Performance of the Atherosclerotic Cardiovascular Disease Pooled Cohort Risk Equations by Social Deprivation Status
Background The atherosclerosis cardiovascular disease (ASCVD) Pooled Cohort risk equations have shown different calibration across US populations with varied levels of social deprivation.
Methods and Results We analyzed the calibration and discrimination of the Pooled Cohort risk equations by social deprivation status among 9066 REGARDS (REasons for Geographic And Racial Differences in Stroke) study participants not taking statins for whom ASCVD risk may lead to statin initiation. Patients were aged 45 to 79 years, had no ASCVD or diabetes mellitus, and had a low‐density lipoprotein cholesterol level 70 to 189 mg/dL. Social deprivation was defined using 3 indicators: annual household income <$25 000, less than a high school education, and living without a partner. At baseline in 2003–2007, 54.6%, 27.4%, and 18.0% of participants had 0, 1, and 2 or 3 indicators showing deprivation, respectively. From baseline through December 2012, 457 participants developed ASCVD (nonfatal/fatal stroke, myocardial infarction, or coronary heart disease death). Predicted and observed ASCVD incidence per 1000 person‐years were 8.02 and 6.23 (95% CI, 5.31–7.31), respectively, among participants with 0 indicators of deprivation (Hosmer–Lemeshow P=0.01); 8.05 and 6.61 (95% CI, 5.29–8.24), respectively, with 1 indicator (P=0.09); and 9.83 and 11.40 (95% CI, 9.23–14.05), respectively, with 2 or 3 indicators (P=0.12). The C‐index (95% CI) was 0.72 (0.69–0.75), 0.73 (0.69–0.78), and 0.70 (0.65–0.75) among participants with 0, 1, and 2 or 3 indicators of deprivation, respectively. The net reclassification improvement after adding deprivation data to the Pooled Cohort risk equations was modest (0.12; 95% CI, 0.03–0.21).
Conclusions The Pooled Cohort risk equations have good calibration among individuals with social deprivation but overestimate ASCVD risk among those with less social deprivation.
In 2013, the American College of Cardiology (ACC) and the American Heart Association (AHA) published a set of equations for estimating 10‐year atherosclerotic cardiovascular disease (ASCVD) risk, the Pooled Cohort risk equations.1 Simultaneously, the ACC/AHA published a cholesterol management guideline that recommended using the Pooled Cohort risk equations to guide the decision to initiate statins.2 After these publications, the ACC/AHA Pooled Cohort risk equations were found to overestimate ASCVD risk in some contemporary US cohorts, including Women's Health Study (WHS), Physicians' Health Study (PHS), and Women's Health Initiative (WHI).3, 4 Participants included in the WHS and PHS were health professionals, and >40% of women enrolled in the WHI had a managerial or professional occupation.5, 6, 7 In contrast, results from REasons for Geographic And Racial Differences in Stroke (REGARDS) study suggest that the Pooled Cohort risk equations have good calibration among white and black adults from the general US population for whom ASCVD‐predicted risk may lead a discussion of statin initiation.8
Social deprivation is associated with increased risk for incident ASCVD.9, 10, 11 Studies from Europe have found that some cardiovascular risk prediction models show substantial differences in calibration across deprivation levels.12, 13 Therefore, it is possible that the calibration of the Pooled Cohort risk equations differs by social deprivation status, which may have contributed to the mismatch between predicted and observed ASCVD risk found in some US cohorts. We used data from the REGARDS study to investigate whether the calibration and discrimination of the ASCVD Pooled Cohort risk equations differ by levels of social deprivation. If the calibration or discrimination of the ASCVD Pooled Cohort risk equations are different across levels of social deprivation, this would support the need to include data on social deprivation for ASCVD risk prediction in the United States. We further investigated whether adding indicators of social deprivation would improve the discrimination of the ASCVD Pooled Cohort risk equations.
