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Implementation of polygenic risk scores (PRS) may improve disease prevention and management but poses several challenges: the construction of clinically valid assays, interpretation for individual patients, and the development of clinical workflows and resources to support their use in patient care. For the ongoing Veterans Affairs Genomic Medicine at Veterans Affairs (GenoVA) Study we developed a clinical genotype array-based assay for six published PRS. We used data from 36,423 Mass General Brigham Biobank participants and adjustment for population structure to replicate known PRS–disease associations and published PRS thresholds for a disease odds ratio (OR) of 2 (ranging from 1.75 (95% CI: 1.57–1.95) for type 2 diabetes to 2.38 (95% CI: 2.07–2.73) for breast cancer). After confirming the high performance and robustness of the pipeline for use as a clinical assay for individual patients, we analyzed the first 227 prospective samples from the GenoVA Study and found that the frequency of PRS corresponding to published OR > 2 ranged from 13/227 (5.7%) for colorectal cancer to 23/150 (15.3%) for prostate cancer. In addition to the PRS laboratory report, we developed physician- and patient-oriented informational materials to support decision-making about PRS results. Our work illustrates the generalizable development of a clinical PRS assay for multiple conditions and the technical, reporting and clinical workflow challenges for implementing PRS information in the clinic.

Risk prediction models for cardiovascular disease (CVD) death developed from patients without vascular disease may not be suitable for myocardial infarction (MI) survivors. Prediction of mortality risk after MI may help to guide secondary prevention. Using national electronic record data from the Veterans Health Administration 2002 to 2012, we developed risk prediction models for CVD death and all-cause death based on 5-year follow-up data of 100,601 survivors of MI using Cox proportional hazards models. Model performance was evaluated using a cross-validation approach. During follow-up, there were 31,622 deaths and 12,901 CVD deaths. In men, older age, current smoking, atrial fibrillation, heart failure, peripheral artery disease, and lower body mass index were associated with greater risk of death from CVD or all-causes, and statin treatment, hypertension medication, estimated glomerular filtration rate level, and high body mass index were significantly associated with reduced risk of fatal outcomes. Similar associations and slightly different predictors were observed in women. The estimated Harrell's C-statistics of the final model versus the cross-validation estimates were 0.77 versus 0.77 in men and 0.81 versus 0.77 in women for CVD death. Similarly, the C-statistics were 0.75 versus 0.75 in men, 0.78 versus 0.75 in women for all-cause mortality. The predicted risk of death was well calibrated compared with the observed risk. In conclusion, we developed and internally validated risk prediction models of 5-year risk for CVD and all-cause death for outpatient survivors of MI. Traditional risk factors, co-morbidities, and lack of blood pressure or lipid treatment were all associated with greater risk of CVD and all-cause mortality.

ImportancePrimary prevention of atherosclerotic cardiovascular disease (ASCVD) relies on risk stratification. Genome-wide polygenic risk scores (PRSs) are proposed to improve ASCVD risk estimation.ObjectiveTo determine whether genome-wide PRSs for coronary artery disease (CAD) and acute ischemic stroke improve ASCVD risk estimation with traditional clinical risk factors in an ancestrally diverse midlife population.Design, Setting, and ParticipantsThis was a prognostic analysis of incident events in a retrospectively defined longitudinal cohort conducted from January 1, 2011, to December 31, 2018. Included in the study were adults free of ASCVD and statin naive at baseline from the Million Veteran Program (MVP), a mega biobank with genetic, survey, and electronic health record data from a large US health care system. Data were analyzed from March 15, 2021, to January 5, 2023.ExposuresPRSs for CAD and ischemic stroke derived from cohorts of largely European descent and risk factors, including age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein (HDL) cholesterol, smoking, and diabetes status.Main Outcomes and MeasuresIncident nonfatal myocardial infarction (MI), ischemic stroke, ASCVD death, and composite ASCVD events.ResultsA total of 79 151 participants (mean [SD] age, 57.8 [13.7] years; 68 503 male [86.5%]) were included in the study. The cohort included participants from the following harmonized genetic ancestry and race and ethnicity categories: 18 505 non-Hispanic Black (23.4%), 6785 Hispanic (8.6%), and 53 861 non-Hispanic White (68.0%) with a median (5th-95th percentile) follow-up of 4.3 (0.7-6.9) years. From 2011 to 2018, 3186 MIs (4.0%), 1933 ischemic strokes (2.4%), 867 ASCVD deaths (1.1%), and 5485 composite ASCVD events (6.9%) were observed. CAD PRS was associated with incident MI in non-Hispanic Black (hazard ratio [HR], 1.10; 95% CI, 1.02-1.19), Hispanic (HR, 1.26; 95% CI, 1.09-1.46), and non-Hispanic White (HR, 1.23; 95% CI, 1.18-1.29) participants. Stroke PRS was associated with incident stroke in non-Hispanic White participants (HR, 1.15; 95% CI, 1.08-1.21). A combined CAD plus stroke PRS was associated with ASCVD deaths among non-Hispanic Black (HR, 1.19; 95% CI, 1.03-1.17) and non-Hispanic (HR, 1.11; 95% CI, 1.03-1.21) participants. The combined PRS was also associated with composite ASCVD across all ancestry groups but greater among non-Hispanic White (HR, 1.20; 95% CI, 1.16-1.24) than non-Hispanic Black (HR, 1.11; 95% CI, 1.05-1.17) and Hispanic (HR, 1.12; 95% CI, 1.00-1.25) participants. Net reclassification improvement from adding PRS to a traditional risk model was modest for the intermediate risk group for composite CVD among men (5-year risk >3.75%, 0.38%; 95% CI, 0.07%-0.68%), among women, (6.79%; 95% CI, 3.01%-10.58%), for age older than 55 years (0.25%; 95% CI, 0.03%-0.47%), and for ages 40 to 55 years (1.61%; 95% CI, −0.07% to 3.30%).Conclusions and RelevanceStudy results suggest that PRSs derived predominantly in European samples were statistically significantly associated with ASCVD in the multiancestry midlife and older-age MVP cohort. Overall, modest improvement in discrimination metrics were observed with addition of PRSs to traditional risk factors with greater magnitude in women and younger age groups.

