Predicting Mortality Risk Using the PREVENT Equation Across Diverse Racial Groups

ABSTRACT

Objectives: The Predicting Risk of CVD Events (PREVENT) score offers a contemporary tool for assessing cardiovascular risk without incorporating race, which has raised concerns about its performance across diverse racial and ethnic groups. We aimed to validate the performance of the PREVENT cardiovascular risk equation across diverse racial and ethnic groups and assess its association with long-term all-cause and cardiovascular mortality.

Study Design: Observational cohort study using nationally representative data from the National Health and Nutrition Examination Survey (NHANES) linked with mortality data.

Methods: Using 10-year data from the NHANES (2009-2018), we analyzed a cohort of more than 177 million adults in the US to evaluate the association between baseline cardiovascular risk, as determined by the PREVENT overall cardiovascular disease risk equation, and long-term all-cause and cardiovascular mortality across racial and ethnic groups. The cohort was stratified by race and ethnicity. We employed Cox proportional hazards models to assess the relationship between cardiovascular risk and mortality.

Results: Our analysis revealed significant variations in baseline cardiovascular risk across racial and ethnic groups. Across all groups, there was a consistent incremental increase in both cardiovascular and all-cause mortality rates with higher estimated cardiovascular risk. During up to a decade of follow-up, we found that individuals at high risk had a 6-fold higher risk of all-cause mortality and a 9-fold higher risk of cardiovascular mortality compared with individuals at low cardiovascular risk. The association between cardiovascular risk and mortality remained consistent across all racial and ethnic groups, albeit with very different risk estimates. For every 5% increase in estimated 10-year cardiovascular risk, there was a 54% increase in all-cause mortality and a 57% increase in cardiovascular mortality.

Conclusions: These study findings validate PREVENT scores across diverse racial and ethnic populations, highlighting the tool’s effectiveness in predicting cardiovascular risk and mortality regardless of race or ethnicity.

Am J Manag Care. 2025;31(5):In Press

Takeaway Points

This study validates the Predicting Risk of CVD Events (PREVENT) score across diverse racial and ethnic populations, highlighting its effectiveness in predicting cardiovascular (CV) risk and mortality, regardless of race or ethnicity.

• We observed a notable variation in baseline CV risk across different racial and ethnic groups.
• Additionally, across all racial and ethnic groups, we observed an incremental increase in both CV and all-cause mortality rates with higher estimated baseline CV risk.

Despite advancements in prevention and treatment strategies, cardiovascular disease (CVD) remains a leading cause of mortality worldwide. Assessment of the long-term risk for CV events is recommended to personalize preventive therapies based on an individual’s risk of developing CVD.1 In 2013, the American Heart Association (AHA) developed the pooled cohort equations (PCEs), which generate sex- and race-specific estimates of atherosclerotic CVD (ASCVD) in Black and White adults.2 Despite their endorsement by the AHA,1 the PCEs have several limitations, including that they do not capture the contemporary changes in risk factor prevalence and preventive measures.3 It also is unclear whether the PCEs apply to other racial and ethnic groups. Furthermore, the growing burden of CVD morbidity and mortality related to cardiovascular-kidney-metabolic (CKM) conditions4-7 requires improved risk prediction equations relevant to CKM risk. Therefore, the AHA developed and validated a novel risk assessment tool, Predicting Risk of CVD Events (PREVENT), to assess the risk of ASCVD, heart failure, or both.8,9 Unlike the PCEs, race is not included as a factor when calculating risk in the PREVENT equation.

The exclusion of race from the PREVENT equation was a deliberate choice to avoid perpetuating race-based treatment decisions. However, it is essential to explore disparities across racial and ethnic groups because social, environmental, and structural determinants of health often vary significantly across these populations. Even in a race-neutral model, these factors can influence the baseline risk profiles and outcomes, making it crucial to assess how effectively the PREVENT score functions across diverse racial and ethnic groups. This exploration helps determine whether a race-neutral approach like that of PREVENT may contribute to equitable CV risk prediction or whether additional factors should be considered to fully capture the disparities faced by different populations. Furthermore, given the interplay between CV risk factors and long-term mortality, there is a need to evaluate the prognostic utility of the PREVENT score in predicting not only CV events but also long-term overall and CV mortality among diverse patient populations.

