Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index

Performance of contemporary cardiovascular risk stratification scores in Brazil: an evaluation in the ELSA-Brasil study

AIMS

Despite notable population differences in high-income and low- and middle-income countries (LMICs), national guidelines in LMICs often recommend using US-based cardiovascular disease (CVD) risk scores for treatment decisions. We examined the performance of widely used international CVD risk scores within the largest Brazilian community-based cohort study (Brazilian Longitudinal Study of Adult Health, ELSA-Brasil).

METHODS

All adults 40-75 years from ELSA-Brasil (2008-2013) without prior CVD who were followed for incident, adjudicated CVD events (fatal and non-fatal MI, stroke, or coronary heart disease death). We evaluated 5 scores-Framingham General Risk (FGR), Pooled Cohort Equations (PCEs), WHO CVD score, Globorisk-LAC and the Systematic Coronary Risk Evaluation 2 score (SCORE-2). We assessed their discrimination using the area under the receiver operating characteristic curve (AUC) and calibration with predicted-to-observed risk (P/O) ratios-overall and by sex/race groups.

RESULTS

There were 12 155 individuals (53.0±8.2 years, 55.3% female) who suffered 149 incident CVD events. All scores had a model AUC>0.7 overall and for most age/sex groups, except for white women, where AUC was <0.6 for all scores, with higher overestimation in this subgroup. All risk scores overestimated CVD risk with 32%-170% overestimation across scores. PCE and FGR had the highest overestimation (P/O ratio: 2.74 (95% CI 2.42 to 3.06)) and 2.61 (95% CI 1.79 to 3.43)) and the recalibrated WHO score had the best calibration (P/O ratio: 1.32 (95% CI 1.12 to 1.48)).

CONCLUSION

In a large prospective cohort from Brazil, we found that widely accepted CVD risk scores overestimate risk by over twofold, and have poor risk discrimination particularly among Brazilian women. Our work highlights the value of risk stratification strategies tailored to the unique populations and risks of LMICs.

The importance of LDL-C lowering in atherosclerotic cardiovascular disease prevention: Lower for longer is better

Cumulative exposure to low-density lipoprotein cholesterol (LDL-C) is a key driver of atherosclerotic cardiovascular disease (ASCVD) risk. An armamentarium of therapies to achieve robust and sustained reduction in LDL-C can reduce ASCVD risk. The gold standard for LDL-C assessment is ultracentrifugation but in routine clinical practice LDL-C is usually calculated and the most accurate calculation is the Martin/Hopkins equation. For primary prevention, consideration of estimated ASCVD risk frames decision making regarding use of statins and other therapies, and tools such as risk enhancing factors and coronary artery calcium enable tailoring of risk assessment and decision making. In patients with diabetes, lipid lowering therapy is recommended in most patients to reduce ASCVD risk with an opportunity to tailor therapy based on other risk factors. Patients with primary hypercholesterolemia and familial hypercholesterolemia (FH) with baseline LDL-C greater than or equal to 190 mg/dL are at elevated risk, and LDL-C lowering with high-intensity statin therapy is often combined with non-statin therapies to prevent ASCVD. Secondary prevention of ASCVD, including in patients with prior myocardial infarction or stroke, requires intensive lipid lowering therapy and lifestyle modification approaches. There is no established LDL-C level below which benefit ceases or safety concerns arise. When further LDL-C lowering is required beyond lifestyle modifications and statin therapy, additional medications include oral ezetimibe and bempedoic acid, or injectables such as PCSK9 monoclonal antibodies or siRNA therapy. A novel agent that acts independently of hepatic LDL receptors is evinacumab, which is approved for patients with homozygous FH. Other emerging agents are targeted at Lp(a) and CETP. In light of the expanding lipid treatment landscape, this manuscript reviews the importance of early, intensive, and sustained LDL-C-lowering for primary and secondary prevention of ASCVD.

Comparison of atherosclerotic cardiovascular disease (ASCVD) and Framingham risk scores (FRS) in an Iranian population

Background

Framingham risk score (FRS) and Atherosclerotic Cardiovascular Disease risk score (ASCVDrs) are widely used tools developed based on the American population. This study aimed to compare the ASCVDrs and FRS in an Iranian population.

Method

The participants of the Fasa Adult Cohort Study and the patients of the cardiovascular database of Vali-Asr Hospital of Fasa, aged 40-80 years, were involved in the present cross-sectional study. After excluding non-eligible participants, the individuals with a history of myocardial infarction or admission to the cardiology ward due to heart failure were considered high-risk, and the others were considered low-risk. The discriminative ability of FRS and ASCVDrs was evaluated and compared using receiver operating characteristic curve analysis. The correlation and agreement of ASCVDrs and FRS were tested using Cohen Kappa and Spearman.

Results

Finally, 8983 individuals (mean age:53.9 ± 9.5 y, 49.2 % male), including 1827 high-risk participants, entered the study. ASCVDrs detected a greater portion of participants as high-risk in comparison with FRS (28.7 % vs. 15.7 %). ASVD (AUC:0.794) had a higher discriminative ability than FRS (AUC:0.746), and both showed better discrimination in women. Optimal cut-off points for both ASCVDrs (4.36 %) and FRS (9.05 %) were lower than the original ones and in men. Compared to FRS, ASCVDrs had a higher sensitivity (79.3 % vs. 71.6 %) and lower specificity (64.5 % vs. 65.1 %). FRS and ASCVDrs had a moderate agreement (kappa:0.593,p-value<0.001) and were significantly correlated (Spearman:0.772,p-value<0.001).

Conclusions

ASCVDrs had a more accurate prediction of cardiovascular events and identified a larger number of people as high-risk in the Iranian population.

Risk equations for prosthetic joint infections (PJIs) in UK: a retrospective study using the Clinical Practice Research Datalink (CPRD) AURUM and GOLD databases

BACKGROUND

Prosthetic joint infections (PJIs) are a serious negative outcome of arthroplasty with incidence of about 1%. Risk of PJI could depend on local treatment policies and guidelines; no UK-specific risk scoring is currently available.

OBJECTIVE

To determine a risk quantification model for the development of PJI using electronic health records.

DESIGN

Records in Clinical Practice Research Datalink (CPRD) GOLD and AURUM of patients undergoing hip or knee arthroplasty between January 2007 and December 2014, with linkage to Hospital Episode Statistics and Office of National Statistics, were obtained. Cohorts' characteristics and risk equations through parametric models were developed and compared between the two databases. Pooled cohort risk equations were determined for the UK population and simplified through stepwise selection.