Background: The lipid hypothesis postulates that lower blood cholesterol is associated with reduced coronary heart disease (CHD) risk, which has been challenged by recent studies that observed a U-shaped relation between cholesterol and mortality. The effect of low cholesterol and CHD risk among Veterans is unclear. Methods: This prospective cohort study included Veterans greater than 18 years of age with baseline outpatient visits from 2002 to 2007 and follow-up to December 30, 2018 in the Veterans Health Administration electronic health record system. Veterans were followed to assess CHD mortality risk in relation to outpatient blood cholesterol levels. We used Cox proportional hazard regression to estimate the hazard ratio (HR) and 95% confidence interval (CI) of CHD mortality associated with total cholesterol (TC). Results: Among 4,467,942 Veterans, 381,871 CHD deaths were recorded. We observed a V-shaped relation between TC and age-, sex, race and smoking-adjusted risk of CHD mortality. The association became U-shaped after adjustment for statin use, body mass index, hypertension, and diabetes. When further adjusted for high-density lipoprotein level, 11 baseline diseases, and applying a 2-year lag analysis, the relation to CHD mortality was J-shaped--flat for TC Conclusions: Based on prospective data for almost 4.5 million adult Veterans, CHD mortality risk steadily increased for TC ≥200mg/dL after adjustment for a range of health conditions. Our results support the lipid hypothesis that lower blood cholesterol is associated with reduced CHD risk and lower prevalence of multimorbidity, mental health disorders, nutritional deficits, and other risk factors for CHD. Furthermore, the hypothesis remained true when TC was low due to use of statins or other lipid-lowering medication. The changes in risk for CHD mortality by TC groups observed in our study (L- to U- to J-shape) highlights the importance of fully adjusting for the presence of multimorbidity and HDL-C to avoid misleading conclusions.

Aim: The first Plan-Do-Study-Act cycle for the Veterans Affairs Pharmacogenomic Testing for Veterans pharmacogenomic clinical testing program is described. Materials & methods: Surveys evaluating implementation resources and processes were distributed to implementation teams, providers, laboratory and health informatics staff. Survey responses were mapped to the Consolidated Framework for Implementation Research constructs to identify implementation barriers. The Expert Recommendation for Implementing Change strategies were used to address implementation barriers. Results: Survey response rate was 23–73% across personnel groups at six Veterans Affairs sites. Nine Consolidated Framework for Implementation Research constructs were most salient implementation barriers. Program revisions addressed these barriers using the Expert Recommendation for Implementing Change strategies related to three domains. Conclusion: Beyond providing free pharmacogenomic testing, additional implementation barriers need to be addressed for improved program uptake.

High dimensional predictive models of Major Adverse Cardiac Events (MACE), which includes heart attack (AMI), stroke, and death caused by cardiovascular disease (CVD), were built using four longitudinal cohorts of Veterans Administration (VA) patients created from VA medical records. We considered 247 variables / risk factors measured across 7.5 years for millions of patients in order to compare predictions for the first reported MACE event using six distinct modelling methodologies. The best-performing methodology varied across the four cohorts. Model coefficients related to disease pathophysiology and treatment were relatively constant across cohorts, while coefficients dependent upon the confounding variables of age and healthcare utilization varied considerably across cohorts. In particular, models trained on a retrospective case-control (Rcc) cohort (where controls are matched to cases by date of birth cohort and overall level of healthcare utilization) emphasize variables describing pathophysiology and treatment, while predictions based on the cohort of all active patients at the start of 2017 (C-17) rely much more on age and variables reflecting healthcare utilization. In consequence, directly using an Rcc-trained model to evaluate the C-17 cohort resulted in poor performance (C-statistic = 0.65). However, a simple reoptimization of model dependence on age, demographics, and five other variables improved the C-statistic to 0.74, nearly matching the 0.76 obtained on C-17 by a C-17-trained model. Dependence of MACE risk on biomarkers for hypertension, cholesterol, diabetes, body mass index, and renal function in our models was consistent with the literature. At the same time, including medications and procedures provided important indications of both disease severity and the level of treatment. More detailed study designs will be required to disentangle these effects.

BackgroundClinical implementation of polygenic risk scores (PRS) for precision prevention depends on the utility and barriers primary care physicians (PCPs) perceive to their use.MethodsAn online survey asked PCPs in a national database about the clinical utility of PRS they perceived for categories of medical decision-making and perceived benefits of and barriers to that use. Latent class analysis (LCA) was used to identify subgroups of PCPs based on response patterns.ResultsAmong 367 respondents (email open rate 10.8%; participation rate 96.3%; completion rate 93.1%), mean (SD) age was 54.9 (12.9) years, 137 (37.3%) were female, and mean (SD) time since medical school graduation was 27.2 (13.3) years. Respondents reported greater perceived utility for more clinical action (e.g., earlier or more intensive screening, preventive medications, or lifestyle modification) for patients with high-risk PRS than for delayed or discontinued prevention actions for low-risk patients (pSkeptics (n=83, 22.6%) endorsed less agreement with individual clinical utilities, saw patient anxiety and insurance discrimination as significant barriers, and agreed less often that PRS could help patients make better health decisions. Learners (n=134, 36.5%) and enthusiasts (n=150, 40.9%) expressed similar levels of agreement that PRS had utility for preventive actions and that PRS could be useful for patient decision-making. Compared with enthusiasts, however, learners perceived greater barriers to the clinical use of PRS.ConclusionPCPs generally endorsed using PRS to guide medical decision-making about preventive care, with a preference for more clinical action over less. Barriers identified suggest interventions to address the needs and concerns of PCPs along the spectrum of acceptance and uptake.

In 2019, the Veterans Affairs (VA), the largest integrated US healthcare system, started the Pharmacogenomic Testing for Veterans (PHASER) clinical program that provides multi-gene pharmacogenomic (PGx) testing for up to 250,000 veterans at approximately 50 sites. PHASER is staggering program initiation at sites over a 5-year period from 2019 to 2023, as opposed to simultaneous initiation at all sites, to facilitate iterative program quality improvements through Plan-Do-Study-Act cycles. Current resources in the PGx field have not focused on multisite, remote implementation of panel-based PGx testing. In addition to bringing large scale PGx testing to veterans, the PHASER program is developing a roadmap to maximize uptake and optimize the use of PGx to improve drug response outcomes.