In this study, we aimed to assess the association between baseline estimated CV risk as determined by the PREVENT score and long-term all-causes and CV mortality across different racial and ethnic groups. Additionally, we sought to explore potential disparities in baseline risk and mortality outcomes across different racial and ethnic groups, despite the absence of race as a factor in the risk calculation. Our findings have the potential to inform personalized risk assessment strategies and improve long-term outcomes for individuals at risk of CVD.

METHODS

The National Health and Nutrition Examination Survey (NHANES) is a series of nationally representative studies that monitor the health of the US population. Participants are selected from the noninstitutionalized civilian population in the US, and the data are publicly available.10 As such, patient consent for this specific analysis was not required, and the study was exempt from institutional review board approval.

We used 10 years of data from the 2009 to 2018 NHANES cycles. These data were linked with data from the NHANES Linked Mortality Files, which link adult (≥ 18 years) participants of NHANES with death records in the National Death Index data set through December 31, 2019, the latest mortality data available.

The total combined sample of NHANES from 2009 through 2018 comprised 5959 adult participants and their questionnaire and laboratory data. We excluded individuals with a self-reported history of coronary heart disease, angina, heart attack, or stroke; those ineligible for mortality follow-up; and those with missing measures of weight or blood pressure (eAppendix Figure [eAppendix available at ajmc.com]).

Demographic and Social Characteristics and Quantification of CV Risk

Demographic characteristics were queried during the home interview. Participants were stratified by self-reported race and Hispanic origin information.11 Household poverty index was used as a measure of socioeconomic status (SES) and calculated as the ratio of monthly family income to poverty levels, categorized as low (≤1.30), low-middle (1.31-1.85), middle (1.86-3.50), and high (>3.50) income.12 Education level was categorized as less than high school, high school or equivalent, or more than high school. Venous blood samples were drawn at mobile examination centers using standardized protocols. Serum low-density lipoprotein cholesterol (LDL-C) levels were derived from fasting study participants examined in the morning session. LDL-C was calculated from measured values of total cholesterol, triglycerides, and high-density lipoprotein cholesterol (HDL-C) according to the Friedewald calculation (mg/dL) ([LDL-C] = [total cholesterol] – [HDL-C] – [triglycerides / 5]). This is valid for triglycerides up to 400 mg/dL. Systolic blood pressure, height, and weight were measured at the examination center, and smoking status, diabetes, and usage of antihypertensive and lipid-lowering medications were assessed in dedicated questionnaires.

We calculated the CV risk of each individual according to the overall risk of CVD PREVENT basic equations, which include sex, age, cholesterol levels, estimated glomerular filtration rate, systolic blood pressure, body mass index, diabetes, and smoking status, as well as usage of antihypertensive and lipid-lowering medications.8,9 Following the AHA’s recommendations, we categorized CV risk into 3 levels of 10-year risk of CVD: low (up to 5%), borderline-intermediate (5%-20%), and high ( > 20%).1

Assessment of Mortality Status

The mortality status of each participant in NHANES was determined through a probabilistic record match to death certificate records from the National Death Index. Vital status was ascertained from additional sources, including information obtained from linkages with the US Social Security Administration and/or by active follow-up of survey participants.13 Follow-up time for each outcome was counted from the baseline examination date until the registered date of death or the end of the study (December 31, 2019), whichever occurred first. The primary outcome of interest in this study was mortality from all causes, with a secondary outcome of interest of CV mortality (International Statistical Classification of Diseases, Tenth Revision codes I00-I09, I11, I13, I20-I51, and I60-I69). Because follow-up time was until any event of mortality, for CV mortality, individuals were censored if they died of a non-CVD cause.

