Pooled Cohort Equations Research Articles

Performance of Cardiovascular Risk Prediction Models Among People Living With HIV

Extant data on the performance of cardiovascular disease (CVD) risk score models in people living with HIV have not been synthesized. To synthesize available data on the performance of the various CVD risk scores in people living with HIV. PubMed and Embase were searched from inception through January 31, 2021. Selected studies (1) were chosen based on cohort design, (2) included adults with a diagnosis of HIV, (3) assessed CVD outcomes, and (4) had available data on a minimum of 1 CVD risk score. Relevant data related to study characteristics, CVD outcome, and risk prediction models were extracted in duplicate. Measures of calibration and discrimination are presented in tables and qualitatively summarized. Additionally, where possible, estimates of discrimination and calibration measures were combined and stratified by type of risk model. Measures of calibration and discrimination. Nine unique observational studies involving 75 304 people (weighted average age, 42 years; 59 490 male individuals [79%]) living with HIV were included. In the studies reporting these data, 86% were receiving antiretroviral therapy and had a weighted average CD4+ count of 449 cells/μL. Included in the study were current smokers (50%), patients with diabetes (5%), and patients with hypertension (25%). Ten risk prediction scores (6 in the general population and 4 in the HIV-specific population) were analyzed. Most risk scores had a moderate performance in discrimination (C statistic: 0.7-0.8), without a significant difference in performance between the risk scores of the general and HIV-specific populations. One of the HIV-specific risk models (Data Collection on Adverse Effects of Anti-HIV Drugs Cohort 2016) and 2 of the general population risk models (Framingham Risk Score [FRS] and Pooled Cohort Equation [PCE] 10 year) had the highest performance in discrimination. In general, models tended to underpredict CVD risk, except for FRS and PCE 10-year scores, which were better calibrated. There was substantial heterogeneity across the studies, with only a few studies contributing data for each risk score. Results of this systematic review and meta-analysis suggest that general population and HIV-specific CVD risk models had comparable, moderate discrimination ability in people living with HIV, with a general tendency to underpredict risk. These results reinforce the current recommendations provided by the American College of Cardiology/American Heart Association guidelines to consider HIV as a risk-enhancing factor when estimating CVD risk.

Predictive Utility of a Coronary Artery Disease Polygenic Risk Score in Primary Prevention

The clinical utility of polygenic risk scores (PRS) for coronary artery disease (CAD) has not yet been established. To investigate the ability of a CAD PRS to potentially guide statin initiation in primary prevention after accounting for age and clinical risk. This was a longitudinal cohort study with enrollment starting on January 1, 2006, and ending on December 31, 2010, with data updated to mid-2021, using data from the UK Biobank, a long-term population study of UK citizens. A replication analysis was performed in Biobank Japan. The analysis included all patients without a history of CAD and who were not taking lipid-lowering therapy. Data were analyzed from January 1 to June 30, 2022. Polygenic risk for CAD was defined as low (bottom 20%), intermediate, and high (top 20%) using a CAD PRS including 241 genome-wide significant single-nucleotide variations (SNVs). The pooled cohort equations were used to estimate 10-year atherosclerotic cardiovascular disease (ASCVD) risk and classify individuals as low (<5%), borderline (5-<7.5%), intermediate (7.5-<20%), or high risk (≥20%). Myocardial infarction (MI) and ASCVD events (defined as incident clinical CAD [including MI], stroke, or CV death). A total of 330 201 patients (median [IQR] age, 57 [40-74] years; 189 107 female individuals [57%]) were included from the UK Biobank. Over the 10-year follow-up, 4454 individuals had an MI. The CAD PRS was significantly associated with the risk of MI in all age groups but had significantly stronger risk prediction at younger ages (age <50 years: hazard ratio [HR] per 1 SD of PRS, 1.72; 95% CI, 1.56-1.89; age 50-60 years: HR, 1.46; 95% CI, 1.38-1.53; age >60 years: HR, 1.42; 95% CI, 1.37-1.48; P for interaction <.001). In patients younger than 50 years, those with high PRS had a 3- to 4-fold increased associated risk of MI compared with those in the low PRS category. A significant interaction between CAD PRS and age was replicated in Biobank Japan. When CAD PRS testing was added to the clinical ASCVD risk score in individuals younger than 50 years, 591 of 4373 patients (20%) with borderline risk were risk stratified into intermediate risk, warranting initiation of statin therapy and 3198 of 7477 patients (20%) with both borderline or intermediate risk were stratified as low risk, thus not warranting therapy. Results of this cohort study suggest that the predictive ability of a CAD PRS was greater in younger individuals and can be used to better identify patients with borderline and intermediate clinical risk who should initiate statin therapy.