PURPOSE The use of genomic sequencing (GS) in military settings poses unique considerations, including the potential for GS to impact service members' careers. The MilSeq Project investigated the use of GS in clinical care of active duty Airmen in the United States Air Force (USAF). METHODS We assessed perceived risks, benefits, and attitudes toward use of GS in the USAF among patient participants (n = 93) and health-care provider participants (HCPs) (n = 12) prior to receiving or disclosing GS results. RESULTS Participants agreed that there are health benefits associated with GS (90% patients, 75% HCPs), though more HCPs (75%) than patients (40%) agreed that there are risks (p = 0.048). The majority of both groups (67% HCPs, 77% patients) agreed that they trust the USAF with genetic information, but far fewer agreed that genetic information should be used to make decisions about deployment (5% patients, 17% HCPs) or duty assignments (3% patients, 17% HCPs). Despite their hesitancy, patients were supportive of the USAF testing for nondisease traits that could impact their duty performance. Eighty-seven percent of patients did not think their GS results would influence their career. CONCLUSION Results suggest favorable attitudes toward the use of GS in the USAF when not used for deployment or assignment decisions.

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.

Pharmacogenetics (PGx) testing can be used for detecting genetic variations that may affect an individual's anticipated metabolism of, or response to, medications. Although several studies have focused on developing tools for delivering results from PGx testing, there is a relative dearth of information about how to design provider-friendly electronic order-entry systems for PGx. The U.S. Department of Veterans Affairs (VA) is preparing to implement a new electronic health records system. In this study, VA PGx test end users were surveyed about their preferences for how electronic test orders for PGx should be structured, including the nomenclature that should be used to search for and identify PGx-test orders, whether to offer single- versus multigene tests, and whether information about test methodology should be included in the order name. Responses were analyzed systematically to identify areas of agreement and disagreement with the survey options, and areas where respondents' opinions diverged. End users endorsed preferences for flexible ways to identify and order PGx tests and multigene panel tests; opinions on whether test methodology should be included in the test name were divergent. The results could be used for both informing the VA's new electronic health records implementation (including how PGx tests are searched for and ordered) and for providing insights for other health systems implementing PGx-testing programs.

The prediction of myocardial infarction (MI), acute ischemic stroke (AIS), cardiovascular (CVD) death, and 3-point MACE (MI, AIS, or CVD death) in Million Veteran Program (MVP) participants over 8 years of follow up was evaluated using environmental/traditional risk factors (“E”), polygenic risk scores (“G”), and a combination of both approaches (“GxE”). Individuals free of atherosclerotic cardiovascular disease (ASCVD) at baseline were included. Risk factor levels for the participants close to the time of MVP enrollment for age, sex, systolic blood pressure, cholesterol, HDL cholesterol, smoking status, and diabetes status were analyzed. Outcomes were determined from VA electronic record data, Medicare/Medicaid data, and the National Death Index. Analyses were undertaken separately for non-Hispanic European (EUR), African American (AFR), and Hispanic (HIS) participants. There were 157,941 veterans at risk with 8,157 MI events, 2,024 AIS events, 1,778 CVD deaths, and 9,350 3-pt MACE events over 8 years of follow-up. The overall results showed good performance with the environmental model for all three racial-ethnic groups, with C-statistics ranging from 0.69 to 0.77. The G models showed very modest prediction capabilities and similarly modest improvement in the combined GxE models. In conclusion, traditional risk factor modeling has been shown to be highly effective and, in the MVP experience, the additional impact of genetic or genetic interaction information was small.

There is a well-validated association between SLCO1B1 (rs4149056) and statin-associated muscle symptoms (SAMS). Preemptive SLCO1B1 pharmacogenetic (PGx) testing may diminish the incidence of SAMS by identifying individuals with increased genetic risk before statin initiation. Despite its potential clinical application, the cost implications of SLCO1B1 testing are largely unknown. We conducted a cost–consequence analysis of preemptive SLCO1B1 testing (PGx+) versus usual care (PGx−) among Veteran patients enrolled in the Integrating Pharmacogenetics in Clinical Care (I-PICC) Study. The assessment was conducted using a health system perspective and 12-month time horizon. Incremental costs of SLCO1B1 testing and downstream medical care were estimated using data from the U.S. Department of Veterans Affairs’ Managerial Cost Accounting System. A decision analytic model was also developed to model 1-month cost and SAMS-related outcomes in a hypothetical cohort of 10,000 Veteran patients, where all patients were initiated on simvastatin. Over 12 months, 13.5% of PGx+ (26/193) and 11.2% of PGx− (24/215) participants in the I-PICC Study were prescribed Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline-concordant statins (Δ2.9%, 95% CI −4.0% to 10.0%). Differences in mean per-patient costs for lipid therapy prescriptions, including statins, for PGx+ compared to PGx− participants were not statistically significant (Δ USD 9.53, 95% CI −0.86 to 22.80 USD). Differences in per-patient costs attributable to the intervention, including PGx testing, lipid-lowering prescriptions, SAMS, laboratory and imaging expenses, and primary care and cardiology services, were also non-significant (Δ− USD 1004, 95% CI −2684 to 1009 USD). In the hypothetical cohort, SLCO1B1-informed statin therapy averted 109 myalgias and 3 myopathies at 1-month follow up. Fewer statin discontinuations (78 vs. 109) were also observed, but the SLCO1B1 testing strategy was 96 USD more costly per patient compared to no testing (124 vs. 28 USD). The implementation of SLCO1B1 testing resulted in small, non-significant increases in the proportion of patients receiving CPIC-concordant statin prescriptions within a real-world primary care context, diminished the incidence of SAMS, and reduced statin discontinuations in a hypothetical cohort of 10,000 patients. Despite these effects, SLCO1B1 testing administered as a standalone test did not result in lower per-patient health care costs at 1 month or over 1 year of treatment. The inclusion of SLCO1B1, among other well-validated pharmacogenes, into preemptive panel-based testing strategies may provide a better balance of clinical benefit and cost.