Statistical Analysis

NHANES oversamples individuals 60 years and older, African American/Black individuals, and those of Hispanic ethnicity. To ensure nationally representative estimates, sampling weights were considered in all analyses to account for oversampling of subgroups and complex sample design. Continuous variables are presented as a mean and SD, and categorical data are presented as percentages and frequencies. Categorical variables were compared using the Pearson χ2 test, whereas continuous variables were compared using the Student t test or the Mann-Whitney U test. Cox proportional hazard models were used to evaluate the association between the estimated CV risk and risk of mortality (all-cause and CV mortality), and follow-up time was used as the underlying time metric. We further tested the interaction between CV risk and self-reported race and Hispanic origin on mortality, using alternative models with interaction terms. We ran alternative models, one using CV risk as a categorical variable using the predefined risk categories and one continuous model testing the mortality risk of a 5% increase in CV risk. Follow-up time was calculated from the date of the interview or examination until the date of death or end of the study (December 31, 2019). We performed univariable and multivariable-adjusted analyses; the latter adjusted for education level and ratio of family income to poverty as social determinants of health and were calculated according to self-reported race and Hispanic origin. Proportional hazards (PH) assumptions for the Cox models were assessed based on Schoenfeld residual testing. In this method, the correlation of time with the residuals between the observed and expected values of covariates in each failure time point was examined. We did not observe any significant correlation of residuals with time that may be interpreted as a violation of the PH assumption. All adult participants included in NHANES have assumed data on vital status updated through December 31, 2019. Only individuals eligible for mortality follow-up were included in this analysis, without the need to censor data for the primary outcome of all-cause mortality. All analyses were performed using Stata SE 17.0 (StataCorp LLC) and SPSS 26 (IBM).

RESULTS

After applying exclusion criteria, this analysis included 4917 participants, representing 177,946,187 adults in the US, with 125,635,832 (70.6%) at low CV risk, 41,048,484 (23.1%) at borderline-intermediate risk, and 11,261,871 (6.3%) at high CV risk (Figure 1 [A]). Individuals at high CV risk were older (mean age, 74.9 years) compared with those with borderline-intermediate (mean age, 60.6 years) or low (mean age, 36.7 years) CV risk (P < .001). Individuals at low CV risk were more likely to be female, Mexican American, or other Hispanic ethnicity and to have a higher education level. They were less likely to have diabetes, hypertension, or hyperlipidemia but were more likely to be current smokers than higher-risk individuals (Table 1). Figure 1 (B) presents the distribution of CV risk groups across the racial and Hispanic origin groups. In our analysis, we observed a statistically significant difference in the prevalence of low CV risk among different racial and ethnic groups. Mexican American and other Hispanic individuals exhibited the highest prevalence of low-risk individuals (82.5% and 83.3%, respectively), whereas non-Hispanic Black and non-Hispanic White groups showed significantly lower proportions of individuals at low CV risk (73.1% and 66.8%, respectively; P < .001). Correspondingly, the highest proportion of individuals at high CV risk was observed among the non-Hispanic White group (7.1%), and the lowest proportion of individuals at high CV risk was observed among Mexican American and other Hispanic groups (3.9% for both; P < .001).

eAppendix Tables 1 through 5 present the baseline characteristics of individuals across different race and Hispanic origin, stratified by baseline CV risk categories. Overall, across all racial and Hispanic origin groups, individuals at low CV risk were younger and more likely to have a higher education level and a lower burden of comorbidities. Among individuals at high CV risk, non-Hispanic White individuals were the oldest (mean age, 75.9 years vs 70.6-73.4 years; P < .001), with a lower prevalence of current smoking (13.0% vs 18.8%-20.7%; P < .001) and diabetes (31.8% vs 46.7%-49.2%; P < .001) compared with non-Hispanic Black, Mexican American, and other Hispanic individuals.

The mean (SD) follow-up time from interview date was 70.4 (35.1) months and did not differ significantly among the racial groups. Crude mortality rates for all-cause and CV mortality based on CV risk groups are shown in Figure 2. Over up to 10 years of follow-up, we observed an incremental increase in CV and all-cause mortality rates with increasing baseline risk (from 0.3% and 2.0% in the low-risk group to 14.6% and 49.4% in the high-risk group for CV and all-cause mortality, respectively; P < .001). Figure 3 presents the observed crude mortality rates by self-reported race and ethnicity and CV risk groups during the study’s follow-up period. Among individuals at high CV risk, the highest all-cause and CV mortality rates were observed among non-Hispanic White individuals (55.0% and 18.6%, respectively), and the lowest mortality rates were observed among Mexican American individuals (39.4% and 15.3%, respectively). Across all racial and Hispanic origin groups, we observed an increase in both all-cause and CV mortality in the higher-risk groups, with the highest increase observed among non-Hispanic White individuals (2.2% to 55.0% and 0.2% to 18.6% for all-cause and CV mortality, respectively).