RESULTS

After applying the inclusion/exclusion criteria, 174 905 joints (1021 developed PJI) were identified in CPRD AURUM and 48 419 joints (228 developed PJI) in CPRD GOLD. Patients undergoing hip or knee arthroplasty in both databases exhibited different sociodemographic characteristics and medical/drug history. However, the quantification of the impact of such covariates (coefficients of parametric models fitted to the survival curves) on the risk of PJI between the two cohorts was not statistically significant. The log-normal model fitted to the pooled cohorts after stepwise selection had a C-statistic >0.7.

CONCLUSIONS

The risk prediction tool developed here could help prevent PJI through identifying modifiable risk factors pre-surgery and identifying the patients most likely to benefit from close monitoring/preventive actions. As derived from the UK population, such tool will help the National Health Service reduce the impact of PJI on its resources and patient lives.

Increased cardiovascular risk in epilepsy

Epilepsy is associated with increased mortality. Cardiovascular disease confers a significant portion of this increased risk. Recently there is increased interest in the burden of cardiovascular mortality in people with epilepsy. This review discusses the most common cardiovascular risk factors and their association with epilepsy including obesity, diabetes mellitus, and hyperlipidemia. Hyperlipidemia related to the use of enzyme inducing anti-seizure medications is also discussed as a topic that is of particular importance to prescribers that have patients with comorbid cardiovascular risk and epilepsy. Heart rate variability (HRV) and its association with SUDEP is discussed as well as a contributor to vascular risk. Finally, the authors discuss a potential role for neurologists who treat epilepsy to engage closer with their patient's cardiovascular risk factors using available tools such as a the ASCVD score calculator to determine the overall risk of mortality, as well as acting upon this information to guide treatment approaches integrating the information provided in this review.

Cardiometabolic Disease Staging and Major Adverse Cardiovascular Event Prediction in 2 Prospective Cohorts

BACKGROUND

Cardiometabolic risk prediction models that incorporate metabolic syndrome traits to predict cardiovascular outcomes may help identify high-risk populations early in the progression of cardiometabolic disease.

OBJECTIVES

The purpose of this study was to examine whether a modified cardiometabolic disease staging (CMDS) system, a validated diabetes prediction model, predicts major adverse cardiovascular events (MACE).

METHODS

We developed a predictive model using data accessible in clinical practice [fasting glucose, blood pressure, body mass index, cholesterol, triglycerides, smoking status, diabetes status, hypertension medication use] from the REGARDS (REasons for Geographic And Racial Differences in Stroke) study to predict MACE [cardiovascular death, nonfatal myocardial infarction, and/or nonfatal stroke]. Predictive performance was assessed using receiver operating characteristic curves, mean squared errors, misclassification, and area under the curve (AUC) statistics.

RESULTS

Among 20,234 REGARDS participants with no history of stroke or myocardial infarction (mean age 64 ± 9.3 years, 58% female, 41% non-Hispanic Black, and 18% diabetes), 2,695 developed incident MACE (13.3%) during a median 10-year follow-up. The CMDS development model in REGARDS for MACE had an AUC of 0.721. Our CMDS model performed similarly to both the ACC/AHA 10-year risk estimate (AUC 0.721 vs 0.716) and the Framingham risk score (AUC 0.673).

CONCLUSIONS

The CMDS predicted the onset of MACE with good predictive ability and performed similarly or better than 2 commonly known cardiovascular disease prediction risk tools. These data underscore the importance of insulin resistance as a cardiovascular disease risk factor and that CMDS can be used to identify individuals at high risk for progression to cardiovascular disease.

Risk Prediction for Atherosclerotic Cardiovascular Disease With and Without Race Stratification

Importance

Use of race-specific risk prediction in clinical medicine is being questioned. Yet, the most commonly used prediction tool for atherosclerotic cardiovascular disease (ASCVD)-pooled cohort risk equations (PCEs)-uses race stratification.

Objective

To quantify the incremental value of race-specific PCEs and determine whether adding social determinants of health (SDOH) instead of race improves model performance.

Design, Setting, and Participants

Included in this analysis were participants from the biracial Reasons for Geographic and Racial Differences in Stroke (REGARDS) prospective cohort study. Participants were aged 45 to 79 years, without ASCVD, and with low-density lipoprotein cholesterol level of 70 to 189 mg/dL or non-high-density lipoprotein cholesterol level of 100 to 219 mg/dL at baseline during the period of 2003 to 2007. Participants were followed up to 10 years for incident ASCVD, including myocardial infarction, coronary heart disease death, and fatal and nonfatal stroke. Study data were analyzed from July 2022 to February 2023.

Main outcome/measures

Discrimination (C statistic, Net Reclassification Index [NRI]), and calibration (plots, Nam D'Agostino test statistic comparing observed to predicted events) were assessed for the original PCE, then for a set of best-fit, race-stratified equations including the same variables as in the PCE (model C), best-fit equations without race stratification (model D), and best-fit equations without race stratification but including SDOH as covariates (model E).

Results

This study included 11 638 participants (mean [SD] age, 61.8 [8.3] years; 6764 female [58.1%]) from the REGARDS cohort. Across all strata (Black female, Black male, White female, and White male participants), C statistics did not change substantively compared with model C (Black female, 0.71; 95% CI, 0.68-0.75; Black male, 0.68; 95% CI, 0.64-0.73; White female, 0.77; 95% CI, 0.74-0.81; White male, 0.68; 95% CI, 0.64-0.71), in model D (Black female, 0.71; 95% CI, 0.67-0.75; Black male, 0.68; 95% CI, 0.63-0.72; White female, 0.76; 95% CI, 0.73-0.80; White male, 0.68; 95% CI, 0.65-0.71), or in model E (Black female, 0.72; 95% CI, 0.68-0.76; Black male, 0.68; 95% CI, 0.64-0.72; White female, 0.77; 95% CI, 0.74-0.80; White male, 0.68; 95% CI, 0.65-0.71). Comparing model D with E using the NRI showed a net percentage decline in the correct assignment to higher risk for male but not female individuals. The Nam D'Agostino test was not significant for all race-sex strata in each model series, indicating good calibration in all groups.

Conclusions

Results of this cohort study suggest that PCE performed well overall but had poorer performance in both BM and WM participants compared with female participants regardless of race in the REGARDS cohort. Removal of race or the addition of SDOH did not improve model performance in any subgroup.

Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association

Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.

Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches

PURPOSE OF REVIEW

In this review, we sought to provide an overview of ML and focus on the contemporary applications of ML in cardiovascular risk prediction and precision preventive approaches. We end the review by highlighting the limitations of ML while projecting on the potential of ML in assimilating these multifaceted aspects of CAD in order to improve patient-level outcomes and further population health.

RECENT FINDINGS

Coronary artery disease (CAD) is estimated to affect 20.5 million adults across the USA, while also impacting a significant burden at the socio-economic level. While the knowledge of the mechanistic pathways that govern the onset and progression of clinical CAD has improved over the past decade, contemporary patient-level risk models lag in accuracy and utility. Recently, there has been renewed interest in combining advanced analytic techniques that utilize artificial intelligence (AI) with a big data approach in order to improve risk prediction within the realm of CAD. By virtue of being able to combine diverse amounts of multidimensional horizontal data, machine learning has been employed to build models for improved risk prediction and personalized patient care approaches. The use of ML-based algorithms has been used to leverage individualized patient-specific data and the associated metabolic/genomic profile to improve CAD risk assessment. While the tool can be visualized to shift the paradigm toward a patient-specific care, it is crucial to acknowledge and address several challenges inherent to ML and its integration into healthcare before it can be significantly incorporated in the daily clinical practice.

Aspirin for the Primary Prevention of Vascular Ischemic Events: An Updated Systematic Review and Meta-analysis to Support Shared Decision-Making

Background

Since the publication of the 2010 Canadian antiplatelet guidelines, several large randomized controlled trials (RCTs) have evaluated the role of aspirin (ASA) use in primary prevention. We evaluated the effect of ASA use, compared with no ASA, on ischemic and bleeding events in patients without known atherosclerotic cardiovascular diseases.

Methods

We updated a published systematic review and meta-analysis by searching MEDLINE, Embase, and CENTRAL for the period up to March 2023. We included RCTs that enrolled patients for primary prevention of atherosclerotic cardiovascular diseases, and compared use of ASA to no ASA. We assessed risk of bias (RoB) using the Cochrane RoB tool, and certainty of evidence using the grading recommendations, assessment, development, and evaluation (GRADE) criteria. The primary efficacy outcome was major adverse cardiovascular events (MACE) (death, myocardial infarction, or stroke). The primary safety outcomes were intracranial hemorrhage and extracranial major bleeding events. We used a random-effects model to generate pooled risk ratios (RRs) and 95% confidence intervals (CIs).

Results

We included 14 RCTs (n = 167,587) at overall low RoB, with a median follow-up of 5 years. Compared to no ASA, ASA use reduced the incidence of MACE (RR 0.90, 95% CI 0.86-0.94), with a higher risk of intracranial hemorrhage (RR 1.33, 95% CI 1.13-1.56) and extracranial major bleeding (RR 1.67, 95% CI 1.36-2.06). In prespecified subgroups of age, sex, and diabetes, effect estimates were consistent.

Conclusions

ASA use in primary prevention is associated with a consistent reduction in MACE, but at the expense of major bleeding events. Patient values and preferences should be taken into account when considering ASA use for primary prevention.

Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG

Background

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide, driven primarily by coronary artery disease (CAD). ASCVD risk estimators such as the pooled cohort equations (PCE) facilitate risk stratification and primary prevention of ASCVD but their accuracy is still suboptimal.

Methods

Using deep electronic health record data from 7,116,209 patients seen at 70+ hospitals and clinics across 5 states in the USA, we developed an artificial intelligence-based electrocardiogram analysis tool (ECG-AI) to detect CAD and assessed the additive value of ECG-AI-based ASCVD risk stratification to the PCE. We created independent ECG-AI models using separate neural networks including subjects without known history of ASCVD, to identify coronary artery calcium (CAC) score ≥300 Agatston units by computed tomography, obstructive CAD by angiography or procedural intervention, and regional left ventricular akinesis in ≥1 segment by echocardiogram, as a reflection of possible prior myocardial infarction (MI). These were used to assess the utility of ECG-AI-based ASCVD risk stratification in a retrospective observational study consisting of patients with PCE scores and no prior ASCVD. The study period covered all available digitized EHR data, with the first available ECG in 1987 and the last in February 2023.

Findings

ECG-AI for identifying CAC ≥300, obstructive CAD, and regional akinesis achieved area under the receiver operating characteristic (AUROC) values of 0.88, 0.85, and 0.94, respectively. An ensembled ECG-AI identified 3, 5, and 10-year risk for acute coronary events and mortality independently and additively to PCE. Hazard ratios for acute coronary events over 3-years in patients without ASCVD that tested positive on 1, 2, or 3 versus 0 disease-specific ECG-AI models at cohort entry were 2.41 (2.14-2.71), 4.23 (3.74-4.78), and 11.75 (10.2-13.52), respectively. Similar stratification was observed in cohorts stratified by PCE or age.

Interpretation

ECG-AI has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimates ASCVD risk, and in whom risk assessment over time periods shorter than 10 years is desired.

Funding

Anumana.

Added value of arterial stiffness index for the 10-year atherosclerotic cardiovascular disease risk determination in a middle-aged population-based study

OBJECTIVE

Atherosclerotic cardiovascular disease (ASCVD) is considered the leading cause of mortality worldwide. Arterial stiffness, measured by arterial stiffness index (ASI), could be a main predictor in target damage of organs. Uncertainty remains regarding the contribution of ASI to estimated ASCVD risk. This study investigates the added value of ASI in ASCVD risk determination using the general UK Biobank middle-aged population.

METHODS

Among 100,598 participants from the UK Biobank population, ASI was assessed and associations between ASCVD risk were stratified by sex and estimated using multiple linear and logistic regressions adjusted for heart rate, physical activity, alcohol status, smocking pack years, BMI categories and CKD.

RESULTS

Males presented higher ASCVD risk than females (8.58% vs. 2.82%, p < 0.001) and higher ASI levels (9.50 m/s vs. 7.00 m/s, p < 0.001). The Youden index was determined at 9.70 m/s in males (p < 0.001) and 10.46 m/s in females (p < 0.001). Among females, participants with ASI > 10.46 m/s showed higher ASCVD risk than others (3.64% vs. 2.56%, p < 0.001), similar results were observed for males (9.92% vs. 7.21%, p < 0.001). In males, ASI showed significant added value information when including overall covariates (AUC = 0.695 vs. AUC = 0.663, p < 0.001). Similar results were observed in females (AUC = 0.693 vs. AUC = 0.687, p = 0.001). In both genders, for the association between ASI and ASCVD risk, nonlinear relationships were observed with higher accuracies than linear models.