Performance of the pooled cohort equation in South Asians: insights from a large integrated healthcare delivery system

South Asian ethnicity is associated with increased atherosclerotic cardiovascular disease (ASCVD) risk and has been identified as a “risk enhancer” in the 2018 American College of Cardiology/American Heart Association Guidelines. Risk estimation and statin eligibility in South Asians is not well understood; we studied the accuracy of 10-years ASCVD risk prediction by the pooled cohort equation (PCE), based on statin use, in a South Asian cohort. This is a retrospective cohort study of Kaiser Permanente Northern California South Asian members without existing ASCVD, age range 30–70, and 10-years follow up. ASCVD events were defined as myocardial infarction, ischemic stroke, and cardiovascular death. The cohort was stratified by statin use during the study period: never; at baseline and during follow-up; and only during follow-up. Predicted probability of ASCVD, using the PCE was calculated and compared to observed ASCVD events for low < 5.0%, borderline 5.0 to < 7.5%, intermediate 7.5 to < 20.0%, and high ≥ 20.0% risk groups. A total of 1835 South Asian members were included: 773 never on statin, 374 on statins at baseline and follow-up, and 688 on statins during follow-up only. ASCVD risk was underestimated by the PCE in low-risk groups: entire cohort: 1.8 versus 4.9%, p < 0.0001; on statin at baseline and follow-up: 2.58 versus 8.43%, p < 0.0001; on statin during follow-up only: 2.18 versus 7.77%, p < 0.0001; and never on statin: 1.37 versus 2.09%, p = 0.12. In this South Asian cohort, the PCE underestimated risk in South Asians, regardless of statin use, in the low risk ASCVD risk category.

Estimated versus observed 10-year atherosclerotic cardiovascular event rates in a rural population-based health initiative: The Heart of New Ulm Project

ObjectiveAssess discrepancy between estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk and observed 10-year event rates in a rural population participating in cardiovascular health initiative. MethodsThe study included a rural sample of individuals participating in the Heart of New Ulm (HONU), a population-based health initiative aimed at reducing ASCVD risk in a rural community. HONU conducted over 100 baseline screening events with 5221 individuals participating in 2009. For this analysis, we included participants who were aged 40–79 years, free of ASCVD at baseline, and had adequate data to calculate 10-year ASCVD risk. Electronic health record data and state death records were used to determine rates of non-fatal myocardial infarction and stroke, and ASCVD death from 2010–2019. ASCVD event rates were compared to estimated 10-year risks calculated using the Pooled Cohort Equations, stratified by sex and clinically relevant risk categories. ResultsThe sample (n = 2819, mean ± SD age 56.1 ± 9.9 years, 59.6% female) had a low prevalence of tobacco use (8.1% current smokers) and diabetes (6.5%) and a high prevalence of hypertension (44.4%) and hyperlipidemia (56.6%). The median estimated 10-year ASCVD risk for the entire sample was 5.7% (IQR 2.3–13.5%) with an observed 10-year ASCVD event rate of 3.4%. The largest gap between observed and estimated risk was in those at intermediate/high (≥7.5%) ASCVD risk (median 10-year risk 15.8% [IQR 10.4–29.0], observed ASCVD event rate 6.4%). ConclusioIn a sample of rural participants exposed to a multifaceted ASCVD prevention initiative, observed rates of ASCVD were substantially lower compared to estimated ASCVD risk. The potential for significantly lower than predicted ASCVD event rates in certain populations should be included in the clinician-patient risk discussion.

Association of Blood Heavy Metal Exposure with Atherosclerotic Cardiovascular Disease (ASCVD) Among White Adults: Evidence from NHANES 1999-2018.

Cardiovascular diseases (CVD) are main public health concerns highly prevalent in industrialized societies where human health is threatened by a series of environmental pollutants, particularly heavy metal contaminants. We aimed to find out if blood heavy metals are associated with the 10-year risk of atherosclerotic cardiovascular disease (ASCVD) in a nationally representative sample of US adults. We analyzed the cross-sectional data on blood heavy metals of 3268 non-Hispanic white participants aged 40-79years from the National Health and Nutrition Examination Survey (NHANES) 1999-2018. We introduced a risk estimation algorithm, namely the 2013 Pooled Cohort Equations (PCE), to assess the risk for ASCVD over a 10-year period. The 10-year risk for ASCVD was categorized as either reduced risk (< 7.5% risk) or elevated risk (≥ 7.5% risk). Blood lead, cadmium, and mercury were distributed into four quartiles. We used weighted multivariate logistic regression models and restricted cubic spline (RCS) regression to detect the association of blood heavy metal exposure with 10-year ASCVD risk. Following the adjustment of covariates, the adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for elevated 10-year ASCVD risk for participants from the highest quartiles were 4.50 (2.88-7.02), 2.59 (1.68-4.00), and 1.06 (0.66-1.71) for blood cadmium, lead, and mercury compared to the lowest quartiles, respectively. The RCS plot demonstrated that blood cadmium was linearly and positively associated with 10-year ASCVD risk (P for nonlinearity = 0.112). According to our findings, non-Hispanic whites aged 40-79years had a greater 10-year ASCVD risk as their blood lead and cadmium levels increased. Consequently, when establishing approaches for ASCVD prevention, blood heavy metals should be considered.

Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts

Binary diagnosis of coronary artery disease does not preserve the complexity of disease or quantify its severity or its associated risk with death; hence, a quantitative marker of coronary artery disease is warranted. We evaluated a quantitative marker of coronary artery disease derived from probabilities of a machine learning model. In this cohort study, we developed and validated a coronary artery disease-predictive machine learning model using 95 935 electronic health records and assessed its probabilities as in-silico scores for coronary artery disease (ISCAD; range 0 [lowest probability] to 1 [highest probability]) in participants in two longitudinal biobank cohorts. We measured the association of ISCAD with clinical outcomes-namely, coronary artery stenosis, obstructive coronary artery disease, multivessel coronary artery disease, all-cause death, and coronary artery disease sequelae. Among 95 935 participants, 35 749 were from the BioMe Biobank (median age 61 years [IQR 18]; 14 599 [41%] were male and 21 150 [59%] were female; 5130 [14%] were with diagnosed coronary artery disease) and 60 186 were from the UK Biobank (median age 62 [15] years; 25 031 [42%] male and 35 155 [58%] female; 8128 [14%] with diagnosed coronary artery disease). The model predicted coronary artery disease with an area under the receiver operating characteristic curve of 0·95 (95% CI 0·94-0·95; sensitivity of 0·94 [0·94-0·95] and specificity of 0·82 [0·81-0·83]) and 0·93 (0·92-0·93; sensitivity of 0·90 [0·89-0·90] and specificity of 0·88 [0·87-0·88]) in the BioMe validation and holdout sets, respectively, and 0·91 (0·91-0·91; sensitivity of 0·84 [0·83-0·84] and specificity of 0·83 [0·82-0·83]) in the UK Biobank external test set. ISCAD captured coronary artery disease risk from known risk factors, pooled cohort equations, and polygenic risk scores. Coronary artery stenosis increased quantitatively with ascending ISCAD quartiles (increase per quartile of 12 percentage points), including risk of obstructive coronary artery disease, multivessel coronary artery disease, and stenosis of major coronary arteries. Hazard ratios (HRs) and prevalence of all-cause death increased stepwise over ISCAD deciles (decile 1: HR 1·0 [95% CI 1·0-1·0], 0·2% prevalence; decile 6: 11 [3·9-31], 3·1% prevalence; and decile 10: 56 [20-158], 11% prevalence). A similar trend was observed for recurrent myocardial infarction. 12 (46%) undiagnosed individuals with high ISCAD (≥0·9) had clinical evidence of coronary artery disease according to the 2014 American College of Cardiology/American Heart Association Task Force guidelines. Electronic health record-based machine learning was used to generate an in-silico marker for coronary artery disease that can non-invasively quantify atherosclerosis and risk of death on a continuous spectrum, and identify underdiagnosed individuals. National Institutes of Health.

Pregnancy-related factors may signal additional protection or risk of future cardiovascular diseases

BackgroundCardiovascular disease (CVD) guidelines recommend using the Pooled Cohort Equation (PCE) to assess 10-year CVD risk based on traditional risk factors. Pregnancy-related factors have been associated with future CVD. We examined the contribution of two pregnancy-related factors, (1) history of a low birthweight (LBW) infant and (2) breastfeeding to CVD risk accounting for traditional risk factors as assessed by the PCE.MethodsA nationally representative sample of women, ages 40–79, with a history of pregnancy, but no prior CVD, was identified using NHANES 1999–2006. Outcomes included (1) CVD death and (2) CVD death plus CVD surrogates. We used Cox proportional hazards models to adjust for PCE risk score.ResultsAmong 3,758 women, 479 had a LBW infant and 1,926 reported breastfeeding. Mean follow-up time was 12.1 years. Survival models showed a consistent reduction in CVD outcomes among women with a history of breastfeeding. In cause-specific survival models, breastfeeding was associated with a 24% reduction in risk of CVD deaths (HR 0.76; 95% CI 0.45─1.27, p = 0.30) and a 33% reduction in risk of CVD deaths + surrogate CVD, though not statistically significant. (HR 0.77; 95% CI 0.52─1.14, p = 0.19). Survival models yielded inconclusive results for LBW with wide confidence intervals (CVD death: HR 0.98; 95% CI 0.47─2.05; p = 0.96 and CVD death + surrogate CVD: HR 1.29; 95% CI 0.74─2.25; p = 0.38).ConclusionPregnancy-related factors may provide important, relevant information about CVD risk beyond traditional risk factors. While further research with more robust datasets is needed, it may be helpful for clinicians to counsel women about the potential impact of pregnancy-related factors, particularly the positive impact of breastfeeding, on cardiovascular health.