The REGARDS study was designed to investigate the reasons underlying the higher rate of stroke mortality among blacks compared with whites, and among residents of the Southeastern United States versus other US regions.14 Coronary heart disease (CHD) events are being identified and adjudicated in an ancillary study.15 A total of 30 239 participants 45 years and older from all 48 contiguous US states and the District of Columbia were enrolled between January 2003 and October 2007. For the present analysis, we included REGARDS study participants for whom ASCVD risk may lead to consideration of statin initiation: those aged 45 to 79 years without a history of ASCVD or diabetes mellitus who were not taking statins and had fasting low‐density lipoprotein cholesterol 70 to 189 mg/dL or non–high‐density lipoprotein cholesterol (non–HDL‐C) 100 to 219 mg/dL (see below).2 We excluded participants with atrial fibrillation or heart failure because they were not included in the population used to develop the ASCVD Pooled Cohort risk equations.1 Participants with no follow‐up to detect incident ASCVD, missing data to calculate 10‐year ASCVD‐predicted risk, or missing data on income, education, or relationship status (married, living with someone in married‐like relationship) were excluded. Overall, 9066 REGARDS study participants were included in the current analysis (Figure S1). The REGARDS study protocol was approved by the institutional review boards governing research in human subjects at the participating centers. All participants provided written informed consent.
A computer‐assisted telephone interview was used to collect self‐reported information on participants' age, race, sex, education, annual household income, place of residence, relationship status, current smoking, history of comorbid conditions (eg, myocardial infarction [MI], stroke, diabetes mellitus, atrial fibrillation) and vascular interventions (eg, coronary and lower extremity revascularization procedures, aortic aneurysm repair surgery), and use of antihypertensive, antidiabetes, and lipid‐lowering medications. Trained health professionals conducted an in‐home examination following standardized protocols. Procedures included 2 blood pressure measurements that were averaged, electrocardiography, collection of blood samples, and an inventory of all medications taken during the 2‐week period prior to the study visit. Serum total cholesterol, HDL‐C, triglyceride, and glucose levels were measured by colorimetric reflectance spectrophotometry using the collected blood samples. For participants with fasting triglycerides <400 mg/dL, low‐density lipoprotein cholesterol was calculated using the Friedewald equation.16 For participants who did not fast (n=1369) or who had triglycerides ≥400 mg/dL (n=91), non–HDL‐C was calculated as total cholesterol minus HDL‐C.
History of ASCVD at baseline was defined as self‐report of a physician diagnosis of MI or stroke; a prior coronary artery bypass, coronary angioplasty or stenting; a lower extremity revascularization procedure or an aortic aneurysm repair surgery; or evidence of a previous MI on the study electrocardiogram. Diabetes mellitus was defined as fasting glucose ≥126 mg/dL or nonfasting glucose ≥200 mg/dL, or self‐report of a prior diagnosis of diabetes mellitus with current use of antidiabetes medications. Atrial fibrillation was defined using the baseline electrocardiogram or self‐report of a physician diagnosis. Information on heart failure was not ascertained at baseline. Therefore, we defined prevalent heart failure as treatment with digoxin based on the study inventory of medications.17 We defined statin use through the medication inventory or by self‐reported use of lipid‐lowering medications.
We studied 3 indicators of social deprivation that can be easily assessed in clinical practice: self‐reported annual household income, education, and relationship status. Reporting an annual household income <$25 000, having less than a high school education, and living without a partner were each defined as deprivation. Living without a partner was defined by reporting a marital status other than married (ie, single, divorced, widowed, or other) and responding “no” to the question “Are you currently living with someone in a marriage‐like relationship?” For secondary analyses, social deprivation was defined using the area deprivation index (ADI) based on participants' census tracts.18 Higher values on the ADI represent more social deprivation.
Living REGARDS participants or proxy respondents were contacted every 6 months via telephone to assess incident stroke or CHD events. When nonfatal events were reported, medical records were retrieved for adjudication. Stroke events were confirmed by a panel of experts following the World Health Organization definition.19 Nonfatal MIs were adjudicated by trained clinicians following published guidelines.20, 21, 22, 23 When deaths were reported, interviews with next‐of‐kin or proxies, medical records in the last year of life, death certificates, and autopsy reports were used to determine whether a stroke or a CHD event was the main underlying cause. We defined incident ASCVD as an incident nonfatal or fatal stroke, nonfatal MI, or CHD death.2 Stroke and CHD events were adjudicated through December 31, 2012.