BACKGROUND AND AIMS: Observational and clinical trial evidence suggests an inverse association of omega-3 polyunsaturated fatty acid with coronary artery disease (CAD) mortality, although relationships with non-fatal CAD and stroke are less clear. We investigated whether omega-3 fatty acid supplement use and fish intake were associated with incident non-fatal CAD and ischemic stroke among US Veterans. METHODS: The Million Veteran Program (MVP) is an ongoing nation-wide longitudinal cohort study of US Veterans with self-reported survey, biospecimen, and electronic health record data. Regular use of omega-3 supplements (yes/no) and frequency of fish intake within the past year were assessed using a food frequency questionnaire. Cox proportional hazard models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the associations of omega-3 supplement use and fish intake with incident non-fatal CAD and ischemic stroke, defined from electronic health records using validated algorithms. Multivariable models included demographics, body mass index, education, smoking status, alcohol intake, and exercise frequency. RESULTS: Among 197,761 participants with food frequency data (mean age: 66 ± 12 years, 92% men), 21% regularly took omega-3 supplements and median fish intake was 1 (3–5 ounce) serving/week. Over a median follow-up of 2.9 years for non-fatal CAD and 3.3 years for non-fatal ischemic stroke, we observed 6,265 and 4,042 incident cases of non-fatal CAD and non-fatal ischemic stroke, respectively. Omega-3 fatty acid supplement use was independently associated with a lower risk of non-fatal ischemic stroke [HR (95% CI): 0.88 (0.81, 0.95)] but not non-fatal CAD [0.99 (0.93, 1.06)]. Fish intake was not independently associated with non-fatal CAD [1.01 (0.94, 1.09) for 1–3 servings/month, 1.03 (0.98, 1.11) for 1 serving/week, 1.02 (0.93, 1.11) for 2–4 servings/week, and 1.15 (0.98, 1.35) for ≥5 servings/week, reference =

Pragmatic clinical trials (PCTs) have an established presence in clinical research and yet have only recently garnered attention within the landscape of genomic medicine. Using the PRagmatic‐Explanatory Continuum Indicator Summary 2 (PRECIS‐2) as a framework, this paper illustrates the application of PCT principles to The Integrating Pharmacogenetics In Clinical Care (I‐PICC) Study, a trial of pharmacogenetic testing prior to statin initiation for cardiovascular disease prevention in primary care. The trial achieved high engagement with providers (85% enrolled of those approached) and enrolled a representative sample of participants for which statin therapy would be recommended. The I‐PICC Study has a high level of pragmatism, which should enhance the generalizability of its findings. The PRECIS‐2 may be useful in the design and evaluation of PCTs of genomic medicine interventions, contributing to the generation of evidence that can bridge the gap between genomics innovation and clinical adoption.

BACKGROUND: Although previous studies have suggested cocoa products may promote cardiovascular health in the general population, no public data are available from patients receiving care in a national integrated health care system. OBJECTIVES: We tested the hypothesis that regular chocolate consumption is associated with a lower risk of coronary artery disease (CAD) events among participants of the Million Veteran Program (MVP). Secondary analysis examined if the main hypothesis was observed among participants with type 2 diabetes. METHODS: We analyzed data from MVP participants who completed the food frequency section of the MVP Lifestyle Survey and were free of CAD at the time of survey completion. CAD events during follow-up (International Statistical Classification of Diseases Ninth Revision codes 410–411 and 413–414, and Tenth Revision codes I20–I25 except I25.2) were assessed using electronic health records. We fitted a Cox proportional hazard model to estimate the RR of CAD. RESULTS: Of 188,447 MVP enrollees with survey data, mean ± SD age was 64 ± 12.0 y and 90% were men. For regular chocolate (28.3 g/serving) consumption of

Implementation of polygenic risk scores (PRS) may improve disease prevention and management but requires the construction and validation of clinical assays, interpretation, and reporting pipelines. We developed a clinical genotype array-based assay for published PRS for 6 common diseases. First, we calculated PRS for 36,423 Mass General Brigham Biobank (MGBB) participants. Finding significant variation in the PRS distributions by race, we implemented adjustment for population structure with ancestry-informative principal components. We replicated published thresholds for odds ratio (OR)>2 in MGBB overall [ranging from 1.75 (1.57, 1.95) for Type 2 diabetes to 2.38 (2.07, 2.73) for breast cancer]. After confirming the high performance and robustness of the pipeline for use as a clinical assay, we analyzed the first 141 prospective samples from the Genomic Medicine at VA Study; frequency of PRS corresponding to published OR>2 ranged from 5/141 (3.6%) for colorectal cancer to 8/48 (16.7%) for breast cancer. Our development of a clinical PRS assay for multiple conditions illustrates the generalizability of this process and necessary technical and reporting decisions for meaningful clinical PRS implementation.

Implementation of polygenic risk scores (PRS) may improve disease prevention and management but poses several challenges: the construction of clinically valid assays, interpretation for individual patients, and the development of clinical workflows and resources to support their use in patient care. For the ongoing Veterans Affairs Genomic Medicine at Veterans Affairs (GenoVA) Study we developed a clinical genotype array-based assay for six published PRS. We used data from 36,423 Mass General Brigham Biobank participants and adjustment for population structure to replicate known PRS-disease associations and published PRS thresholds for a disease odds ratio (OR) of 2 (ranging from 1.75 (95% CI: 1.57-1.95) for type 2 diabetes to 2.38 (95% CI: 2.07-2.73) for breast cancer). After confirming the high performance and robustness of the pipeline for use as a clinical assay for individual patients, we analyzed the first 227 prospective samples from the GenoVA Study and found that the frequency of PRS corresponding to published OR 2 ranged from 13/227 (5.7%) for colorectal cancer to 23/150 (15.3%) for prostate cancer. In addition to the PRS laboratory report, we developed physician- and patient-oriented informational materials to support decision-making about PRS results. Our work illustrates the generalizable development of a clinical PRS assay for multiple conditions and the technical, reporting and clinical workflow challenges for implementing PRS information in the clinic.

