Revising the atherosclerotic cardiovascular disease calculator without race

The C2DREAM framework: Investigating the structural mechanisms undergirding racial health inequities

Racism shapes the distribution of the social determinants of health (SDoH) along racial lines. Racism determines the environments in which people live, the quality of housing, and access to healthcare. Extensive research shows racism in its various forms negatively impacts health status, yet few studies and interventions seriously interrogate the role of racism in impacting health. The C2DREAM framework illuminates how exposure to racism, in multiple forms, connects to cardiovascular disease, hypertension, and obesity. The goal of the C2DREAM framework is to guide researchers to critically think about and measure the role of racism across its many levels of influence to better elucidate the ways it contributes to persistent health inequities. The conceptual framework highlights the interconnectedness between forms of racism, SDoH, and the lifecourse to provide a greater context to individual health outcomes. Utilizing this framework and critically contending with the effects of racism in its multiple and cumulative forms will lead to better research and interventions.

Impact of Race on Classification of Atherosclerotic Risk Using a National Cardiovascular Risk Prediction Tool

Introduction

The use of race in clinical risk prediction tools may exacerbate racial disparities in healthcare access and outcomes. This study quantified the number of individuals reclassified for primary prevention of cardiovascular disease owing to a change in their race alone on the basis of a commonly used risk prediction tool.

Methods

This is a cross-sectional analysis of individuals aged 40-75 years without a history of cardiovascular events, diabetes, or other high-risk features using the 2005-2018 National Health and Nutritional Examination Survey. Authors compared atherosclerotic cardiovascular disease risk scores using the American Heart Association/American College of Cardiology equation recommended for White individuals or individuals of other races with that recommended for Black individuals.

Results

A total of 2,946 White individuals; 1,361 Black individuals; and 2,495 individuals of other races were included in the analysis. Using the American Heart Association/American College of Cardiology equation, the mean 10-year atherosclerotic cardiovascular disease risk was 5.80% (95% CI=5.54, 6.06) for White individuals, 7.04% (956% CI=6.69, 7.39) for Black individuals, and 4.93% (95% CI=4.61, 5.24) for individuals of other races. When using the American Heart Association/American College of Cardiology equation designated for the opposite race (White/other race versus Black), the mean atherosclerotic cardiovascular disease risk score increased by 1.02% (95% CI=0.90, 1.13) for White individuals, decreased by 1.82% (95% CI= -1.67, -1.96) for Black individuals, and increased by 0.98% (95% CI=0.85, 1.10) for individuals of other races. When using clinical atherosclerotic cardiovascular disease categories of <7.5%, 7.5%-10%, and >10%, 16.93% of all individuals were reclassified when using the American Heart Association/American College of Cardiology's equation designated for the opposite race.

Conclusions

Changing race within a commonly used cardiovascular risk prediction tool results in significant changes in risk classification among eligible White and Black individuals in the U.S.

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.

Race as a Component of Cardiovascular Disease Risk Prediction Algorithms

PURPOSE OF REVIEW

Several prediction algorithms include race as a component to account for race-associated variations in disease frequencies. This practice has been questioned recently because of the risk of perpetuating race as a biological construct and diverting attention away from the social determinants of health (SDoH) for which race might be a proxy. We evaluated the appropriateness of including race in cardiovascular disease (CVD) prediction algorithms, notably the pooled cohort equations (PCE).

RECENT FINDINGS

In a recent investigation, we reported substantial and biologically implausible differences in absolute CVD risk estimates upon using PCE for predicting CVD risk in Black and White persons with identical risk factor profiles, which might result in differential treatment decisions based solely on their race. We recommend the development of raceless CVD risk prediction algorithms that obviate race-associated risk misestimation and racializing treatment practices, and instead incorporate measures of SDoH that mediate race-associated risk differences.