We calculated the cumulative number of indicators showing deprivation for each participant, with possible values ranging from 0 (less social deprivation) to 3 (more social deprivation). Baseline characteristics of participants were calculated by the number of indicators showing deprivation (0, 1, and 2 or 3).
Calibration and discrimination of the Pooled Cohort risk equations were assessed in strata defined by the cumulative number of indicators showing deprivation and by categories of each indicator of social deprivation status, separately. For calibration, observed and predicted number of ASCVD events at 5 years were calculated by quintiles of predicted risk and compared using a modified Hosmer‐Lemeshow χ2 statistic.24 A 5‐year observation period was selected because REGARDS participants currently have less than 10 years of follow‐up (median follow‐up 7.0 years; maximum follow‐up 9.9 years). We used the Kaplan–Meier method to calculate the cumulative incidence of ASCVD and the observed number of ASCVD events at 5 years.25 The predicted number of events at 5 years was estimated as shown in Table S1.8 Discrimination was evaluated using the Harrell's C‐index.26 We conducted a sensitivity analysis of the calibration and discrimination of the Pooled Cohort risk equations using multiple imputation to include participants with missing data to calculate 10‐year ASCVD‐predicted risk (n=528) and indicators of social deprivation status (n=1174).
Cox‐proportional hazard models were used to analyze the association of the number of indicators showing deprivation and, separately, each indicator of social deprivation status with ASCVD. In addition to a crude model, 2 levels of adjustment were performed. Model 1 adjusted for age, sex, and race. Model 2 adjusted for variables in model 1 plus the remaining variables in the Pooled Cohort risk equations (ie, total cholesterol, HDL‐C, systolic blood pressure, use of antihypertensive medications, and smoking status).
For each race‐sex group, we developed best‐fit Cox‐proportional hazard regression models for incident ASCVD including variables in the Pooled Cohort risk equations. Next, we added the cumulative number of indicators showing deprivation to the best‐fit models. The C‐index for these models were calculated and change in risk classification across the models was analyzed using the continuous net reclassification improvement method.27 The C‐index and net reclassification improvement were also calculated after adding annual household income, education, and relationship status, separately to the best‐fit models. Proportional hazards assumptions were evaluated by plots (ie, the log (‐log (survival)) plot) and adding interaction terms between social deprivation status and the log of follow‐up time. These assumptions were not violated.
In a secondary analysis, we assessed the calibration and discrimination of the ASCVD Pooled Cohort risk equations in strata defined by quartiles of the ADI. We used chained equations in Stata/I.C. 12.1 (Stata Corporation, College Station, TX) to impute 12 data sets for analyses using multiple imputation.28 All other analyses were conducted in SAS 9.3 (SAS Institute Inc., Cary, NC) using a level of significance α<0.05.
Among participants included in the analysis, 4944 (54.6%), 2487 (27.4%), and 1635 (18.0%) had 0, 1, and 2 or 3 indicators showing deprivation, respectively. Participants with a higher cumulative number of indicators showing deprivation were older, less likely to be male, and more likely to be black, current smokers, and taking antihypertensive medication (Table 1).
A total of 457 incident ASCVD events occurred during 59 648 person‐years of follow‐up. Participants with more indicators showing deprivation had a higher incidence of ASCVD (Figure and Table 2, top panel). Among participants with 0 or 1 indicator showing deprivation, the Pooled Cohort risk equations overestimated ASCVD risk (Table S2). However, the Pooled Cohort risk equations underestimated ASCVD risk among participants with 2 or 3 indicators showing deprivation. Discrimination of the Pooled Cohort risk equations was similar among participants with 0, 1, and 2 or 3 indicators showing deprivation.