CONCLUSION

Further studies should investigate ASCVD risk stratification management depending on ASI measurement.

The mediation effect of lipids, blood pressure and BMI between air pollutant mixture and atherosclerotic cardiovascular disease: The CHCN-BTH cohort study

BACKGROUND

The combine effect of air pollutant mixture on atherosclerotic cardiovascular disease (ASCVD) remain undefined. This study aims to explore the association between long-term exposure of air pollutants and ASCVD, focusing on the mediating role of lipids, blood pressure and BMI.

METHODS

This study was based on the CHCN-BTH cohort study. The annual concentrations of air pollutants and PM2.5 constituents were sourced from in the Tracking Air Pollution in China (TAP) and ChinaHighAirPollutants (CHAP) datasets from 2014 to 2019. A Cox mixed-effects model was used to investigate the associations between long-term exposure of air pollutants and ASCVD. The combined impact of the air pollutant mixture was assessed using Quantile g-Computation. Stratified, sensitivity, and mediation analyses were conducted.

RESULTS

A total of 27,134 participants aged 18-80 were recruited in the present study. We found that each IQR increase of PM2.5, PM1, NO2, O3, BC, SO42-, and OM were significantly associated with the incidence of ASCVD, the hazard ratios (HRs) and 95 % confidence interval (CI) were 1.55 (1.35, 1.78), 1.46 (1.27, 1.67), 1.30 (1.21, 1.39), 1.66 (1.41,1.95), 2.14 (1.63, 2.83), 1.65 (1.25, 2.17) and 1.92(1.52, 2.45), respectively. The combined effect of air pollutant mixture on ASCVD was 1.79 (1.46, 2.20), PM2.5 contributed 83.3 % to this combined effect. Mediation effect models suggested that air pollutants and ASCVD might be mediated through SBP, DBP, HDL-C, LDL-C, hsCRP and BMI (mediation proportion range from 1.3 % to 26.1 %), Notably, HDL-C played mediation roles of 11.3 % (7.0 %, 18.4), 26.1 % (17.7 %, 38.1 %) and 25.4 % (15.4, 47.7 %) in the effects of long-term exposure to PM2.5, PM1 and OM on ASCVD, respectively.

CONCLUSIONS

Long-term, high-level air pollutant exposure was significantly associated with an elevated risk of ASCVD, particularly for PM2.5. Blood pressure, lipids and BMI, especially HDL-C, may mediate the effects of air pollutants exposure on ASCVD.

Sex Associations Between Air Pollution and Estimated Atherosclerotic Cardiovascular Disease Risk Determination

Objective: The purpose of this study was to investigate the sex correlations of particulate matters (PM2.5, PM10, PM2.5-10), NO2 and NOx with ASCVD risk in the UK Biobank population. Methods: Among 285,045 participants, pollutants were assessed and correlations between ASCVD risk were stratified by sex and estimated using multiple linear and logistic regressions adjusted for length of time at residence, education, income, physical activity, Townsend deprivation, alcohol, smocking pack years, BMI and rural/urban zone. Results: Males presented higher ASCVD risk than females (8.63% vs. 2.65%, p < 0.001). In males PM2.5, PM10, NO2, and NOx each were associated with an increased ASCVD risk >7.5% in the adjusted logistic models, with ORs [95% CI] for a 10 μg/m3 increase were 2.17 [1.87-2.52], 1.15 [1.06-1.24], 1.06 [1.04-1.08] and 1.05 [1.04-1.06], respectively. In females, the ORs for a 10 μg/m3 increase were 1.55 [1.19-2.05], 1.22 [1.06-1.42], 1.07 [1.03-1.10], and 1.04 [1.02-1.05], respectively. No association was observed in both sexes between ASCVD risk and PM2.5-10. Conclusion: Our findings may suggest the possible actions of air pollutants on ASCVD risk.

Shared Decision-Making and Cardiovascular Health: A Scientific Statement From the American Heart Association

Shared decision-making is increasingly embraced in health care and recommended in cardiovascular guidelines. Patient involvement in health care decisions, patient-clinician communication, and models of patient-centered care are critical to improve health outcomes and to promote equity, but formal models and evaluation in cardiovascular care are nascent. Shared decision-making promotes equity by involving clinicians and patients, sharing the best available evidence, and recognizing the needs, values, and experiences of individuals and their families when faced with the task of making decisions. Broad endorsement of shared decision-making as a critical component of high-quality, value-based care has raised our awareness, although uptake in clinical practice remains suboptimal for a range of patient, clinician, and system issues. Strategies effective in promoting shared decision-making include educating clinicians on communication techniques, engaging multidisciplinary medical teams, incorporating trained decision coaches, and using tools (ie, patient decision aids) at appropriate literacy and numeracy levels to support patients in their cardiovascular decisions. This scientific statement shines a light on the limited but growing body of evidence of the impact of shared decision-making on cardiovascular outcomes and the potential of shared decision-making as a driver of health equity so that everyone has just opportunities. Multilevel solutions must align to address challenges in policies and reimbursement, system-level leadership and infrastructure, clinician training, access to decision aids, and patient engagement to fully support patients and clinicians to engage in the shared decision-making process and to drive equity and improvement in cardiovascular outcomes.

Association of cardiovascular health and risk prediction algorithms with subclinical atherosclerosis identified by carotid ultrasound

Background

The requirement for laboratory tests to assess conventional cardiovascular disease (CVD) risk may be a barrier to the early detection and management of atherosclerosis in some population groups. A simpler risk assessment could facilitate detection of CVD.

Objectives

The association of the Fuster-BEWAT Score (FBS), Framingham Risk Score (FRS), and Pooled Cohort Equation (PCE) with the presence of carotid plaque was investigated, with the intention of developing a stepped screening process for the primary prevention of CVD.

Methods

Asymptomatic participants with a family history of premature CVD had an absolute cardiovascular disease risk (ACVDR) score calculated using the FBS, FRS, and PCE risk equations. This risk classification was compared with the presence or absence of carotid plaque on ultrasound. Prediction of carotid plaque presence by risk scores and risk factors was assessed by logistic regression and area under the curve (AUC) for discrimination and diagnostic performance. A classification and regression-tree (CART) model was obtained for stratification of risk assessment.