Key Points Question Can pharmacogenetic results for statin myopathy risk be used clinically without the unintended harms of statin avoidance or underdosing? Findings In this randomized clinical trial including 408 patients, statin-naive patients whose physicians knew their SLCO1B1 genotype results at baseline did not have poorer low-density lipoprotein cholesterol reductions after 1 year, compared with patients who received usual care. Meaning Although these findings do not support the widespread adoption of stand-alone preemptive SLCO1B1 genotype testing, they may allay stakeholder concerns about the potential unintended harms of the clinical use of such information., This randomized clinical trial examines the impact of delivering SLCO1B1 pharmacogenetic results to physicians on low-density lipoprotein cholesterol levels and concordance with prescribing guidelines for statin safety and effectiveness., Importance Nonadherence to statin guidelines is common. The solute carrier organic anion transporter family member 1B1 (SLCO1B1) genotype is associated with simvastatin myopathy risk and is proposed for clinical implementation. The unintended harms of using pharmacogenetic information to guide pharmacotherapy remain a concern for some stakeholders. Objective To determine the impact of delivering SLCO1B1 pharmacogenetic results to physicians on the effectiveness of atherosclerotic cardiovascular disease (ASCVD) prevention (measured by low-density lipoprotein cholesterol [LDL-C] levels) and concordance with prescribing guidelines for statin safety and effectiveness. Design, Setting, and Participants This randomized clinical trial was performed from December 2015 to July 2019 at 8 primary care practices in the Veterans Affairs Boston Healthcare System. Participants included statin-naive patients with elevated ASCVD risk. Data analysis was performed from October 2019 to September 2020. Interventions SLCO1B1 genotyping and results reporting to primary care physicians at baseline (intervention group) vs after 1 year (control group). Main Outcomes and Measures The primary outcome was the 1-year change in LDL-C level. The secondary outcomes were 1-year concordance with American College of Cardiology–American Heart Association and Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for statin therapy and statin-associated muscle symptoms (SAMS). Results Among 408 patients (mean [SD] age, 64.1 [7.8] years; 25 women [6.1%]), 193 were randomized to the intervention group and 215 were randomized to the control group. Overall, 120 participants (29%) had a SLCO1B1 genotype indicating increased simvastatin myopathy risk. Physicians offered statin therapy to 65 participants (33.7%) in the intervention group and 69 participants (32.1%) in the control group. Compared with patients whose physicians did not know their SLCO1B1 results at baseline, patients whose physicians received the results had noninferior reductions in LDL-C at 12 months (mean [SE] change in LDL-C, −1.1 [1.2] mg/dL in the intervention group and −2.2 [1.3] mg/dL in the control group; difference, −1.1 mg/dL; 90% CI, −4.1 to 1.8 mg/dL; P

Introduction: CYP2C19 reduced function (RF) alleles (*2, *3) have been shown to impair clopidogrel effectiveness following percutaneous coronary intervention (PCI) in the setting of acute coronary syndromes (ACS); however, this association has not been explored in the Veterans Health Administration. Hypothesis: CYP2C19 RF alleles are associated with major adverse cardiac events (MACE) in patients treated with clopidogrel Methods: MACE and CYP2C19 genotype were determined in MVP participants who underwent PCI between 2009-2017 with 1 year follow-up. Age was restricted to Results: Among 4,490 Veterans (age 59.1 ± 5 years, 18% African American, 6% Hispanic, 44% ACS) who were prescribed clopidogrel following PCI, 1,261 (28%) had RF CYP2C19 alleles. The overall MACE rate was 7.0%. MACE was associated with ACS as initial presentation, diagnosis of diabetes, chronic kidney disease, peripheral vascular disease, congestive heart failure, history of stroke, prior MI. Among the subgroup presenting with ACS, the adjusted risk of MACE was greater in participants with RF alleles compared to those with wildtype alleles (HR 1.38, 95% CI 1.002-1.916). There was no impact of RF alleles on MACE risk in those in the non-ACS group (HR 1.00, 95% CI 0.702-1.429). Conclusions: These data from the largest integrated health system in the US demonstrate that in Veterans presenting with ACS undergoing PCI, there was a higher risk of MACE in CYP2C19 RF carriers prescribed clopidogrel vs. wildtype carriers. The impact of the CYP2C19-clopidogrel interaction was not observed in the non-ACS patients. Further studies should explore the role of pharmacogenetic testing in Veterans.

Objective: This is a large prospective study aimed to develop risk prediction models of CVD and all-cause mortality in patients who survived MI. Methods: Using 2002-2012 national electronic health record data from the Veterans Health Administration, sex-specific risk prediction models for CVD and all-cause death were developed from the 5-year follow-up data of 100,601 first MI survivors aged >30 years. Model performance was evaluated using a 5-fold cross-validation approach. Results: We followed 98,657 male and 1,944 female MI survivors up to 5 years (407,199 person-years). There were 31,622 deaths (men 31,147, women 475) and 12,901 CVD deaths (men 12,752, women 149) observed during follow up. Among men, greater age, current smoking, diabetes, atrial fibrillation, heart failure, peripheral artery disease, geographic region, and lower BMI (2 ) were associated with increased risk of subsequent CVD and all-cause-mortality, while statin treatment, hypertension medication, beta-blocker, eGFR level, and high BMI (≥25 kg/m 2 ) were significantly associated with reduced risk of CVD and all-cause-mortality. Similar associations were generally observed among women. We observed U-shaped relations between total cholesterol and outcomes, and HDL cholesterol and outcomes. The prediction models demonstrated good discrimination and calibration. The estimated Harrell’s C-statistics of the final models versus the cross-validation estimates were similar, ranging from 0.75 to 0.81. The predicted risk of death was well-calibrated compared to observed risk. Conclusions: We developed and validated risk prediction models of 5-year risk for CVD and all-cause death for patients following MI. Traditional risk factors, co-morbidity, lack of blood pressure or lipid treatment, and geographic region were all associated with greater risk of CVD and all-cause mortality.

Trials evaluating treatments for COVID-19 often use the time to a positive outcome as a key end point. In the presence of death as a competing risk, commonly used survival analysis techniques may not be appropriate. Using examples from 2 recent trials of treatments for COVID-19, the authors discuss issues with the current practice and present alternative, more clinically interpretable approaches., 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.

