statin users with nonusers. Conclusions and Relevance: Among US veterans 75 years and older and free of ASCVD at baseline, new statin use was significantly associated with a lower risk of all-cause and cardiovascular mortality. Further research, including from randomized clinical trials, is needed to more definitively determine the role of statin therapy in older adults for primary prevention of ASCVD.

Pharmacogenetic testing in the Veterans Health Administration (VHA): policy recommendations from the VHA Clinical Pharmacogenetics Subcommittee.

PURPOSE: The Veterans Health Administration (VHA) Clinical Pharmacogenetics Subcommittee is charged with making recommendations about whether specific pharmacogenetic tests should be used in healthcare at VHA facilities. We describe a process to inform VHA pharmacogenetic testing policy. METHODS: After developing consensus definitions of clinical validity and utility, the Subcommittee identified salient drug-gene pairs with potential clinical application in VHA. Members met monthly to discuss each drug-gene pair, the evidence of clinical utility for the associated pharmacogenetic test, and any VHA-specific testing considerations. The Subcommittee classified each test as strongly recommended, recommended, or not routinely recommended before drug initiation. RESULTS: Of 30 drug-gene pair tests reviewed, the Subcommittee classified 4 (13%) as strongly recommended, including HLA-B*15:02 for carbamazepine-associated Stevens-Johnston syndrome and G6PD for rasburicase-associated hemolytic anemia; 12 (40%) as recommended, including CYP2D6 for codeine toxicity; and 14 (47%) as not routinely recommended, such as CYP2C19 for clopidogrel dosing. CONCLUSION: Only half of drug-gene pairs with high clinical validity received Subcommittee support for policy promoting their widespread use across VHA. The Subcommittee generally found insufficient evidence of clinical utility or available, effective alternative strategies for the remainders. Continual evidence review and rigorous outcomes research will help promote the translation of pharmacogenetic discovery to healthcare.

Correction: Secondary findings from clinical genomic sequencing: prevalence, patient perspectives, family history assessment, and health-care costs from a multisite study.

The originally published version of this Article contained errors in Fig. 2. The numbers below the black arrowheads were incorrect; please see incorrect Figure in associated Correction. These errors have now been corrected in the PDF and HTML versions of the Article.

Secondary findings from clinical genomic sequencing: prevalence, patient perspectives, family history assessment, and health-care costs from a multisite study.

PURPOSE: Clinical sequencing emerging in health care may result in secondary findings (SFs). METHODS: Seventy-four of 6240 (1.2%) participants who underwent genome or exome sequencing through the Clinical Sequencing Exploratory Research (CSER) Consortium received one or more SFs from the original American College of Medical Genetics and Genomics (ACMG) recommended 56 gene-condition pair list; we assessed clinical and psychosocial actions. RESULTS: The overall adjusted prevalence of SFs in the ACMG 56 genes across the CSER consortium was 1.7%. Initially 32% of the family histories were positive, and post disclosure, this increased to 48%. The average cost of follow-up medical actions per finding up to a 1-year period was $128 (observed, range: $0-$678) and $421 (recommended, range: $141-$1114). Case reports revealed variability in the frequency of and follow-up on medical recommendations patients received associated with each SF gene-condition pair. Participants did not report adverse psychosocial impact associated with receiving SFs; this was corroborated by 18 participant (or parent) interviews. All interviewed participants shared findings with relatives and reported that relatives did not pursue additional testing or care. CONCLUSION: Our results suggest that disclosure of SFs shows little to no adverse impact on participants and adds only modestly to near-term health-care costs; additional studies are needed to confirm these findings.

Short-term costs of integrating whole-genome sequencing into primary care and cardiology settings: a pilot randomized trial.