Univariate HRs for mortality by CV risk and self-reported race and ethnicity are shown in eAppendix Tables 6 and 7. Univariate analyses revealed an association between a higher CV risk and increased all-cause and CV mortality in the entire cohort and across all racial and Hispanic origin groups.

Given differences in social determinants of health characteristics, analyses adjusted for the ratio of family income to poverty and education level are presented in Table 2 and eAppendix Table 8. When analyzed by predefined CV risk groups, individuals at borderline-intermediate and high CV risk demonstrated increased all-cause mortality (adjusted HR [AHR], 3.15 [95% CI, 2.27-4.37] and 6.20 [4.17-9.20], respectively; both P < .001) and CV mortality (AHR, 3.33 [95% CI, 1.51-7.33] and 9.37 [3.85-22.82], respectively; P = .003 and P < .001) (eAppendix Table 8). These findings were consistent across all racial and Hispanic origin groups, with increasing CV risk associated with increased CV and total mortality. There were no significant interactions in these outcomes across racial and Hispanic origin groups. However, because of the relatively small sample size, the increased risk did not reach statistical significance in all subgroups. We further assessed the risk across different racial and Hispanic origin groups according to CV risk groups and found no significant difference in CV or all-cause mortality (eAppendix Table 9).

Using a continuous model (Table 2), we observed a significant increase in all-cause (AHR, 1.54; 95% CI, 1.49-1.59; P < .001) and CV (AHR, 1.57; 95% CI, 1.46-1.62; P < .001) mortality risk for every 5% increase in the calculated CV risk. These findings were consistent across all racial and Hispanic origin groups, with 44% to 67% and 49% to 82% increased risk of all-cause and CV mortality, respectively, for every 5% increase in the calculated CV risk. There were no significant interactions in these associations among different racial and Hispanic origin groups. Furthermore, we found no significant difference in CV or all-cause mortality when we compared the mortality risk across different racial and Hispanic origin groups adjusted to the calculated CV risk (eAppendix Table 10).

DISCUSSION

The present study aimed to assess the association between baseline CV risk as determined by the PREVENT score and long-term all-cause and CV mortality across racial and Hispanic origin groups. The PREVENT equation offers a contemporary risk assessment tool that does not include race as a factor in risk calculation, raising concerns about its performance in different ethnic and racial groups.14 Our analysis of a large cohort representing more than 177 million adults in the US demonstrated several key findings with implications for CV risk stratification and mortality prediction among different racial groups. First, we observed a notable variation in baseline CV risk across different racial and ethnic groups. Individuals of Mexican American or other Hispanic origin exhibited the highest prevalence of low CV risk. The lowest rates of low CV risk and higher rates of high CV risk were observed in non-Hispanic Black and non-Hispanic White individuals. These findings suggest important disparities in baseline CV risk that should be considered when assessing CV health and mortality outcomes across diverse populations. Additionally, across all racial and ethnic groups, we observed an incremental increase in both CV and all-cause mortality rates with higher estimated baseline CV risk. Individuals at high CV risk demonstrated significantly elevated mortality rates compared with those at low or borderline-intermediate risk, emphasizing the prognostic utility of the PREVENT score in identifying individuals at heightened risk for adverse CV outcomes. This association remained consistent across all racial and ethnic groups, with no significant difference between groups, underscoring the prognostic utility of the PREVENT score in predicting mortality across all patient groups.

The PREVENT equation takes into account impaired CKM health in the CVD risk assessment.9 CKM health is the clinical presentation of the pathophysiological interactions among metabolic risk factors such as obesity, diabetes, chronic kidney disease (CKD), and CVD.15 Although the prevalence of poor CKM health is increasing in the US,7 it is disproportionately prevalent among racial and ethnic minority groups, primarily due to adverse social factors.16-18 We previously reported increased CV mortality related to obesity,6 diabetes,19 and CKD4 among racial and ethnic minority groups and socially deprived individuals. Using the PREVENT equation, in our cohort, non-Hispanic White individuals were most likely to be at high CV risk, a finding that may be explained by the older mean age in this group.

