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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.

In this retrospective cohort study of 94 595 severe acute respiratory syndrome coronavirus 2–positive cases, we developed and validated an algorithm to assess the association between coronavirus disease 2019 (COVID-19) severity and long-term complications (stroke, myocardial infarction, pulmonary embolism/deep vein thrombosis, heart failure, and mortality). COVID-19 severity was associated with a greater risk of experiencing a long-term complication 31–120 days postinfection. Most incident events occurred 31–60 days postinfection and diminished after day 91, except heart failure for severe patients and death for moderate patients, which peaked on days 91–120. Understanding the differential impact of COVID-19 severity on long-term events provides insight into possible intervention modalities and critical prevention strategies.

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.

Introduction: The majority of population-based studies of myocardial infarction (MI) rely on billing codes for classification. Classification algorithms employing machine learning (ML) increasingly used for phenotyping using electronic health record (EHR) data. Hypothesis: ML algorithms integrating billing and information from narrative notes extracted using natural language processing (NLP) can improve classification of MI compared to billing code algorithms. Improved classification will improve power to compare risk factors across population subgroups. Methods: Retrospective cohort study of nationwide Veterans Affairs (VA) EHR data. MI classified using 2 approaches: (1) published billing code algorithm, (2) published phenotyping pipeline incorporating NLP and ML. Results compared against gold standard chart review of MI outcomes in 308 Veterans. We also tested known association between high density lipoprotein cholesterol (HDL-C) and MI outcomes classified using the 2 approaches among Black and White Veterans, stratified by sex and race; prior study showed HDL-C less protective for Black compared to White individuals. Results: We studied 17,176,658 million Veterans, mean age 69 years, 94% male, 12% self-report Black, 71% White. The billing code algorithm classified MI at positive predictive value (PPV) 0.64 compared to the published ML approach, PPV 0.90; the latter classified a modestly higher percentage of non-White Veterans. Using ML algorithm for MI, we replicated a reduced protective effect of HDL-C in Black vs White male and female Veterans (Table); with the billing code algorithm no association was observed between low density lipoprotein cholesterol (LDL-C) or HDL-C with MI among Black female Veterans. Conclusions: Using nationwide VA data, application of an ML approach improved classification of MI particularly among non-White Veterans, resulting in improved power to study differences in association for MI risk factors among Black and White 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.

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.