Publisher's version (útgefin grein), Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p, WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. The grant funding of WHI are R21 HL123677, R56 DK104806 and R01 MD012765 to NF. The FamHS was funded by R01HL118305 and R01HL117078 NHLBI grants, and 5R01DK07568102 and 5R01DK089256 NIDDK grant." and "The Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study was supported by the Intramural Research Program of the National Institute on Aging, National Institutes of Health (project # Z01-AG000513 and human subjects protocol number 09-AGN248). Support for GENOA was provided by the National Heart, Lung and Blood Institute (HL119443, HL087660, HL054464, HL054457, and HL054481) of the National Institutes of Health. Ruth loos is supported by the NIH (R01DK110113, U01HG007417, R01DK101855, R01DK107786). The Rotterdam Study GWAS datasets are supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), and the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project nr. 050-060-810. The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006- 01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the programme "Quality of Life and Management of the Living Resources" of 5th Framework Programme (no. QLG2-CT-2002- 01254). The ERF study was further supported by ENGAGE consortium and CMSB. Highthroughput analysis of the ERF data was supported by joint grant from Netherlands Organisation for Scientific Research and the Russian Foundation for Basic Research (NWORFBR 047.017.043).ERF was further supported by the ZonMw grant (project 91111025), and this work was partially supported by the National Heart, Lung and Blood Institute’s Framingham Heart Study (Contract No. N01-HC25195) and its contract with Affymetrix, Inc for genotyping services (Contract No. N02-HL-6- 4278). This study is also supported by National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) R01 DK078616 to Drs. Meigs, Dupuis and Florez, NIDDK K24 DK080140 to Dr. Meigs, and a Doris Duke Charitable Foundation Clinical Scientist Development Award to Dr. Florez. The HERITAGE Family Study was supported by National Heart, Lung, and Blood Institute grant HL-45670. The Women's Genome Health Study is supported by the National Heart, Lung, and Blood Instutute (HL043851 and HL080467) and the National Cancer Institute (CA047988 and UM1CA182913). Additional support for endpoint collection was provided by the National Heart, Lung, and Blood Institute under ARRA funding (HL099355). HyperGEN (Hypertension Genetic Epidemiology Network): The hypertension network is funded by cooperative agreements (U10) with NHLBI: HL54471, HL54472, HL54473, HL54495, HL54496, HL54497, HL54509, HL54515, and 2 R01 HL55673- 12. The AGES study has been funded by NIH contracts N01-AG-1-2100 and 271201200022C. Caroline Hayward is supported by an MRC University Unit Programme Grant MC_UU_00007/10 (QTL in Health and Disease)”and “Generation Scotland received core funding from the Chief Scientist Office of the Scottish Government Health Directorate CZD/16/6, the Scottish Funding Council HR03006 and the Wellcome Trust through a Strategic Award (reference 104036/Z/14/Z) for Stratifying Resilience and Depression Longitudinally (STRADL). Genotyping was funded by the UK's Medical Research Council. Jose C. Florez, NIDDK K24 DK110550 The MESA project is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1-TR-001881, and DK063491. Additionally, one or more authors are affiliated with the following commercial entities: Interleukin Genetics, GlaxoSmithKline, Daiichi-Sankyo, AstraZeneca, Data Tecnica International LLC, Illumina Inc., University of California Healthcare, Janssen Pharmaceuticals, Goldfinch Bio, and Novo Nordisk. Please see the Competing Interests Statement for additional details. The funders provided support in the form of salaries for authors but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

Summary Background There are more than 300 known red blood cell (RBC) antigens and 33 platelet antigens that differ between individuals. Sensitisation to antigens is a serious complication that can occur in prenatal medicine and after blood transfusion, particularly for patients who require multiple transfusions. Although pre-transfusion compatibility testing largely relies on serological methods, reagents are not available for many antigens. Methods based on single-nucleotide polymorphism (SNP) arrays have been used, but typing for ABO and Rh—the most important blood groups—cannot be done with SNP typing alone. We aimed to develop a novel method based on whole-genome sequencing to identify RBC and platelet antigens. Methods This whole-genome sequencing study is a subanalysis of data from patients in the whole-genome sequencing arm of the MedSeq Project randomised controlled trial (NCT01736566) with no measured patient outcomes. We created a database of molecular changes in RBC and platelet antigens and developed an automated antigen-typing algorithm based on whole-genome sequencing (bloodTyper). This algorithm was iteratively improved to address cis–trans haplotype ambiguities and homologous gene alignments. Whole-genome sequencing data from 110 MedSeq participants (30 × depth) were used to initially validate bloodTyper through comparison with conventional serology and SNP methods for typing of 38 RBC antigens in 12 blood-group systems and 22 human platelet antigens. bloodTyper was further validated with whole-genome sequencing data from 200 INTERVAL trial participants (15 × depth) with serological comparisons. Findings We iteratively improved bloodTyper by comparing its typing results with conventional serological and SNP typing in three rounds of testing. The initial whole-genome sequencing typing algorithm was 99·5% concordant across the first 20 MedSeq genomes. Addressing discordances led to development of an improved algorithm that was 99·8% concordant for the remaining 90 MedSeq genomes. Additional modifications led to the final algorithm, which was 99·2% concordant across 200 INTERVAL genomes (or 99·9% after adjustment for the lower depth of coverage). Interpretation By enabling more precise antigen-matching of patients with blood donors, antigen typing based on whole-genome sequencing provides a novel approach to improve transfusion outcomes with the potential to transform the practice of transfusion medicine. Funding National Human Genome Research Institute, Doris Duke Charitable Foundation, National Health Service Blood and Transplant, National Institute for Health Research, and Wellcome Trust.

In the era of next-generation sequencing, it is essential to collect and understand the patient outcomes that result from this new technology. One critical determinant of these is the process by which individuals first decide whether and how to pursue genome sequencing. In this perspective article, we examine the literature on adult patient decision-making in genome sequencing and identify current research gaps to address. Several studies have explored the motivations and concerns of patients undergoing sequencing; less attention has been paid to those who decline sequencing or to individuals from lower socioeconomic groups. Many factors that might play a role in the decision to pursue or decline sequencing, including trust, family dynamics and barriers to access, have yet to be explored fully. Future research that captures the experience of the wider population will produce a more generalizable understanding of the clinical, psychosocial, and economic outcomes of pursuing or declining sequencing.