Results

Risk score calculation and ultrasound scanning were performed in 1031 participants, of whom 51 had carotid plaques. Participants with plaque and male sex showed higher risk (higher PCE and FRS and lower FBS, as higher scores of FBS indicate better cardiovascular health). Participants ≤50 years of age showed the FBS was a significant predictor; there was a reduced likelihood of plaque presence with a higher score (OR 0.54, 95% CI 0.39-0.75, P < .01). Higher ACVDR (evidenced by higher PCE and FRS scores and lower FBS score) was associated with an increased likelihood of carotid plaque; however, the FBS and the addition of risk factors not included in the equation showed the highest AUC (AUC = 0.76, P < .001). CART modeling showed that participants with FBS between 6 and 9 would be recommended for further risk stratification using the PCE, whereupon a PCE score ≥5% conferred an increased risk and greater possibility for plaque. Validation of the model using a different cohort showed similar risk stratification for plaque presence according to level of risk by CART analysis.

Conclusion

FBS was able to identify the presence of carotid plaque in asymptomatic individuals. Its use for initial risk delineation might improve the selection of patients for more specific and complex assessment, reducing cost and time.

Comparison of cardiovascular mortality between MAFLD and NAFLD: A cohort study

BACKGROUND AND AIMS

A new diagnostic criterion of metabolic dysfunction-associated fatty liver disease (MAFLD) has been proposed. However, only few studies have shown that MAFLD predicts cardiovascular disease (CVD) mortality better than non-alcoholic fatty liver disease (NAFLD). Therefore, a cohort study was conducted to assess this relationship.

METHODS AND RESULTS

Health examination data from health care centers in South Korea were assessed after excluding participants with missing covariates and cancer history (n = 701,664). Liver ultrasonography reports, laboratory and anthropometric data were extracted. Diagnoses of NAFLD and MAFLD were performed according to standard definitions. Participants were categorized based on the presence of NAFLD and MAFLD. In addition, participants were classified into five categories: no fatty liver disease (no FLD), NAFLD-only, MAFLD-only, both FLDs, and alcoholic FLD (AFLD) and non-MAFLD. Multivariable regression modeling was performed. The median follow-up duration was 8.77 years, and 52.56% of participants were men. After stratifying the cohort into no-MAFLD and MAFLD groups, MAFLD was associated with increased CVD mortality (adjusted HR 1.14, 95% CI 1.02-1.28). When participants were divided into no-NAFLD and NAFLD groups, there was a non-significant trend towards an increase in CVD mortality in NAFLD group (adjusted HR 1.07, 95% CI 0.95-1.21). When participants were divided into five categories, MAFLD-only group showed increased CVD mortality (adjusted HR 1.35, 95% CI 1.07-1.70) while NAFLD-only group showed no significant association with CVD mortality (adjusted HR 0.67, 95% CI 0.38-1.19).

CONCLUSIONS

In conclusion, MAFLD is associated with increased CVD mortality in a relatively young Korean population.

Clinical utility gains from incorporating comorbidity and geographic location information into risk estimation equations for atherosclerotic cardiovascular disease

OBJECTIVE

There are over 363 customized risk models of the American College of Cardiology and the American Heart Association (ACC/AHA) pooled cohort equations (PCE) in the literature, but their gains in clinical utility are rarely evaluated. We build new risk models for patients with specific comorbidities and geographic locations and evaluate whether performance improvements translate to gains in clinical utility.

MATERIALS AND METHODS

We retrain a baseline PCE using the ACC/AHA PCE variables and revise it to incorporate subject-level information of geographic location and 2 comorbidity conditions. We apply fixed effects, random effects, and extreme gradient boosting (XGB) models to handle the correlation and heterogeneity induced by locations. Models are trained using 2 464 522 claims records from Optum©'s Clinformatics® Data Mart and validated in the hold-out set (N = 1 056 224). We evaluate models' performance overall and across subgroups defined by the presence or absence of chronic kidney disease (CKD) or rheumatoid arthritis (RA) and geographic locations. We evaluate models' expected utility using net benefit and models' statistical properties using several discrimination and calibration metrics.

RESULTS

The revised fixed effects and XGB models yielded improved discrimination, compared to baseline PCE, overall and in all comorbidity subgroups. XGB improved calibration for the subgroups with CKD or RA. However, the gains in net benefit are negligible, especially under low exchange rates.

CONCLUSIONS

Common approaches to revising risk calculators incorporating extra information or applying flexible models may enhance statistical performance; however, such improvement does not necessarily translate to higher clinical utility. Thus, we recommend future works to quantify the consequences of using risk calculators to guide clinical decisions.

Association between Social Isolation and Loneliness with Estimated Atherosclerotic Cardiovascular Disease Risk in a UK Biobank Population-Based Study

OBJECTIVE

The association of cardiovascular (CV) risk with social isolation and loneliness remains poorly studied. The purpose of this cross-sectional study was to investigate the associations between social isolation and loneliness with estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk.

METHODS

Among 302,553 volunteers of the UK Biobank population, social isolation and loneliness were assessed with a questionnaire. Associations between social isolation and loneliness with ASCVD risk were estimated using multiple gender regressions.

RESULTS

Men presented a higher estimated 10-year ASCVD risk (8.63% vs. 2.65%, p < 0.001) and higher proportions of social isolation (9.13% vs. 8.45%, p < 0.001) and loneliness (6.16% vs. 5.57%, p < 0.001) than women. In all covariate-adjusted models, social isolation was associated with an increased ASCVD risk in men (B = 0.21 (0.16; 0.26), p < 0.001) and women (B = 0.12 (0.10; 0.14), p < 0.001). Loneliness was associated with an increased ASCVD risk in men (B = 0.08 (0.03; 0.14), p = 0.001) but not in women (p = 0.217). A significant interaction was observed between social isolation and loneliness with ASCVD risk in men (p = 0.009) and women (p = 0.016). After adjustment for all covariates, both social isolation and loneliness were significantly associated with ASCVD risk in men (B = 0.44 (0.28; 0.61), p < 0.001) and women (B = 0.20 (0.12; 0.29), p < 0.001).

CONCLUSION

Social isolation was associated with a higher estimated 10-year ASCVD risk in both genders but only loneliness among men. Social isolation and loneliness can be considered potential added risk factors for CV risk. Health policies should address these notions in prevention campaigns, in addition to traditional risk factors.