PURPOSE: Great uncertainty exists about the costs associated with whole-genome sequencing (WGS). METHODS: One hundred cardiology patients with cardiomyopathy diagnoses and 100 ostensibly healthy primary care patients were randomized to receive a family-history report alone or with a WGS report. Cardiology patients also reviewed prior genetic test results. WGS costs were estimated by tracking resource use and staff time. Downstream costs were estimated by identifying services in administrative data, medical records, and patient surveys for 6 months. RESULTS: The incremental cost per patient of WGS testing was $5,098 in cardiology settings and $5,073 in primary care settings compared with family history alone. Mean 6-month downstream costs did not differ statistically between the control and WGS arms in either setting (cardiology: difference = -$1,560, 95% confidence interval -$7,558 to $3,866, p = 0.36; primary care: difference = $681, 95% confidence interval -$884 to $2,171, p = 0.70). Scenario analyses showed the cost reduction of omitting or limiting the types of secondary findings was less than $69 and $182 per patient in cardiology and primary care, respectively. CONCLUSION: Short-term costs of WGS were driven by the costs of sequencing and interpretation rather than downstream health care. Disclosing additional types of secondary findings has a limited cost impact following disclosure.

How Primary Care Providers Talk to Patients about Genome Sequencing Results: Risk, Rationale, and Recommendation.

BACKGROUND: Genomics will play an increasingly prominent role in clinical medicine. OBJECTIVE: To describe how primary care physicians (PCPs) discuss and make clinical recommendations about genome sequencing results. DESIGN: Qualitative analysis. PARTICIPANTS: PCPs and their generally healthy patients undergoing genome sequencing. APPROACH: Patients received clinical genome reports that included four categories of results: monogenic disease risk variants (if present), carrier status, five pharmacogenetics results, and polygenic risk estimates for eight cardiometabolic traits. Patients' office visits with their PCPs were audio-recorded, and summative content analysis was used to describe how PCPs discussed genomic results. KEY RESULTS: For each genomic result discussed in 48 PCP-patient visits, we identified a "take-home" message (recommendation), categorized as continuing current management, further treatment, further evaluation, behavior change, remembering for future care, or sharing with family members. We analyzed how PCPs came to each recommendation by identifying 1) how they described the risk or importance of the given result and 2) the rationale they gave for translating that risk into a specific recommendation. Quantitative analysis showed that continuing current management was the most commonly coded recommendation across results overall (492/749, 66%) and for each individual result type except monogenic disease risk results. Pharmacogenetics was the most common result type to prompt a recommendation to remember for future care (94/119, 79%); carrier status was the most common type prompting a recommendation to share with family members (45/54, 83%); and polygenic results were the most common type prompting a behavior change recommendation (55/58, 95%). One-fifth of recommendation codes associated with monogenic results were for further evaluation (6/24, 25%). Rationales for these recommendations included patient context, family context, and scientific/clinical limitations of sequencing. CONCLUSIONS: PCPs distinguish substantive differences among categories of genome sequencing results and use clinical judgment to justify continuing current management in generally healthy patients with genomic results.

Yield and bias in defining a cohort study baseline from electronic health record data.

AIMS: Despite growing interest in using electronic health records (EHR) to create longitudinal cohort studies, the distribution and missingness of EHR data might introduce selection bias and information bias to such analyses. We aimed to examine the yield and potential for these healthcare process biases in defining a study baseline using EHR data, using the example of cholesterol and blood pressure (BP) measurements. METHODS: We created a virtual cohort study of cardiovascular disease (CVD) from patients with eligible cholesterol profiles in the New England (NE) and Southeast (SE) networks of the Veterans Health Administration in the United States. Using clinical data from the EHR, we plotted the yield of patients with BP measurements within an expanding timeframe around an index date of cholesterol testing. We compared three groups: (1) patients with BP from the exact index date; (2) patients with BP not on the index date but within the network-specific 90th percentile around the index date; and (3) patients with no BP within the network-specific 90th percentile. RESULTS: Among 589,361 total patients in the two networks, 146,636 (61.0%) of 240,479 patients from NE and 289,906 (83.1%) of 348,882 patients from SE had BP measurements on the index date. Ninety percent had BP measured within 11 days of the index date in NE and within 5 days of the index date in SE. Group 3 in both networks had fewer available race data, fewer comorbidities and CVD medications, and fewer health system encounters. CONCLUSIONS: Requiring same-day risk factor measurement in the creation of a virtual CVD cohort study from EHR data might exclude 40% of eligible patients, but including patients with infrequent visits might introduce bias. Data visualization can inform study-specific strategies to address these challenges for the research use of EHR data.