Ten-Year Evolution of Statin Eligibility and Use in a Population-Based Cohort

Studies showing that the management of dyslipidemia is suboptimal are hampered by their cross-sectional design or short follow-up. Using recent data from a population-based cohort with a 10-year follow-up, we assessed the use of statins, including their intensity. We used data from the CoLaus|PsyColaus study, involving 4,655 participants at baseline (2003 to 2006) and 3,587 at 10-year follow-up (2014 to 2017). We assessed the cardiovascular risk of participants according to established guidelines from the European Society of Cardiology (ESC) and from the American Heart Association/American College of Cardiology and estimated 10-year cardiovascular risk using corresponding risk scores, Systemic Coronary Risk Evaluation risk prediction model and Pooled Cohort Equations. We first determined eligibility for statins and adherence to recommendations at 2 time periods. Additionally, we assessed the prevalence of statin users from 2014 to 2017 in persons without atherosclerotic cardiovascular disease at baseline and who developed it during the follow-up (secondary prevention). A total of 219 participants developed a first atherosclerotic cardiovascular disease during follow-up. Statin use in eligible subjects was 25.9% and 24.0% from 2003 to 2006 and 35.9% and 26.3% from 2014 to 2017, according to ESC and American Heart Association/American College of Cardiology guidelines, respectively. Per ESC guidelines, only 28.2% of treated persons achieved low-density lipoproteins cholesterol target levels from 2014 to 2017 (15.8% from 2003 to 2006), and women less frequently attained goals. Only 18% of subjects used high-intensity statins from 2014 to 2017, with women less often receiving them (14% vs 22%). In secondary prevention, only 74% of eligible subjects were using statins. In conclusion, based on contemporaneous data, management of dyslipidemia is suboptimal, including in secondary prevention, especially in women who are less frequently treated and, if treated, less frequently receive high-intensity treatment.

Effects of Diet on 10-Year Atherosclerotic Cardiovascular Disease Risk (from the DASH Trial)

Although modern risk estimators, such as the American College of Cardiology/American Heart Association Pooled Cohort Equation, play a central role in the decisions of patients to start pharmacologic therapy to prevent atherosclerotic cardiovascular disease (ASCVD), there is limited evidence to inform expectations for 10-year ASCVD risk reduction from established lifestyle interventions. Using data from the original DASH (Dietary Approaches to Stop Hypertension) trial, we determined the effects of adopting the DASH diet on 10-year ASCVD risk compared with adopting a control or a fruits and vegetables (F/V) diet. The DASH trial included 459 adults aged 22 to 75 years without CVD and not taking antihypertensive or diabetes mellitus medications, who were randomized to controlled feeding of a control diet, an F/V diet, or the DASH diet for 8 weeks. We determined 10-year ASCVD risk with the American College of Cardiology/American Heart Association Pooled Cohort Equation based on blood pressure and lipids measured before and after the 8-week intervention. Compared with the control diet, the DASH and F/V diets changed 10-year ASCVD risk by -10.3% (95% confidence interval [CI] -14.4 to -5.9) and -9.9% (95% CI -14.0 to -5.5) respectively; these effects were more pronounced in women and Black adults. There was no difference between the DASH and F/V diets (-0.4%, 95% CI -6.9 to 6.5). ASCVD reductions attributable to the difference in systolic blood pressure alone were -14.6% (-17.3 to -11.7) with the DASH diet and -7.9% (-10.9 to -4.8) with the F/V diet, a net relative advantage of 7.2% greater relative reduction from DASH compared with F/V. This was offset by the effects on high-density lipoprotein of the DASH diet, which increased 10-year ASCVD by 8.8% (5.5 to 12.3) compared with the more neutral effect of the F/V diet of -1.9% (-5.0 to 1.2). In conclusion, compared with a typical American diet, the DASH and F/V diets reduced 10-year ASCVD risk scores by about 10% over 8 weeks. These findings are informative for counseling patients on both choices of diet and expectations for 10-year ASCVD risk reduction.