Performance of Cardiovascular Risk Prediction Models Among People Living With HIV: A Systematic Review and Meta-analysis

Importance

Extant data on the performance of cardiovascular disease (CVD) risk score models in people living with HIV have not been synthesized.

Objective

To synthesize available data on the performance of the various CVD risk scores in people living with HIV.

Data Sources

PubMed and Embase were searched from inception through January 31, 2021.

Study Selection

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.

Data Extraction and Synthesis

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.

Main Outcomes and Measures

Measures of calibration and discrimination.

Results

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.

Conclusions and Relevance

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.

Sex-Specific Survival Bias and Interaction Modeling in Coronary Artery Disease Risk Prediction

BACKGROUND

The 10-year Atherosclerotic Cardiovascular Disease risk score is the standard approach to predict risk of incident cardiovascular events, and recently, addition of coronary artery disease (CAD) polygenic scores has been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined. This study performed an extensive evaluation of age and sex effects in genetic CAD risk prediction.

METHODS

The population-based Norwegian HUNT2 (Trøndelag Health Study 2) cohort of 51 036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372 410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards, and Harrell concordance index, sensitivity, and specificity were compared.

RESULTS

Inclusion of age and sex interactions of CAD polygenic score to the prediction models increased the C-index and sensitivity by accounting for nonadditive effects of CAD polygenic score and likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. We identified a total of 82.6% of incident CAD cases by using a 2-step approach: (1) Atherosclerotic Cardiovascular Disease risk score (74.1%) and (2) the CAD polygenic score interaction model for those in low clinical risk (additional 8.5%).

CONCLUSIONS

These findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age- and sex-interaction terms with polygenic scores to optimize detection of individuals at high risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information.

Temporal trends in atherosclerotic cardiovascular disease risk among U.S. adults. Analysis of the National Health and Nutrition Examination Survey, 1999-2018

BACKGROUND

Atherosclerotic cardiovascular diseases are a significant cause of disability and mortality. Study of trends in cardiovascular risk at a population level helps understand the overall cardiovascular health and the impact of primary prevention efforts.

AIMS

To assess trends in the estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk among U.S. adults from 1999-2000 to 2017-18 with no established cardiovascular disease (CVD).

METHODS AND RESULTS

Serial cross-sectional analysis of National Health and Nutrition Examination Survey (NHANES) data from 1999-2000 to 2017-18 (10 cycles), including 24 022 US adults aged 40-79 years with no reported ASCVD. ASCVD risk was assessed using the pooled cohort equations (PCEs). There was a significant temporal decline in the mean 10-year ASCVD risk from 13.5% (95% CI, 12.5-14.4) in 1999-2000 to 11.1% (10.5-11.7) in 2011-12 (Ptrend < 0.001) and to 12.0% (11.3-12.7) in 2017-2018 (overall Ptrend = 0.001), with the mean ASCVD risk score remaining stable from 2013-14 through 2017-2018 (Ptrend = 0.056). A declining trend in ASCVD risk was noted in females, non-Hispanic Blacks and those with income <3 times the poverty threshold with Ptrend of <0.001, 0.002, and 0.007, respectively. Mean total cholesterol and prevalence of smokers showed a downward trend (Ptrend <0.001 for both), whereas type 2 diabetes and mean BMI showed an upward trend (Ptrend < 0.001 for both).

CONCLUSIONS

The 20-year trend of ASCVD risk among NHANES participants 40-79 years, as assessed by the use of PCE, showed a non-linear downward trend from 1999-2000 to 2017-18. The initial and significant decline in estimated ASCVD risk from 1999-2000 to 2011-12 subsequently stabilized, with no significant change from 2013-14 to 2017-18. Mean BMI and prevalence of diabetes mellitus increased while mean serum cholesterol levels and prevalence of smoking declined during the study period. Our findings support invigoration of efforts aimed at prevention of CVD, including primordial prevention of CVD risk factors.

Association between plaque localization in proximal coronary segments and MACE outcomes in patients with mild CAC: Results from the EISNER study

Objective

Coronary artery calcium score (CAC) is a validated tool to predict and reclassify cardiovascular risk. Additional metrics such as regional distribution and extent of CAC over Agatston CAC score may allow further risk stratification. In this study, we evaluate the prognostic significance of proximal CAC involvement in asymptomatic population from the prospective EISNER (Early-Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) registry, focusing on patients with mild CAC (score 1-99).

Methods

This study included a total of 2,047 adult asymptomatic subject who underwent baseline CAC scan and 14-year follow-up for MACE, defined as myocardial infarction, late revascularization, or cardiac death. Proximal involvement was defined as presence of CAC in the LM, proximal LAD, LCX or RCA. CAC was categorized as 0, 1-99, and ≥100.

Results

1,090 (53.2%) subjects had no CAC, 576 (28.1%) had CAC 1-99, and 381 (18.7%) had CAC ≥100. Proximal involvement was seen in 67.2% of subjects with CAC 1-99 and 97.3% of subjects with CAC ≥100. In the CAC 1-99 category, the presence of proximal CAC was associated with increased MACE risk after adjustment for CAC score, CAC extent and conventional risk factors compared to those without proximal CAC (HR: 2.84 95% CI: 1.29-6.25, p=0.009).

Conclusion

In asymptomatic subjects with CAC scores of 1-99, the presence and extent of proximal CAC plaques provides strong independent prognostic information in predicting MACE

Machine learning derived ECG risk score improves cardiovascular risk assessment in conjunction with coronary artery calcium scoring

Background

Precision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. In this study, we aimed to develop a quantitative ECG risk score (eRiS) to predict major adverse cardiovascular events (MACE) alone, or when added to CAC. Further, we aimed to construct and validate a novel nomogram incorporating ECG, CAC and clinical factors for ASCVD.

Methods

We analyzed 5,864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). Events were defined as myocardial infarction, coronary revascularization, stroke or death. A total of 649 ECG features, consisting of measurements such as amplitude and interval measurements from all deflections in the ECG waveform (53 per lead and 13 overall) were automatically extracted using a clinical software (GE Muse™ Cardiology Information System, GE Healthcare). The data was split into 4 training (S_tr) and internal validation (S_v) sets [S_tr (1): S_v (1): 50:50; S_tr (2): S_v (2): 60:40; S_tr (3): S_v (3): 70:30; S_tr (4): S_v (4): 80:20], and the results were compared across all the subsets. We used the ECG features derived from S_tr to develop eRiS. A least absolute shrinkage and selection operator-Cox (LASSO-Cox) regularization model was used for data dimension reduction, feature selection, and eRiS construction. A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (M_ecg), CAC alone (M_cac) and a combination of eRiS and CAC (M_ecg+cac) for MACE prediction. A nomogram (M_nom) was further constructed by integrating eRiS with CAC and demographics (age and sex). The primary endpoint of the study was the assessment of the performance of M_ecg, M_cac, M_ecg+cac and M_nom in predicting CV disease-free survival in ASCVD.