There is a well-validated association between SLCO1B1 (rs4149056) and statin-associated muscle symptoms (SAMS). Preemptive SLCO1B1 pharmacogenetic (PGx) testing may diminish the incidence of SAMS by identifying individuals with increased genetic risk before statin initiation. Despite its potential clinical application, the cost implications of SLCO1B1 testing are largely unknown. We conducted a cost–consequence analysis of preemptive SLCO1B1 testing (PGx+) versus usual care (PGx−) among Veteran patients enrolled in the Integrating Pharmacogenetics in Clinical Care (I-PICC) Study. The assessment was conducted using a health system perspective and 12-month time horizon. Incremental costs of SLCO1B1 testing and downstream medical care were estimated using data from the U.S. Department of Veterans Affairs’ Managerial Cost Accounting System. A decision analytic model was also developed to model 1-month cost and SAMS-related outcomes in a hypothetical cohort of 10,000 Veteran patients, where all patients were initiated on simvastatin. Over 12 months, 13.5% of PGx+ (26/193) and 11.2% of PGx− (24/215) participants in the I-PICC Study were prescribed Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline-concordant statins (Δ2.9%, 95% CI −4.0% to 10.0%). Differences in mean per-patient costs for lipid therapy prescriptions, including statins, for PGx+ compared to PGx− participants were not statistically significant (Δ USD 9.53, 95% CI −0.86 to 22.80 USD). Differences in per-patient costs attributable to the intervention, including PGx testing, lipid-lowering prescriptions, SAMS, laboratory and imaging expenses, and primary care and cardiology services, were also non-significant (Δ− USD 1004, 95% CI −2684 to 1009 USD). In the hypothetical cohort, SLCO1B1-informed statin therapy averted 109 myalgias and 3 myopathies at 1-month follow up. Fewer statin discontinuations (78 vs. 109) were also observed, but the SLCO1B1 testing strategy was 96 USD more costly per patient compared to no testing (124 vs. 28 USD). The implementation of SLCO1B1 testing resulted in small, non-significant increases in the proportion of patients receiving CPIC-concordant statin prescriptions within a real-world primary care context, diminished the incidence of SAMS, and reduced statin discontinuations in a hypothetical cohort of 10,000 patients. Despite these effects, SLCO1B1 testing administered as a standalone test did not result in lower per-patient health care costs at 1 month or over 1 year of treatment. The inclusion of SLCO1B1, among other well-validated pharmacogenes, into preemptive panel-based testing strategies may provide a better balance of clinical benefit and cost.

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. 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. 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). 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

OBJECTIVE Guidelines for hypertension treatment in patients with diabetes diverge regarding the systolic blood pressure (SBP) threshold at which treatment should be initiated and treatment goal. We examined associations of early SBP treatment with atherosclerotic cardiovascular disease (ASCVD) events in U.S. adults with diabetes. RESEARCH DESIGN AND METHODS We studied 43,986 patients with diabetes who newly initiated antihypertensive therapy between 2002 and 2007. Patients were classified into categories based on SBP at treatment initiation (130–139 or ≥140 mmHg) and after 2 years of treatment (100–119, 120–129, 130–139, 140–159, and ≥160 mmHg). The primary outcome was composite ASCVD events (fatal and nonfatal myocardial infarction and stroke), estimated using inverse probability of treatment-weighted Poisson regression and multivariable Cox proportional hazards regression. RESULTS Relative to individuals who initiated treatment when SBP was 130–139 mmHg, those with pretreatment SBP ≥140 mmHg had higher ASCVD risk (hazard ratio 1.10 [95% CI 1.02, 1.19]). Relative to those with pretreatment SBP of 130–139 mmHg and on-treatment SBP of 120–129 mmHg (reference group), ASCVD incidence was higher in those with pretreatment SBP ≥140 mmHg and on-treatment SBP 120–129 mmHg (adjusted incidence rate difference [IRD] 1.0 [−0.2 to 2.1] events/1,000 person-years) and in those who achieved on-treatment SBP 130–139 mmHg (IRD 1.9 [0.6, 3.2] and 1.1 [0.04, 2.2] events/1,000 person-years for those with pretreatment SBP 130–139 mmHg and ≥140 mmHg, respectively). CONCLUSIONS In this observational study, patients with diabetes initiating antihypertensive therapy when SBP was 130–139 mmHg and those achieving on-treatment SBP

INTRODUCTION: Previous studies of the relationship between fried food consumption and coronary artery disease (CAD) have yielded conflicting results. We tested the hypothesis that frequent fried food consumption is associated with a higher risk of incident CAD events in Million Veteran Program (MVP) participants. METHODS: Veterans Health Administration electronic health record data were linked to questionnaires completed at MVP enrollment. Self-reported fried food consumption at baseline was categorized: (

Estimating ASCVD risk in patients with diabetes could guide intensity of risk factor treatment, but existing risk calculators (2013 ACC/AHA ASCVD pooled cohort equations [PCE] and RECODe risk equations [RECODe]) have not been evaluated in a contemporary American population with diabetes. Using validated phenotyping algorithms, we evaluated prediction models for the development of a composite “hard” ASCVD outcome (non-fatal myocardial infarction, non-fatal stroke, cardiovascular death) over 5 years in 184,823 diabetic U.S. Veterans without baseline ASCVD receiving primary care in the VA 2002-2007 (2.4% women, 19.5% black, mean age 61 years, 9851 ASCVD events). We compared discrimination using the C-statistic and calibration using the Greenwood-Nam-D’Agostino test (GND) of the PCE variables (sex, age, race, cholesterol, blood pressure [BP], smoking status, BP medications, statin treatment, and aspirin), RECODe, and a series of models using PCE variables with VA sample-specific coefficients. Models with PCE variables and VA-specific coefficients had better discrimination (C=0.642) than the PCE (C=0.582) or RECODe (C=0.633). Successive addition of hemoglobin A1c, estimated glomerular filtration rate, diabetes medications, and statin therapy improved the model (full model, C=0.669), with only minimal enhancement by semisupervised variable selection using LASSO (C=0.672). The C-statistic of the full model was similar in those Conclusion: Health system-specific tailoring of prediction equations provides improved discrimination and calibration compared to existing models. This risk predictor could be embedded in the electronic health record and used to guide intensification of treatment aimed to improve ASCVD outcomes. Disclosure S. Raghavan: None. Y. Ho: None. D. Posner: None. J.L. Vassy: None. D.R. Gagnon: None. L.S. Phillips: Advisory Panel; Self; Janssen Pharmaceuticals, Inc. Research Support; Self; AbbVie Inc., GlaxoSmithKline plc., Kowa Pharmaceutical Europe Co. Ltd., Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Pfizer Inc. Stock/Shareholder; Self; Diasyst Inc. Other Relationship; Self; Diasyst Inc., Janssen Pharmaceuticals, Inc. P.W. Wilson: None. Funding U.S. Department of Veterans Affairs; American Heart Association