Performance of the Pooled Cohort Equations in non-alcoholic fatty liver disease: The Multi-Ethnic Study of Atherosclerosis.

Non-alcoholic fatty liver disease (NAFLD) is associated with a high risk of cardiovascular disease. Whether risk scores developed in the general population accurately assess cardiovascular risk in the NAFLD population is unknown. This study aimed to evaluate the performance of the Pooled Cohort Equations (PCE) in NAFLD. Individuals in the Multi-Ethnic Study of Atherosclerosis with baseline non-contrast cardiac computed tomography scans with sufficient data to determine the presence of hepatic steatosis were identified and assessed for the development of incident 10-year atherosclerotic cardiovascular disease. The discrimination and calibration of the PCE were evaluated, and the observed and expected events by risk category (<5%, 5-<7.5%, 7.5-<20%, ≥20%) were determined. Risk reclassification with the addition of NAFLD to the PCE was assessed. Of 4014 participants included, 698 (17.4%) with NAFLD were identified, including 247 (35.3%) with moderate-to-severe steatosis. Discrimination of the PCE was suboptimal in NAFLD (c-statistic 0.69), particularly moderate-to-severe steatosis (0.65), and calibration was overall poor. While risk was overestimated in non-NAFLD, it was underestimated in NAFLD in lower/intermediate risk categories, predominantly in women (5-<7.5% observed/expected ratio=1.67). The addition of NAFLD to the PCE improved risk classification in women. The PCE overall performed suboptimally in cardiovascular risk assessment in NAFLD, particularly in women and individuals with moderate-to-severe steatosis in clinically relevant risk categories. Primary prevention may need to be considered at a lower risk threshold in these groups, and further work is needed to improve risk stratification in this growing high-risk population.

Comparison of Framingham risk score and pooled cohort equations for the prediction of coronary atherosclerosis in patients who meet the target LDL-C level of Korean dyslipidemia guideline.

This study aims to compare the predicting performance of coronary atherosclerosis between Framingham Risk Score (FRS) and Pooled Cohort Equations (PCE) in moderate to high-risk patients who meet the target low-density lipoprotein cholesterol (LDL-C) level of Korean dyslipidemia guidelines. Among 1207 patients aged 40 to 65 who underwent coronary computed tomography angiography at outpatient for chest discomfort, we included 414 moderate-risk patients (non-diabetes) and 86 high-risk patients (diabetes). They were divided into 3 groups according to FRS and PCE, then compared with coronary artery calcification score (CACS) and plaque burden degree strata. We presented receiver operating characteristic curves for the presence of coronary artery calcification (CAC) and any plaque. In moderate-risk patients, the distribution of CACS and plaque burden degree according to FRS and PCE risk strata showed significant differences between groups and a consistent trend (P < .001). Both FRS and PCE showed good discrimination for the presence of CAC [area under the curve (AUC); 0.711 vs 0.75, P = .02] and any plaque (AUC; 0.72 vs 0.756, P = .025). However, in high-risk patients, there was no significant differences or consistent trend between groups and the AUC values of FRS and PCE were (0.537 vs 0.571, P = .809) for CAC and (0.478 vs 0.65 P = .273) for any plaque showing poor discrimination. In predicting coronary atherosclerosis in moderate to high-risk patients who meet the target LDL-C level of Korean dyslipidemia guidelines, both FRS and PCE can be used in moderate-risk patients but not in high-risk patients.

Lost Therapeutic Benefit of Delayed Low-Density Lipoprotein Cholesterol Control in Statin-Treated Patients and Cost-Effectiveness Analysis of Lipid-Lowering Intensification