The Impact of Whole-Genome Sequencing on the Primary Care and Outcomes of Healthy Adult Patients: A Pilot Randomized Trial.

Background: Whole-genome sequencing (WGS) in asymptomatic adults might prevent disease but increase health care use without clinical value. Objective: To describe the effect on clinical care and outcomes of adding WGS to standardized family history assessment in primary care. Design: Pilot randomized trial. (ClinicalTrials.gov: NCT01736566). Setting: Academic primary care practices. Participants: 9 primary care physicians (PCPs) and 100 generally healthy patients recruited at ages 40 to 65 years. Intervention: Patients were randomly assigned to receive a family history report alone (FH group) or in combination with an interpreted WGS report (FH + WGS group), which included monogenic disease risk (MDR) results (associated with Mendelian disorders), carrier variants, pharmacogenomic associations, and polygenic risk estimates for cardiometabolic traits. Each patient met with his or her PCP to discuss the report. Measurements: Clinical outcomes and health care use through 6 months were obtained from medical records and audio-recorded discussions between PCPs and patients. Patients' health behavior changes were surveyed 6 months after receiving results. A panel of clinician-geneticists rated the appropriateness of how PCPs managed MDR results. Results: Mean age was 55 years; 58% of patients were female. Eleven FH + WGS patients (22% [95% CI, 12% to 36%]) had new MDR results. Only 2 (4% [CI, 0.01% to 15%]) had evidence of the phenotypes predicted by an MDR result (fundus albipunctatus due to RDH5 and variegate porphyria due to PPOX). Primary care physicians recommended new clinical actions for 16% (CI, 8% to 30%) of FH patients and 34% (CI, 22% to 49%) of FH + WGS patients. Thirty percent (CI, 17% to 45%) and 41% (CI, 27% to 56%) of FH and FH + WGS patients, respectively, reported making a health behavior change after 6 months. Geneticists rated PCP management of 8 MDR results (73% [CI, 39% to 99%]) as appropriate and 2 results (18% [CI, 3% to 52%]) as inappropriate. Limitation: Limited sample size and ancestral and socioeconomic diversity. Conclusion: Adding WGS to primary care reveals new molecular findings of uncertain clinical utility. Nongeneticist providers may be able to manage WGS results appropriately, but WGS may prompt additional clinical actions of unclear value. Primary Funding Source: National Institutes of Health.

Prescription medication changes following direct-to-consumer personal genomic testing: findings from the Impact of Personal Genomics (PGen) Study.

PURPOSE: To measure the frequency of prescription medication changes following direct-to-consumer personal genomic testing (DTC-PGT) and their association with the pharmacogenomic results received. METHODS: New DTC-PGT customers were enrolled in 2012 and completed surveys prior to the return of results and 6 months after results; DTC-PGT results were linked to survey data. "Atypical response" pharmacogenomic results were defined as those indicating an increase or decrease in risk of an adverse drug event or likelihood of therapeutic benefit. At follow-up, participants reported prescription medication changes and health-care provider consultation. RESULTS: Follow-up data were available from 961 participants, of whom 54 (5.6%) reported changing a medication they were taking or starting a new medication due to their DTC-PGT results. Of these, 45 (83.3%) reported consulting with a health-care provider regarding the change. Pharmacogenomic results were available for 961 participants, of which 875 (91.2%) received one or more atypical response results. For each such result received, the odds of reporting a prescription medication change increased 1.57 times (95% confidence interval = 1.17, 2.11). CONCLUSION: Receipt of pharmacogenomic results indicating an atypical drug response is common with DTC-PGT and is associated with prescription medication changes; however, fewer than 1% of consumers report unsupervised changes at 6 months after testing.Genet Med advance online publication 22 September 2016.