Key Points Question Are current risk prediction models accurate at estimating risk of initial atherosclerotic cardiovascular (ASCVD) events in veterans? Findings In this cohort study of 1 672 336 veterans with an outpatient visit between 2002 and 2007, the 2013 American College of Cardiology/American Heart Association model overestimated absolute risk of ASCVD during 5 years of follow-up. Statin use was associated with 7% lower relative risk of ASCVD and 25% lower relative risk of ASCVD mortality. Meaning The findings of this study suggest that reestimation and the inclusion of statin use in ASCVD prediction models might be needed for their appropriate use in a health care system., This cohort study examines the performance of the Pooled Cohort Equations (PCE) for 5-year atherosclerotic cardiovascular disease risk estimation in a contemporary cohort and tests the hypothesis that inclusion of statin therapy improves model performance., Importance Current guidelines recommend statin therapy for millions of US residents for the primary prevention of atherosclerotic cardiovascular disease (ASCVD). It is unclear whether traditional prediction models that do not account for current widespread statin use are sufficient for risk assessment. Objectives To examine the performance of the Pooled Cohort Equations (PCE) for 5-year ASCVD risk estimation in a contemporary cohort and to test the hypothesis that inclusion of statin therapy improves model performance. Design, Setting, and Participants This cohort study included adult patients in the Veterans Affairs health care system without baseline ASCVD. Using national electronic health record data, 3 Cox proportional hazards models were developed to estimate 5-year ASCVD risk, as follows: the variables and published β coefficients from the PCE (model 1), the PCE variables with cohort-derived β coefficients (model 2), and model 2 plus baseline statin use (model 3). Data were collected from January 2002 to December 2012 and analyzed from June 2016 to March 2020. Exposures Traditional ASCVD risk factors from the PCE plus baseline statin use. Main Outcomes and Measures Incident ASCVD and ASCVD mortality. Results Of 1 672 336 patients in the cohort (mean [SD] baseline age 58.0 [13.8] years, 1 575 163 [94.2%] men, 1 383 993 [82.8%] white), 312 155 (18.7%) were receiving statin therapy at baseline. During 5 years of follow-up, 66 605 (4.0%) experienced an ASCVD event, and 31 878 (1.9%) experienced ASCVD death. Compared with the original PCE, the cohort-derived model did not improve model discrimination in any of the 4 age-sex strata but did improve model calibration. The PCE overestimated ASCVD risk compared with the cohort-derived model; 211 237 of 1 136 161 white men (18.6%), 29 634 of 218 463 black men (13.6%), 1741 of 44 399 white women (3.9%), and 836 of 16 034 black women (5.2%) would be potentially eligible for statin therapy under the PCE but not the cohort-derived model. When added to the cohort-derived model, baseline statin therapy was associated with a 7% (95% CI, 5%-9%) lower relative risk of ASCVD and a 25% (95% CI, 23%-28%) lower relative risk for ASCVD death. Conclusions and Relevance In this study, lower than expected rates of incident ASCVD events in a contemporary national cohort were observed. The PCE overestimated ASCVD risk, and more than 15% of patients would be potentially eligible for statin therapy based on the PCE but not on a cohort-derived model. In the statin era, health care professionals and systems should base ASCVD risk assessment on models calibrated to their patient populations.

Introduction: Atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of morbidity in the U.S. We mapped ACC/AHA ASCVD risk in U.S. Veterans outpatients across U.S. geographic regions. Methods: We computed 10-year ASCVD risk using the 2013 ACC/AHA risk calculator with risk factor values derived from electronic health records and compared the estimates to observed ASCVD rates within Veterans Health Administration (VHA) population. We mapped the standardized differences in the observed and predicted risk at the county level using ArcGIS software. Results: There were 1.48 million VHA users between 2002-2007 aged 40 to 79 with median 10 years of follow-up. Mean age was 60 years, 96% were male, and 80% were white. The average 10-year predicted ASCVD risk after adjustment for true follow-up time at baseline was 13.8%, and the average observed risk was 6.6%, an overestimation of 7.2%. At baseline, 43.1% of patients were taking anti-hypertensive medications and 20.3% were taking statins. After updating risk factor statuses two years following baseline, 75.9% were taking anti-hypertensive medication, 42.6% were takings statins, and average predicted risk was dropped to 12.1%. In sex- and race-specific analyses the 2013 ACC/AHA risk calculator over-estimated 10-year ASCVD risk in white female, non-white female, white male, and non-white male by 2.0%, 2.0%, 8.1% and 4.8%, respectively. The greatest over-predictions in 10-year ASCVD risks were observed in western Arizona, southern California, southern Florida and Pennsylvania. These regions had higher percentages of white male VHA users compared to national averages. Conclusion: The 2013 ACC/AHA risk equation generally over-predicts ASCVD risk in the VHA population, particularly among white male Veterans. Increased anti-hypertensive and statin therapy with continual care at VHA was also associated with lower ASCVD risk. The mapping of these results allowed an overall visualization of the geographic variation across the U.S.

There are concerns that 2013 ACC/AHA ASCVD pooled risk prediction models are not accurate predictors in contemporary American patient populations, and that these models do not take into account the highly prevalent use of statin therapy or the effects of statins on vascular disease prevention. Using validated phenotyping algorithms, we evaluated the accuracy of the pooled risk model for the development of Hard ASCVD in 1.42 million Veterans (4.3% women, 16.6% black) with mean age 60 years (range 40-79 years) at baseline VA outpatient clinic evaluations in 2002-2004. There were 93,250 Hard ASCVD events over a mean of 8.4 years of follow up. We estimated Hard ASCVD risk using traditional variables (age, sex, race, total cholesterol, HDL cholesterol, diabetes mellitus, systolic blood pressure, blood pressure treatment, and smoking history) employed in the 2013 ACC/ACC ASCVD risk report. Three analytic approaches were undertaken, using 1) published 2013 ACC/AHA prediction equations, 2) 2013 ACC/AHA variables to fit VA-specific Cox regression models for white men, black men, white women, and black women, and 3) model 2 plus baseline statin use. The Harrell’s c-statistics for the models are shown in the table along with the hazard ratios for statins from model 3. Traditional predictor variables were generally statistically significant in all models, and model 1 over predicted absolute risk of Hard ASCVD in VA by approximately 6%, and statin use was associated with up to an 18% reduction in ASCVD risk. In conclusion, these results demonstrate that the pooled ASCVD prediction model performs relatively well in the VA setting, as shown by relatively good c-statistics. The 2013 ACC/AHA over predicted ASCVD risk and statin therapy at baseline was an important determinant of outcomes.