Findings

Over a median follow-up of 14 months, 494 patients had MACE. The feature selection strategy preserved only about 18% of the features that were consistent across the various strata (S_tr). The M_ecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p = <2e-16). eRiS could predict time-to MACE (C-Index: 0.6, p = <2e-16 across all S_v). The M_ecg+cac model was associated with MACE (C-index: 0.71). Model comparison showed that M_ecg+cac was superior to M_ecg (p = 1.8e-10) or M_cac (p < 2.2e-16) alone. The M_nom, comprising of eRiS, CAC, age and sex was associated with MACE (C-index 0.71). eRiS had the most significant contribution, followed by CAC score and other clinical variables. Further, M_nom was able to identify unique patient risk-groups based on eRiS, CAC and clinical variables.

Conclusion

The use of ECG features in conjunction with CAC may allow for improved prognostication and identification of populations at risk. Future directions will involve prospective validation of the risk score and the nomogram across diverse populations with a heterogeneity of treatment effects.

Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Prediction performance and fairness heterogeneity in cardiovascular risk models

Prediction models are commonly used to estimate risk for cardiovascular diseases, to inform diagnosis and management. However, performance may vary substantially across relevant subgroups of the population. Here we investigated heterogeneity of accuracy and fairness metrics across a variety of subgroups for risk prediction of two common diseases: atrial fibrillation (AF) and atherosclerotic cardiovascular disease (ASCVD). We calculated the Cohorts for Heart and Aging in Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) score for AF and the Pooled Cohort Equations (PCE) score for ASCVD in three large datasets: Explorys Life Sciences Dataset (Explorys, n = 21,809,334), Mass General Brigham (MGB, n = 520,868), and the UK Biobank (UKBB, n = 502,521). Our results demonstrate important performance heterogeneity across subpopulations defined by age, sex, and presence of preexisting disease, with fairly consistent patterns across both scores. For example, using CHARGE-AF, discrimination declined with increasing age, with a concordance index of 0.72 [95% CI 0.72-0.73] for the youngest (45-54 years) subgroup to 0.57 [0.56-0.58] for the oldest (85-90 years) subgroup in Explorys. Even though sex is not included in CHARGE-AF, the statistical parity difference (i.e., likelihood of being classified as high risk) was considerable between males and females within the 65-74 years subgroup with a value of - 0.33 [95% CI - 0.33 to - 0.33]. We also observed weak discrimination (i.e., < 0.7) and suboptimal calibration (i.e., calibration slope outside of 0.7-1.3) in large subsets of the population; for example, all individuals aged 75 years or older in Explorys (17.4%). Our findings highlight the need to characterize and quantify the behavior of clinical risk models within specific subpopulations so they can be used appropriately to facilitate more accurate, consistent, and equitable assessment of disease risk.

Effect of No-Charge Coronary Artery Calcium Scoring on Cardiovascular Prevention

Prevention of cardiovascular disease is currently guided by probabilistic risk scores that may misclassify individual risk and commit many middle-aged patients to prolonged pharmacotherapy. The coronary artery calcium (CAC) score, although endorsed for intermediate-risk patients, is not widely adopted because of barriers in reimbursement. The impact of removing cost barrier on cardiovascular outcomes in real-world settings is not known. Within the University Hospitals Health System (Cleveland, Ohio), CAC was offered to patients with at least 1 cardiovascular risk factor at low charge between 2014 and 2017 ($99) and no charge from January 1, 2018 onward. CAC use and access, patient characteristics, reclassification of risk compared with the pooled cohort equations (PCEs) for atherosclerotic vascular disease, statin use, changes in parameters of cardiometabolic health, downstream cardiovascular testing, downstream coronary revascularization, and cardiovascular outcomes were evaluated. A total of 52,151 patients underwent CAC testing over the study period. Median 10-year PCE for atherosclerotic vascular disease, in the entire cohort was 8.3% (4.0% to 15.9%). Among patients with PCE >20%, 21% had CAC <100, whereas 37% of those with PCE <7.5% had CAC ≥100. Among patients who were not on statin before CAC testing, 1-year statin prescription was 24% and was significantly associated with higher CAC scores. Total cholesterol, low-density lipoprotein cholesterol, and triglycerides all decreased significantly 1-year after CAC, and the degree of decrease was strongly linked with CAC scores. One year after CAC, 14% underwent noninvasive ischemic evaluation, 1.4% underwent invasive coronary angiography, and 0.9% underwent revascularization. The majority (74%) of revascularization procedures occurred in patients with CAC >400. In conclusion, reducing or removing the cost burden of CAC leads to significant test uptake by patients, which is followed by reclassification of statin eligibility, increases in the use of preventive medications, and improvement in risk factors, with very low rates of invasive downstream testing.

Dyslipidemia management for primary prevention of cardiovascular events: Best in-clinic practices

Dyslipidemia is a fundamental risk factor for cardiovascular diseases (CVDs) and can worsen the prognosis, if unaddressed. Lipid guidelines are still evolving as dyslipidemia is affecting newer patient subsets. However, these guidelines are governed by regional demographics and ethnic data. Primary care practitioners (PCPs) are the first to offer treatment, and hence placed early in the healthcare continuum. PCPs shoulder a huge responsibility in early detection of dyslipidemia for primary prevention of future cardiovascular (CV) events. Therefore, as members of Cardiovascular RISk Prevention (CRISP) in Asia network, the authors intend to align and shape-up the daily clinical practice workflow for PCPs and have a goal-directed strategy for managing dyslipidemia. This paper reviews the major international lipid guidelines, namely the American and European guidelines, and the regional guidelines from Indonesia, Malaysia, Philippines, Thailand, and Vietnam to identify their commonalities and heterogeneities. The authors, with a mutual consensus, have put forth, best in-clinic practices for screening, risk assessment, diagnosis, treatment, and management of dyslipidemia, particularly to reduce the overall risk of CV events, especially in the Asian context. The authors feel that PCPs should be encouraged to work in congruence with patients to decide on best possible therapy, which would be a holistic approach, rather than pursuing a "one-size-fits-all" approach. Since dyslipidemia is a dynamic field, accumulation of high-quality evidence and cross-validation studies in the future are warranted to develop best in-clinic practices at a global level.