The higher age among non-Hispanic White individuals may also explain the highest crude all-cause and CV mortality rates in this group. However, it should be noted that we also observed lower prevalence of active smoking and diabetes among non-Hispanic White individuals at high CV risk compared with non-Hispanic Black, Mexican American, and other Hispanic individuals. The finding that non-Hispanic White individuals exhibited higher CV risk despite a lower prevalence of traditional risk factors such as diabetes and smoking is notable and warrants further exploration. One possible explanation is that non-Hispanic White individuals in our cohort were older on average, which may account for their elevated CV risk, as age is a significant determinant of CV health. This highlights the complex interplay among various risk factors and underscores the need for a more holistic approach to CV risk assessment, one that incorporates age, comorbidities, and other less traditional risk factors.

This finding also has important implications for future research. Studies should explore how nontraditional risk factors, such as chronic stress, environmental exposures, or genetic predispositions, might contribute to higher CV risk in populations with lower prevalence of diabetes and smoking. Furthermore, it suggests that interventions targeting CV health must go beyond traditional risk factors, especially in older populations in whom other determinants of health may play a larger role. Understanding these dynamics could lead to more targeted prevention strategies that consider the full spectrum of risk factors, including those that may not be immediately apparent from traditional metrics such as diabetes and smoking.

We found that compared with low-risk individuals, those at high CV risk as estimated by the PREVENT equation had 6- and 9-fold greater risks of all-cause and CV mortality, respectively, during up to a decade of follow-up. This increased risk was consistent across all racial and ethnic groups; every 5% increase in estimated 10-year CV risk was associated with 54% and 57% increases in all-cause and CV mortality, respectively (44%-67% and 49%-82% across different racial groups).

Our analysis reveals both the strengths and limitations of using a race-neutral model such as PREVENT across different racial and ethnic groups. Although we observed consistent associations between higher CV risk and mortality across all racial groups, the disparities in baseline risk levels—particularly among non-Hispanic Black and Mexican American populations—highlight the need for a more nuanced understanding of how structural and social determinants influence cardiovascular outcomes. The PREVENT equation excludes race, but future studies should explore how race can be complemented by additional contextual factors to better capture the unique CV risks faced by minority populations.

The findings of our study have important implications for clinical practice and public health initiatives aimed at reducing CV mortality and disparities. The PREVENT equation offers a valuable tool for identifying individuals at elevated CV risk, facilitating targeted interventions and risk management strategies to mitigate adverse outcomes. The absence of race in the PREVENT equation aims to reduce bias in clinical decision-making, but it also runs the risk of overlooking disparities that are deeply rooted in the lived experiences and environments of racial and ethnic minorities. For example, social determinants of health—such as access to health care, SES, and exposure to chronic stress—vary significantly across racial groups and can contribute to differences in CV outcomes. Our findings underscore the need for further research, which should consider whether additional factors (beyond those traditionally included in CV risk models) could improve the equation’s accuracy and utility in predicting risk for these populations. Exploring these differences now is critical to ensure that race-neutral tools such as PREVENT do not inadvertently contribute to inequities in CV care.

An essential aspect of our study is that we have demonstrated the robustness and effectiveness of the PREVENT score in stratifying CV risk across various racial and ethnic groups. This validation underscores the reliability and generalizability of the PREVENT score as a comprehensive risk assessment tool independent of race or ethnicity. The consistent association between baseline CV risk as assessed by the PREVENT score and long-term all-cause and CV mortality rates across different racial and ethnic groups reinforces its utility and relevance in clinical practice.

In the context of managed care, our findings support using the PREVENT equation as a valuable tool for standardizing CV risk assessment without relying on race as a variable. This aligns well with the goals of managed care systems, which prioritize efficiency and equity in delivering care to diverse populations. However, although the race-neutral approach reduces the potential for bias, it also raises concerns that it may obscure important disparities in care outcomes for racial and ethnic minority groups. Therefore, the PREVENT equation should be used while considering additional data on social determinants of health, such as SES, access to care, and behavioral factors. This could enhance the precision of risk stratification, ensuring that high-risk patients, regardless of race, receive timely and appropriate interventions.