Participants with lower income, less education, and living without a partner, separately, had a higher 5‐year incidence of ASCVD (Table 2, bottom panel). The ASCVD Pooled Cohort risk equations had good calibration among participants with an annual household income <$25 000 and living without a partner, but underestimated ASCVD risk among those with less than high a school education (Tables S3 through S5). The ASCVD Pooled Cohort risk equations overestimated ASCVD risk among individuals with annual household income ≥$25 000, with high school education and above, and those living with a partner. Discrimination of the ASCVD Pooled Cohort risk equations was similar across categories of each indicator of social deprivation status. Results were similar in sensitivity analyses using multiple imputation (Table S6).
Having 2 or 3 versus 0 indicators showing deprivation, and, separately, lower annual household income and less education were associated with an increased hazard ratio for ASCVD after adjustment for variables in the ASCVD Pooled Cohort risk equations (Table 3). Relationship status was not associated with ASCVD risk after adjustment for variables in the Pooled Cohort risk equations. Adding the cumulative number of indicators showing deprivation and, separately, annual household income, improved risk classification using the best‐fit Cox regression models (Table 4). Adding education and relationship status did not improve risk classification.
Participants with a higher ADI had a higher 5‐year incidence of ASCVD (Table S7). Calibration of the ASCVD Pooled Cohort risk equations was good (each χ2 P>0.15) with the exception of participants with an ADI in the second lowest quartile (ADI range: 96.7 to <106.5; χ2 P=0.03; Table S8). ADI was not associated with incident ASCVD after adjustment for variables in the ASCVD Pooled Cohort risk equations (Table S9).
In the current analysis of a large population‐based cohort of black and white US adults, the Pooled Cohort risk equations had good calibration or underestimated ASCVD risk among participants with more social deprivation defined using information on annual household income, education, and relationship status. However, the equations overestimated ASCVD risk among participants with less social deprivation. There was a graded increase in the incidence of ASCVD with more social deprivation, and adding information on social deprivation resulted in a modest improvement in risk classification of the ASCVD Pooled Cohort risk equations.
The ASCVD Pooled Cohort risk equations were developed using data from the Framingham Original and Offspring studies, ARIC (Atherosclerosis Risk in Communities) study, CHS (Cardiovascular Health Study), and CARDIA (Coronary Artery Risk Development in Young Adults) study.1 These US cohorts, which included participants from the general population, were conducted in the era before pharmacologic therapies including statins and aspirin were commonly used for ASCVD prevention.1, 29 After their publication in 2013, the ASCVD Pooled Cohort risk equations were assessed using data from more contemporary US cohorts. The Pooled Cohort risk equations overestimated ASCVD risk in the WHS, PHS, and WHI, which mainly included adults without social deprivation.3, 4 In addition, the Pooled Cohort risk equations overestimated ASCVD risk in MESA (Multi‐Ethnic Study of Atherosclerosis) study.30 In contrast, the ASCVD Pooled Cohort risk equations had good calibration among individuals for whom predicted risk may lead to consideration of statin initiation using data from the REGARDS study, a cohort with a high degree of generalizability to black and white US adults.8 Several factors have been suggested that could explain the mismatch between the ASCVD risk predicted by the Pooled Cohort risk equations with that observed in some contemporary US cohorts, including an increased use of pharmacologic risk reduction therapies (eg, statins, aspirin), a decline in ASCVD incidence in the general population, and a multiethnic composition.29, 31, 32 The current analysis suggests that selecting populations without social deprivation may have also contributed to this mismatch.