Primary and Secondary Prevention of CAD: A Review

Coronary artery disease is the leading cause of death in both men and women, yet adequate control of risk factors can largely reduce the incidence and recurrence of cardiac events. In this review, we discuss various life style and pharmacological measures for both the primary and secondary prevention of coronary artery disease. With a clear understanding of management options, health care providers have an excellent opportunity to educate patients and ameliorate a significant burden of morbidity and mortality.

Pathways by Which Food Insecurity Is Associated With Atherosclerotic Cardiovascular Disease Risk

Background Food insecurity (FI) has been associated with an increased atherosclerotic cardiovascular disease (ASCVD) risk; however, the pathways by which FI leads to worse cardiovascular health are unknown. We tested the hypothesis that FI is associated with ASCVD risk through nutritional/anthropometric (eg, worse diet quality and increased weight), psychological/mental health (eg, increased depressive symptoms and risk of substance abuse), and access to care pathways. Methods and Results We conducted a cross-sectional study of adults (aged 40-79 years) using the 2007 to 2016 National Health and Nutrition Examination Survey. Our primary exposure was household FI, and our outcome was 10-year ASCVD risk categorized as low (<5%), borderline (≥5% -<7.5%), intermediate (≥7.5%-<20%), and high risk (≥20%). We used structural equation modeling to evaluate the pathways and multiple mediation analysis to determine direct and indirect effects. Of the 12 429 participants, 2231 (18.0%) reported living in a food-insecure household; 5326 (42.9%) had a low ASCVD risk score, 1402 (11.3%) borderline, 3606 (29.0%) intermediate, and 2095 (16.9%) had a high-risk score. In structural models, we found significant path coefficients between FI and the nutrition/anthropometric (β, 0.130; SE, 0.027; P<0.001), psychological/mental health (β, 0.612; SE, 0.043; P<0.001), and access to care (β, 0.110; SE, 0.036; P=0.002) pathways. We did not find a significant direct effect of FI on ASCVD risk, and the nutrition, psychological, and access to care pathways accounted for 31.6%, 43.9%, and 15.8% of the association, respectively. Conclusions We found that the association between FI and ASCVD risk category was mediated through the nutrition/anthropometric, psychological/mental health, and access to care pathways. Interventions that address all 3 pathways may be needed to mitigate the negative impact of FI on cardiovascular disease.

Management of Obesity in Cardiovascular Practice: JACC Focus Seminar

Obesity contributes to reduced life expectancy because of its link with type 2 diabetes and cardiovascular disease. Yet, targeting this poorly diagnosed, ill-defined, and underaddressed modifiable risk factor remains a challenge. In this review, we emphasize that the tendency among health care professionals to amalgam all forms of obesity altogether as a single entity may contribute to such difficulties and discrepancies. Obesity is a heterogeneous condition both in terms of causes and health consequences. Attention should be given to 2 prevalent subgroups of individuals: 1) patients who are overweight or moderately obese with excess visceral adipose tissue; and 2) patients with severe obesity, the latter group having distinct additional health issues related to their large body fat mass. The challenge of tackling high-cardiovascular-risk forms of obesity through a combination of personalized clinical approaches and population-based solutions is compounded by the current obesogenic environment and economy.

Performance of the American Heart Association/American College of Cardiology Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Self-reported Physical Activity Levels

Importance

The American Heart Association/American College of Cardiology pooled cohort equations (PCEs) are used for predicting 10-year atherosclerotic cardiovascular disease (ASCVD) risk. Pooled cohort equation risk prediction capabilities across self-reported leisure-time physical activity (LTPA) levels and the change in model performance with addition of LTPA to the PCE are unclear.

Objective

To evaluate PCE risk prediction performance across self-reported LTPA levels and the change in model performance by adding LTPA to the existing PCE model.

Design, Setting, and Participants

Individual-level pooling of data from 3 longitudinal cohort studies-Atherosclerosis Risk in Communities, Multi-Ethnic Study of Atherosclerosis, and Cardiovascular Health Study-was performed. A total of 18 824 participants were stratified into 4 groups based on self-reported LTPA levels: inactive (0 metabolic equivalent of task [MET]-min/wk), less than guideline-recommended (<500 MET-min/wk), guideline-recommended (500-1000 MET-min/week), and greater than guideline-recommended (>1000 MET-min/wk). Pooled cohort equation risk discrimination was studied using the C statistic and reclassification capabilities were studied using the Greenwood Nam-D'Agostino χ2 goodness-of-fit test. Change in risk discrimination and reclassification on adding LTPA to PCEs was evaluated using change in C statistic, integrated discrimination index, and categorical net reclassification index.

Main Outcomes and Measures

Adjudicated ASCVD events during 10-year follow-up.

Results

Among 18 824 participants studied, 10 302 were women (54.7%); mean (SD) age was 57.6 (8.2) years. A total of 5868 participants (31.2%) were inactive, 3849 (20.4%) had less than guideline-recommended LTPA, 3372 (17.9%) had guideline-recommended LTPA, and 5735 (30.5%) had greater than guideline-recommended LTPA level. Higher LTPA levels were associated with a lower risk of ASCVD after adjustment for risk factors (hazard ratio [HR] per 1-SD higher LTPA, 0.91; 95% CI, 0.86-0.96). Across LTPA groups, PCE risk discrimination (C statistic, 0.76-0.78) and risk calibration (all χ2 P > .10) was similar. Addition of LTPA to the PCE model resulted in no significant change in the C statistic (0.0005; 95% CI, -0.0004 to 0.0015; P = .28) and categorical net reclassification index (-0.003; 95% CI, -0.010 to 0.010; P = .95), but a minimal improvement in the integrated discrimination index (0.0008; 95% CI, 0.0002-0.0013; P = .005) was observed. Similar results were noted when cohort-specific coefficients were used for creating the baseline model.

Conclusions and Relevance

Higher self-reported LTPA levels appear to be associated with lower ASCVD risk and increasing LTPA promotes cardiovascular wellness. These findings suggest the American Heart Association/American College of Cardiology PCEs are accurate at estimating the probability of 10-year ASCVD risk regardless of LTPA level. The addition of self-reported LTPA to PCEs does not appear to be associated with improvement in risk prediction model performance.