ObjectivesAttainment of low-density lipoprotein cholesterol (LDL-C) therapeutic goals in statin-treated patients remains suboptimal. We quantified the health economic impact of delayed lipid-lowering intensification from an Australian healthcare and societal perspective. MethodsA lifetime Markov cohort model (n = 1000) estimating the impact on coronary heart disease (CHD) of intensifying lipid-lowering treatment in statin-treated patients with uncontrolled LDL-C, at moderate to high risk of CHD with no delay or after a 5-year delay, compared with standard of care (no intensification), starting at age 40 years. Intensification was tested with high-intensity statins or statins + ezetimibe. LDL-C levels were extracted from a primary care cohort. CHD risk was estimated using the pooled cohort equation. The effect of cumulative exposure to LDL-C on CHD risk was derived from Mendelian randomization data. Outcomes included CHD events, quality-adjusted life-years (QALYs), healthcare and productivity costs, and incremental cost-effectiveness ratios (ICERs). All outcomes were discounted annually by 5%. ResultsOver the lifetime horizon, compared with standard of care, achieving LDL-C control with no delay with high-intensity statins prevented 29 CHD events and yielded 30 extra QALYs (ICERs AU$13 205/QALY) versus 22 CHD events and 16 QALYs (ICER AU$20 270/QALY) with a 5-year delay. For statins + ezetimibe, no delay prevented 53 CHD events and gave 45 extra QALYs (ICER AU$37 271/QALY) versus 40 CHD events and 29 QALYs (ICER of AU$44 218/QALY) after a 5-year delay. ConclusionsDelaying attainment of LDL-C goals translates into lost therapeutic benefit and a waste of resources. Urgent policies are needed to improve LDL-C goal attainment in statin-treated patients.

A tool to help patients visualize ASCVD risk and the potential impact of risk-lowering interventions

Risk assessment is a fundamental step in the current approach to primary prevention of atherosclerotic cardiovascular disease (ASCVD). When considering pharmacotherapy for primary prevention of ASCVD, current prevention guidelines in the United States recommend the use of the pooled cohort equations (PCE) to assess 10-year ASCVD risk and begin the important process of shared decision-making between patients and clinicians. Clinicians should support patients in the decisionmaking process by turning raw data into information that is easily understood and more effectively utilized for decisions around the treatment plan. In this work, we present a tool to help patients visualize ASCVD risk and the projected impact of risk-lowering interventions. We believe this visual tool can facilitate communication of ASCVD risk to patients, and improve patient understanding of risk and the potential impact of risklowering interventions, which we believe may help patients make more informed, empowered decisions that achieve greater risk reduction.

Serum uric acid significantly improves the accuracy of cardiovascular risk score models.

This study evaluated the impact of serum uric acid (sUA) on the accuracy of pooled cohort equations (PCE) model, Systematic COronary Risk Evaluation 2 (SCORE2), and SCORE2-older persons. We evaluated 19 769 asymptomatic self-referred adults aged 40-79 years free of cardiovascular disease and diabetes who were screened annually in a preventive healthcare setting. sUA levels were expressed as a continuous as well as a dichotomous variable (upper sex-specific tertiles defined as high sUA). The primary endpoint was the composite of death, acute coronary syndrome, or stroke, after excluding subjects diagnosed with metastatic cancer during follow-up. Mean age was 50 ± 8 years and 69% were men. During the median follow-up of 6 years, 1658 (8%) subjects reached the study endpoint. PCE, SCORE2, and high sUA were independently associated with the study endpoint in a multivariable model (P < 0.001 for all). Continuous net reclassification improvement analysis showed a 13% improvement in the accuracy of classification when high sUA was added to either PCE or SCORE2 model (P < 0.001 for both). sUA remained independently associated with the study endpoint among normal-weight subjects in the SCORE2 model (HR 1.3, 95% CI 1.1-1.6) but not among overweight individuals (P for interaction = 0.01). Subgroup analysis resulted in a significant 16-20% improvement in the model performance among normal-weight and low-risk subjects (P < 0.001 for PCE; P = 0.026 and P < 0.001 for SCORE2, respectively). sUA significantly improves the classification accuracy of PCE and SCORE2 models. This effect is especially pronounced among normal-weight and low-risk subjects.

Abstract 12728: The Addition of Social Determinants of Health Improves the Predictive Accuracy of the Pooled Cohort Equations for 10-year Ascvd Events in African Americans

Introduction: The gold standard risk prediction tool for atherosclerotic cardiovascular disease (ASCVD), the Pooled Cohort Equations (PCE) underestimates risk among the socially disadvantaged, yet social determinants of health are absent from the PCE. This may be contributing to racial inequities in CVD since due to systemic and structural racism, socioeconomic disadvantage is disproportionately concentrated in African American (AA) communities. This analysis aimed to determine whether SDOH variables were important for the prediction of 10-year ASCVD risk in a large cohort of AA and to test whether the addition of these variables improved PCE model performance. Methods: Retrospective observational study of 3,470 Jackson Heart Study (JHS) participants aged 40-79 years old without prior ASCVD at baseline study visit. 10-year ASCVD event was defined as stroke, MI, or death from MI. First, we used competing risk random survival forests to determine variable importance of risk factors, then we used cox-proportional hazards to identify which variables among those found to be important, were independently associated with our outcome. We used time dependent AUC to compare the performance of PCE alone to models with the PCE plus SDOH to assess concordance with the true event rates for prediction of 10-year ASCVD. Results: Our sample was 35% male, 100% AA and 46% of participants had an income of &lt;$35K/year. Of 30 SDOH factors tested, the following 6: BMI, depression, weekly stress, insurance status, family income, and neighborhood violence were determined most important for prediction and were independently associated with 10-year ASCVD risk. In this sample, the PCE only model had an AUC of 0.822; adding a limited panel of SDOH factors - income, insurance status and occupation, increased the AUC to 0.859, but this improvement was only statistically significant at the 0.10 level (p=0.093). The model adding the full 6 SDOH risk panel to the PCE, saw the highest AUC at 0.863 (p=0.029). Conclusions: Adding SDOH risk factors to the PCE improved risk prediction. These findings should be explored in a larger database to determine if adding SDOH to the PCE can improve risk prediction for ASCVD events among vulnerable populations and guide more optimal use of prevention strategies.