Disparities in Guideline-Recommended Statin Use for Prevention of Atherosclerotic Cardiovascular Disease by Race, Ethnicity, and Gender : A Nationally Representative Cross-Sectional Analysis of Adults in the United States

BACKGROUND

Although statins are a class I recommendation for prevention of atherosclerotic cardiovascular disease and its complications, their use is suboptimal. Differential underuse may mediate disparities in cardiovascular health for systematically marginalized persons.

OBJECTIVE

To estimate disparities in statin use by race-ethnicity-gender and to determine whether these potential disparities are explained by medical appropriateness of therapy and structural factors.

DESIGN

Cross-sectional analysis.

SETTING

National Health and Nutrition Examination Survey from 2015 to 2020.

PARTICIPANTS

Persons eligible for statin therapy based on 2013 and 2018 American College of Cardiology/American Heart Association blood cholesterol guidelines.

MEASUREMENTS

The independent variable was race-ethnicity-gender. The outcome of interest was use of a statin. Using the Institute of Medicine framework for examining unequal treatment, we calculated adjusted prevalence ratios (aPRs) to estimate disparities in statin use adjusted for age, disease severity, access to health care, and socioeconomic status relative to non-Hispanic White men.

RESULTS

For primary prevention, we identified a lower prevalence of statin use that was not explained by measurable differences in disease severity or structural factors among non-Hispanic Black men (aPR, 0.73 [95% CI, 0.59 to 0.88]) and non-Mexican Hispanic women (aPR, 0.74 [CI, 0.53 to 0.95]). For secondary prevention, we identified a lower prevalence of statin use that was not explained by measurable differences in disease severity or structural factors for non-Hispanic Black men (aPR, 0.81 [CI, 0.64 to 0.97]), other/multiracial men (aPR, 0.58 [CI, 0.20 to 0.97]), Mexican American women (aPR, 0.36 [CI, 0.10 to 0.61]), non-Mexican Hispanic women (aPR, 0.57 [CI, 0.33 to 0.82), non-Hispanic White women (aPR, 0.69 [CI, 0.56 to 0.83]), and non-Hispanic Black women (aPR, 0.75 [CI, 0.57 to 0.92]).

LIMITATION

Cross-sectional data; lack of geographic, language, or statin-dose data.

CONCLUSION

Statin use disparities for several race-ethnicity-gender groups are not explained by measurable differences in medical appropriateness of therapy, access to health care, and socioeconomic status. These residual disparities may be partially mediated by unobserved processes that contribute to health inequity, including bias, stereotyping, and mistrust.

PRIMARY FUNDING SOURCE

National Institutes of Health.

An Anti-Racism and Equity Initiative Improves Residency Educational Conferences

Background

Graduate medical education curricula may reinforce systemic inequities and bias, thus contributing to health disparities. Curricular interventions and evaluation measures are needed to increase trainee awareness of bias and known inequities in health care.

Objective

This study sought to improve the content of core noontime internal medicine residency educational conferences by implementing the Department of Medicine Anti-Racism and Equity (DARE) educational initiative.

Methods

DARE best practices were developed from available anti-racism and equity educational materials. Volunteer trainees and faculty in the department of medicine of a large urban academic medical center were recruited and underwent an hourlong training to utilize DARE best practices to coach faculty on improving the anti-racist and equity content of educational conferences. DARE coaches then met with faculty to review the planned 2021-2022 academic year (AY) lectures and facilitate alignment with DARE best practices. A rubric was created from DARE practices and utilized to compare pre-intervention (AY21) and post-intervention (AY22) conferences.

Results

Using the DARE best practices while coaching increased the anti-racism and equity content from AY21 to AY22 (total rubric score mean [SD] 0.16 [1.19] to 1.38 [1.39]; P=.001; possible scores -4 to +5), with 75% (21 of 28) of AY22 conferences showing improvement. This included increased diversity of photographs, discussion of the racial or ethnic makeup of research study participants, appropriate use of race in case vignettes, and discussion of the impact of racism or bias on health disparities.