Social deprivation has been defined using many different indicators including income, education, social connections, and neighborhood of residency.9, 10 Prior studies have shown that social deprivation is associated with ASCVD incidence independently of cardiovascular risk factors commonly used for risk prediction.33, 34, 35 In the ARIC study, after adjustment for the Framingham CHD Risk Score, participants without a history of CHD or diabetes diabetes mellitus who had <12 years of education or an annual income <150% of the federal poverty level had a hazard ratio of 1.58 (95% CI, 1.31–1.90) for CHD compared with their counterparts with ≥12 years of education and an annual income ≥150% of the federal poverty level.33 In the current analysis, social deprivation was also associated with incident ASCVD after adjusting for variables included in the Pooled Cohort risk equations. Pharmacologic therapies including statins and aspirin are effective to reduce ASCVD risk.36, 37 However, individuals with social deprivation may be less likely to initiate and be adherent to these therapies.10, 38 In a previous analysis of the REGARDS study, the multivariable‐adjusted prevalence ratio for statin use at baseline associated with an annual income <$20 000 versus ≥$20 000 was 0.94 (95% CI, 0.88–1.00) among high‐risk participants.39 Therefore, a lower use of pharmacologic therapies may contribute to the higher ASCVD risk associated with social deprivation. Other mechanisms that may contribute to an increased risk for ASCVD among individuals with social deprivation include lifestyle factors (eg, unhealthy diet and low physical activity), less social support, an unfavorable residential environment, and high levels of stress.10, 11
Adding a new factor to risk prediction models may be perceived as unimportant as this commonly leads to a very small improvement in discrimination.40 However, adding factors to risk prediction models could also contribute to improvements in the calibration in specific subpopulations. Results from the REGARDS study suggest that, although the Pooled Cohort risk equations seem to have a good calibration among US adults for whom ASCVD‐predicted risk should lead to a discussion of statin initiation, these equations may overestimate risk among those with less social deprivation. Results in the REGARDS study are consistent with prior studies conducted in Europe showing that prediction models, which do not incorporate data on social deprivation status, could underestimate cardiovascular risk among adults with deprivation and/or overestimate risk among those without deprivation.12, 13 This could occur when social deprivation remains associated with higher ASCVD risk after adjusting for variables included in the prediction model. Therefore, at each level of predicted risk, observed risk would be higher among individuals with more social deprivation, leading to a differential calibration by social deprivation levels. A mismatch between observed and predicted ASCVD risk by social deprivation levels could have substantial consequences when used to guide statin therapy initiation. Underestimation of ASCVD risk among adults with social deprivation may lead to underuse of statins in this population, contributing to disparities in ASCVD. Overestimation of ASCVD risk among those without deprivation may lead to unnecessary treatment, more statin‐related adverse events and higher costs. Therefore, future studies aimed at developing or improving ASCVD risk prediction models in the United States should consider including social deprivation data to achieve good calibration across populations with and without deprivation.
Prior studies have found area‐level indicators of social deprivation to be associated with a higher ASCVD risk.41, 42, 43 Using area‐level information for ASCVD risk prediction in clinical practice is compelling because individuals may be reluctant to report social deprivation data.11 However, people living in the same area may show substantial heterogeneity in their individual levels of deprivation, which may result in area‐level indicators providing low discrimination. An area‐level indicator of social deprivation, the Townsend score, was associated with cardiovascular risk after adjusting for risk factors in the United Kingdom and is incorporated into the QRISK.43, 44 Also, the Scottish risk prediction model ASSIGN incorporates an area‐level indicator of social deprivation, the Scottish Index of Multiple Deprivation.41 In the current study, we used the ADI, a composite score derived from US census data, as an area‐level indicator of social deprivation. Among US adults, the ADI has been associated with all‐cause mortality and 30‐day rehospitalization.18, 45 Results from the current analysis suggest that the ADI may not be independently associated with ASCVD risk and should not be used for ASCVD risk prediction among US adults.
The 2013 ACC/AHA cholesterol management guidelines recommend that ASCVD‐predicted risk should be used to start a clinician‐patient discussion on statin therapy initiation.2 In addition to predicted ASCVD risk, clinicians and patients should discuss potential risk‐reduction benefits and adverse effects associated with statin therapy, heart‐healthy lifestyles, management of other risk factors not included in the Pooled Cohort risk equations, and patient preferences. Results from the current analysis suggest that clinicians should also consider social deprivation when discussing statin initiation. Few data are currently available on adherence to the 2013 ACC/AHA cholesterol management guideline in clinical practice, including the use of the Pooled Cohort risk equations to inform a shared decision to initiate statins. Adherence to the ACC/AHA cholesterol management guideline may be lower among patients with social deprivation as it has been suggested that those who have lower income or education are less likely to be involved in treatment decisions.46 Future studies should investigate adherence to the 2013 ACC/AHA cholesterol management guideline and whether this differs by patients' social deprivation status.