Abstract 10360: Polygenic Risk Score, Rare Monogenic Variants, and Family History Have Independent and Additive Effects on the Risk of Coronary Heart Disease

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

Abstract 11473: An EHR-Based Machine Learning Model Predicts Myocardial Infarction Better Than an ECG-Based Machine Learning Model and the Pooled Cohort Equations

Introduction: Patients at high risk for myocardial infarction (MI) benefit from treatments designed for primary prevention, especially cholesterol lowering therapy. The pooled cohort equations (PCE) are the most commonly used risk predictor for future MI but show only modest performance. Hypothesis: Electronic health record (EHR)-based, and ECG-based machine learning models are better at predicting MI as compared toPCE. Methods: We retrospectively included all patients (age 40-79) with clinical encounters in Geisinger, having at least 1 clinically acquired ECG and no prior history of MI. Incident MI within 10 years of an index ECG was identified. The ability of 2 machine learning models to predict MI were compared to the PCE: 1) an XGBoost model with EHR data (age, sex, race, smoking history, vitals, 24 lab tests) as input features; and 2) a deep neural network (DNN) that used ECG traces, age, and sex as inputs. Models were compared based on area under the receiver operating characteristic curve (AUROC). Results: A total of 103,933 ECGs from 34,932 patients had sufficient follow-up, 21% of ECGs were followed by an MI event within 10 years. The EHR-based XGBoost model had the best performance (AUROC 81%) in comparison with both the ECG-based DNN model (AUROC 68%) and the PCE (AUROC 72%). 9% of the total encounters were predicted to be ‘high risk’ by the EHR-based model and not by the pooled cohort equations. In that subgroup, 60% of patients were not on a statin and the 10-year MI event rate was 26%. 20% of encounters were predicted to be ‘high risk’ by the PCE and not by the EHR-based model. The event rate in that subgroup was 12%. Conclusions: An EHR-based XGBoost model, but not an ECG-based DNN, is superior to the PCE in predicting future MI. Patients identified as high risk by the EHR-based model but missed by the PCE, have a high rate of future MI. Statin use in that group is low, suggesting ample opportunity for intervention.

Abstract 11309: Lipoprotein(a) and the Pooled Cohort Equations for Risk Prediction: The Multi-Ethnic Study of Atherosclerosis

Introduction Lipoprotein(a) [Lp(a)] is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD) but is not included in the ACC/AHA ASCVD Risk Estimator based on the Pooled Cohort Equations (PCE). Methods We evaluated the association between Lp(a) &gt;50 mg/dL and risk for cardiovascular disease (CVD) events using multivariable Cox proportional hazards models stratified by ASCVD risk category in individuals free of baseline ASCVD with elevated Lp(a) in the Multi-Ethnic Study of Atherosclerosis (MESA). We then evaluated actual versus PCE-predicted risk by comparing 10-year CVD event rates in those with Lp(a) &gt;50 mg/dL versus Lp(a) ≤50 mg/dL using Poisson regression with adjustment for the components of the PCE by strata of risk (&lt;5%, 5-7.5%, 7.5-20%, ≥20%). We then evaluated the change in the C-index and continuous net reclassification improvement (NRI) with the addition of Lp(a) to the PCE. Results: In participants with borderline risk (5-7.5%), elevated Lp(a) was associated with greater risk for CVD events (HR 2.59, 95% CI 1.41, 4.78). Among patients with elevated Lp(a), the greatest discrimination of risk was present in those with borderline (5-7.5%) predicted risk, where the PCE significantly underestimated risk (observed rate: 8.9% for Lp(a) &gt;50 mg/dL vs 4.1% for Lp(a)≤50 mg/dL, risk ratio 2.54, 95% CI 1.35-4.62, Figure ). In this group, addition of Lp(a) to the PCE resulted in C-index improvement of 0.063. The continuous NRI with the addition of Lp(a) for CVD was 0.0386 (95% CI -0.0115, 0.1087) overall, but 0.3142 (95% CI 0.0350, 0.6083) among those at borderline predicted risk. Conclusions: The PCE underestimates risk in individuals with borderline predicted risk and elevated Lp(a). The addition of Lp(a) to the PCE improves risk prediction in individuals with borderline risk, for whom the greatest opportunity to guide treatment decisions exists.