Conclusions

Training coaches to implement DARE best practices improved the anti-racism and equity content of existing noontime internal medicine residency educational conferences.

Prevalence of Statin Use for Primary Prevention of Atherosclerotic Cardiovascular Disease by Race, Ethnicity, and 10-Year Disease Risk in the US: National Health and Nutrition Examination Surveys, 2013 to March 2020

Importance

The burden of atherosclerotic cardiovascular disease (ASCVD) in the US is higher among Black and Hispanic vs White adults. Inclusion of race in guidance for statin indication may lead to decreased disparities in statin use.

Objective

To evaluate prevalence of primary prevention statin use by race and ethnicity according to 10-year ASCVD risk.

Design, Setting, and Participants

This serial, cross-sectional analysis performed in May 2022 used data from the National Health and Nutrition Examination Survey, a nationally representative sample of health status in the US, from 2013 to March 2020 (limited cycle due to the COVID-19 pandemic), to evaluate statin use for primary prevention of ASCVD and to estimate 10-year ASCVD risk. Participants aged 40 to 75 years without ASCVD, diabetes, low-density lipoprotein cholesterol levels 190 mg/dL or greater, and with data on medication use were included.

Exposures

Self-identified race and ethnicity (Asian, Black, Hispanic, and White) and 10-year ASCVD risk category (5%-<7.5%, 7.5%-<20%, ≥20%).

Main Outcomes and Measures

Prevalence of statin use, defined as identification of statin use on pill bottle review.

Results

A total of 3417 participants representing 39.4 million US adults after applying sampling weights (mean [SD] age, 61.8 [8.0] years; 1289 women [weighted percentage, 37.8%] and 2128 men [weighted percentage, 62.2%]; 329 Asian [weighted percentage, 4.2%], 1032 Black [weighted percentage, 12.7%], 786 Hispanic [weighted percentage, 10.1%], and 1270 White [weighted percentage, 73.0%]) were included. Compared with White participants, statin use was lower in Black and Hispanic participants and comparable among Asian participants in the overall cohort (Asian, 25.5%; Black, 20.0%; Hispanic, 15.4%; White, 27.9%) and within ASCVD risk strata. Within each race and ethnicity group, a graded increase in statin use was observed across increasing ASCVD risk strata. Statin use was low in the highest risk stratum overall with significantly lower rates of use among Black (23.8%; prevalence ratio [PR], 0.90; 95% CI, 0.82-0.98 vs White) and Hispanic participants (23.9%; PR, 0.90; 95% CI, 0.81-0.99 vs White). Among other factors, routine health care access and health insurance were significantly associated with higher statin use in Black, Hispanic, and White adults. Prevalence of statin use did not meaningfully change over time by race and ethnicity or by ASCVD risk stratum.

Conclusions and Relevance

In this study, statin use for primary prevention of ASCVD was low among all race and ethnicity groups regardless of ASCVD risk, with the lowest use occurring among Black and Hispanic adults. Improvements in access to care may promote equitable use of primary prevention statins in Black and Hispanic adults.

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.

Toward the elimination of race-based medicine: replace race with racism as preeclampsia risk factor

Pregnancy-related morbidity and mortality continue to disproportionately affect birthing people who identify as Black. The use of race-based risk factors in medicine exacerbates racial health inequities by insinuating a false conflation that fails to consider the underlying impact of racism. As we work toward health equity, we must remove race as a risk factor in our guidelines to address disparities due to racism. This includes the most recent US Preventive Services Taskforce, American College of Obstetricians and Gynecologists, and Society for Maternal-Fetal Medicine guidelines for aspirin prophylaxis in preeclampsia, where the risk factor for "Black race" should be replaced with "anti-Black racism." In this commentary, we reviewed the evidence that supports race as a sociopolitical construct and the health impacts of racism. We presented a call to action to remove racial determination in the guidelines for aspirin prophylaxis in preeclampsia and more broadly in our practice of medicine.