Study Strengths and Limitations
Strengths of the current analysis include the large number of participants with information on 3 indicators of social deprivation and factors to calculate ASCVD risk using the Pooled Cohort risk equations. Data collection in the REGARDS study followed standardized protocols and CHD and stroke events were adjudicated by trained personnel following published recommendations. Despite these strengths, the results of our study should be interpreted in the context of known and potential limitations. We used digoxin use as a proxy for heart failure as this was not assessed at baseline in the REGARDS study. Therefore, our analysis may have included some participants with heart failure who were not taking digoxin. Follow‐up of REGARDS study participants is currently ongoing and data were only available to calculate observed ASCVD risk at 5 years. The REGARDS study does not have active surveillance to detect ASCVD events that may have not been reported by participants or proxies, which could have led to an underestimation of the ASCVD risk in this cohort. The ADI uses 2000 US census data and may not represent the environment of REGARDS study participants at the time of their enrollment in 2003–2007.
Results from the current study suggest that ASCVD risk predicted by the Pooled Cohort risk equations may mismatch observed risk by social deprivation levels. Specifically, the Pooled Cohort risk equations may overestimate ASCVD risk among individuals with less social deprivation but may have good calibration or underestimate ASCVD risk among those with more social deprivation. Future ASCVD risk prediction equations could add information on social deprivation status to reduce the mismatch across groups with different levels of deprivation. Meanwhile, clinicians and patients should take social deprivation into consideration, in addition to ASCVD‐predicted risk and other risk factors, when discussing statin initiation.
Sources of Funding
This research project is supported by a cooperative agreement U01‐NS041588 from the National Institute of Neurological Disorders and Stroke, the National Institutes of Health, and the Department of Health and Human Service. Representatives of the funding agency have been involved in the review of the manuscript but not directly involved in the collection, management, analysis, or interpretation of the data. Additional support was provided by grants R01‐HL080477 and K24‐HL111154 from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health.
Carson, Safford and Muntner receive grant support from Amgen Inc. Colantonio, Richman, Lloyd‐Jones, Howard, Deng, Howard, and Goff have no disclosures to report.
Table S1. Estimation of Race‐ and Sex‐Specific Risk for Atherosclerosis Cardiovascular Disease Using the Pooled Cohort Risk Equations
Table S2. Calibration of the Atherosclerotic Cardiovascular Disease Pooled Cohort Risk Equations by the Cumulative Number of Indicators Showing Deprivation
Table S3. Calibration of the Atherosclerotic Cardiovascular Disease Pooled Cohort Risk Equations by Annual Household Income
Table S4. Calibration of the Atherosclerotic Cardiovascular Disease Pooled Cohort Risk Equations by Education
Table S5. Calibration of the Atherosclerotic Cardiovascular Disease Pooled Cohort Risk Equations by Relationship Status
Table S6. Observed and Predicted Incidence Rates of Atherosclerotic Cardiovascular Disease and Calibration and Discrimination of the Pooled Cohort Risk Equations by Social Deprivation Status (Using Multiple Imputation With 12 Imputed Data Sets, n=10 768)
Table S7. Observed and Predicted Incidence Rates of Atherosclerotic Cardiovascular Disease and Calibration and Discrimination of the Pooled Cohort Risk Equations by Area Deprivation Index
Table S8. Calibration of the Atherosclerotic Cardiovascular Disease Pooled Cohort Risk Equations by Area Deprivation Index
Table S9. Hazard Ratios for Atherosclerosis Cardiovascular Disease Associated With Categories of Area Deprivation Index
Figure S1. Flow‐chart of REGARDS study participants included in the analysis.
The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions and further information about the study can be found at http://www.regardsstudy.org.
This work was presented as a poster at the Epidemiology and Prevention/Lifestyle and Cardiometabolic Health Scientific Sessions, March 1–4, 2016, in Phoenix, AZ.
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