Abstract 9843: Dynamic Importance of Genomic and Nongenomic Risk Factors for Coronary Artery Disease Over the Life Course

Introduction: Current cardiovascular risk models are limited by fixed window estimates of risk rather than lifetime trajectories. We create a novel framework to understand the time-varying profiles of genomic and non-genomic risk factors over the life span. Methods: We examine 325,948 individuals from the UK Biobank (UKB) (57% female, enrollment age 40-70 years, mean follow-up 11.9 years) with baseline clinical risk factors, composite Pooled Cohort Equations (PCE), and coronary artery disease (CAD) polygenic risk score (PRS). We compare Cox hazard ratios (HR) and the proportion of variation explained (PVE) at each age of enrollment. We extend analyses to a younger cohort of 3,821 individuals from the Framingham Health Study (FHS) Offspring Cohort, (51% female, enrollment ages 18-57, mean follow-up 35.1 years). Results: Results: HR and PVE of CAD PRS are greater early in life with HR 2.47, [95% CI 2.45-2.50] at age 40 to HR 1.67 [1.64,1.70] at age 70 in UKB; PVE of 7.8% to 2.3% in UKB. Parallel results were observed in FHS (Fig 1A-C) earlier in the life span. While the variation in lifetime risk increases with time, the HR for single risk factors estimated at each age are dynamic. The PCE, via inclusion of age, absorb much of this variation. Reclassification of intermediate 10-year PCE risk (7.5-19.9%) using PRS is most informative in younger individuals. In the UKB, the top-quintile of PRS, when compared to PCE alone, accurately predicts more individuals under age 55 (1108 versus 844) who ultimately develop CAD, but loses superiority over age 55 (Fig. 1D). Conclusions: Depending on when the question is asked, the HR of CAD of both genomic and non-genomic risk factors changes dramatically, with PRS demonstrating superiority early on. Our novel framework incorporates time-varying effects in portraying risk trajectory rather than fixed-window estimates. We show that polygenic scores also predict lifetime risk of disease early on rather than predicting only early-onset disease.

Abstract 10410: Association Between Coronary Artery Calcium Scoring With Statin Initiation, Cholesterol Reduction and Cardiovascular Outcomes in Black Patients

Introduction: Coronary artery calcium (CAC) scoring is a cost-effective and reproducible method to assess risk for cardiovascular (CV) outcomes and is particularly important for guiding prevention in asymptomatic patients. Black patients are at high risk for atherosclerotic cardiovascular disease (ASCVD) that is not fully explained by traditional risk factors. In this study, we investigated the prevalence of CAC and its association with CV events in Black and white individuals. Methods: We investigated the baseline characteristics, rates of statin initiation, changes in cholesterol, and major adverse CV events (myocardial infarction, stroke, death) in 49,799 patients with self-reported race (Black or white) who underwent CAC scoring in the CLARIFY study between 2014 and 2020 (Clinicaltrials.gov NCT04075162). Results: In total, there were 4,292 Black patients and 45,507 white patients. Compared with white patients, Black patients had similar age (P=0.17), were more likely to be female (60.5% vs 50%, P&lt;0.001), had higher 10-year ASCVD risk by pooled cohort equation (16.18% vs. 11.06%, P&lt;0.001), lower LDL cholesterol (115 vs 123 mg/dL, P&lt;0.001), and were more likely to have CAC of 0 (49.2% vs 39.9%, P&lt;0.001). At every 10-year ASCVD risk category by pooled cohort equation, CAC was more prevalent in white patients than Black patients (p&lt;0.001). Despite similar statin initiation post CAC score in patients not on statins at baseline, Black patients had less relative reduction in LDL cholesterol over 1 year compared with white patients (figure). Within each CAC category, Black patients had higher rates of CV events (P&lt;0.003) over 1 year. Conclusions: In this large cohort of patients undergoing CAC scoring, Black individuals had lower CAC scores despite having higher predicted ASCVD risk and worse outcomes. Black individuals had less relative reduction in cholesterol over 1 year compared with white individuals, despite having similar statin initiation rates.