Limitations

Despite the strengths of our study, including a large and diverse cohort, several limitations should be acknowledged. The observational nature of our analysis precludes causal inference, and residual confounding may have influenced our results despite adjustment for relevant covariates. The nature of data collection, reliance on questionnaire data, and exclusion of participants with incomplete data may also bias our results and limit their generalizability. To limit the number of excluded records, we used the basic PREVENT equations. It also should be noted that although access to specialty care resources, such as distance from hospitals or availability of health care providers, is recognized as an important determinant of CV health outcomes, our analysis did not include these factors. This decision was due to the unavailability of comprehensive data on health care access within the NHANES data set, which focuses primarily on clinical, demographic, and behavioral variables. We acknowledge that limited access to health care services can significantly influence CVD risk, particularly in underserved populations. Future studies should aim to incorporate these variables to better understand how health care accessibility interacts with CV risk, especially in diverse racial and ethnic groups. Furthermore, we relied on the causes of mortality as listed in the NHANES, which may be difficult to determine accurately. Additionally, the relatively small sample size within certain racial and ethnic subgroups limited the statistical power to detect significant interactions. Future research with larger, more diverse cohorts and longitudinal follow-up is needed to validate our findings and explore potential mechanisms underlying observed disparities in CV risk and mortality outcomes.

CONCLUSIONS

The results of our study demonstrate the utility of the PREVENT score in predicting long-term all-cause and CV mortality across diverse demographic groups. Although baseline estimated CV risk varies across racial and ethnic groups, the association between CV risk and mortality appears consistent across different racial and ethnic groups. These findings underscore the importance of proactive CV risk assessment and management strategies tailored to individual patient characteristics to optimize long-term outcomes and reduce disparities in CV health. As health care systems shift toward more personalized care models, integrating race-neutral tools such as PREVENT with contextual data on social determinants offers a path forward for reducing health disparities while maintaining equitable risk assessment frameworks in managed care environments. 

Author Affiliations: Keele Cardiovascular Research Group, Centre for Prognosis Research, Keele University (OK, MAM), Stoke-on-Trent, UK; Department of Cardiology, Hillel Yaffe Medical Center (OK, AR), Hadera, Israel; Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology (OK, AR), Haifa, Israel; Diabetes, Endocrinology and Metabolism Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre (MKR), Manchester, UK; Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester (MKR), Manchester, UK; Department of Metabolism, Digestion & Reproduction, Imperial College London (SM), London, UK; Division of Cardiology, Johns Hopkins University School of Medicine (EDM), Baltimore, MD; Aberdeen Cardiovascular and Diabetes Centre, Institute of Medical Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen (PKM), Aberdeen, UK; Division of Cardiothoracic Anesthesiology, Stanford University School of Medicine (LYS), Stanford, CA; National Institute for Health and Care Research, Birmingham Biomedical Research Centre (MAM), Birmingham, UK.

Source of Funding: None.

Author Disclosures: Dr Rutter has served as a consultant for Eli Lilly on matters unrelated to this article and owns stock in GSK. Dr Michos has served as a consultant for Amgen, Arrowhead, AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Edwards Lifescience, Ionis, Merck, Medtronic, New Amsterdam, Novartis, Novo Nordisk, Pfizer, and Zoll. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (OK, MKR, EDM, AR, LYS, MAM); acquisition of data (OK); analysis and interpretation of data (OK, SM, EDM, AR, LYS, MAM); drafting of the manuscript (OK, SM); critical revision of the manuscript for important intellectual content (MKR, SM, EDM, PKM, AR, LYS, MAM); statistical analysis (OK); administrative, technical, or logistic support (PKM, MAM); and supervision (MKR, PKM, MAM).

Address Correspondence to: Mamas A. Mamas, MD, Keele Cardiovascular Research Group, Centre for Prognosis Research, Keele University, David Weatherall Building, Keele, Staffordshire ST5 5BG, United Kingdom. Email: mamasmamas1@yahoo.co.uk.

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