Author + information
- Received May 1, 2018
- Revision received May 17, 2018
- Accepted May 21, 2018
- Published online September 3, 2018.
- Jennifer J. Stuart, ScDa,b,∗ (, )@BrighamWomens@HarvardChanSPH,
- Lauren J. Tanz, ScDa,b,
- Nancy R. Cook, ScDa,c,
- Donna Spiegelman, ScDa,d,e,
- Stacey A. Missmer, ScDa,f,g,
- Eric B. Rimm, ScDa,e,h,
- Kathryn M. Rexrode, MD, MPHb,c,
- Kenneth J. Mukamal, MD, MPHh,i and
- Janet W. Rich-Edwards, ScDa,b
- aDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- bDivision of Women’s Health, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- cDivision of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- dDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- eChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- fDivision of Adolescent and Young Adult Medicine, Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts
- gDepartment of Obstetrics, Gynecology, and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, Michigan
- hDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- iDivision of General Medicine and Primary Care, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- ↵∗Address for correspondence:
Jennifer J. Stuart, ScD, Division of Women’s Health, 1620 Tremont Street, 3rd Floor, Boston, Massachusetts 02120.
Background Hypertensive disorders of pregnancy (HDP) affect 10% to 15% of women and are associated with a 2-fold increased risk of cardiovascular disease (CVD).
Objectives This study sought to determine whether inclusion of HDP in an established CVD risk score improves prediction of CVD events in women.
Methods The analysis comprised 106,230 ≤10-year observations contributed by 67,406 women, age ≥40 years, free of prior CVD, with data available on model covariates in the Nurses’ Health Study II. Participants were followed up for confirmed myocardial infarction, fatal coronary heart disease, or stroke from 1989 to 2013. We fit an established CVD risk prediction model (Model A: age, total cholesterol and high-density lipoprotein cholesterol, systolic blood pressure, antihypertensive medication use, current smoking, diabetes mellitus) and compared it to the same model plus HDP and parity (Model B); Cox proportional hazards models were used to obtain predicted probabilities for 10-year CVD risk.
Results HDP and parity were associated with 10-year CVD risk independent of established CVD risk factors, overall and at ages 40 to 49 years. However, inclusion of HDP and parity in the risk prediction model did not improve discrimination (Model A: C-index = 0.691; Model B: C-index = 0.693; p value for difference = 0.31) or risk reclassification (net reclassification improvement = 0.4%; 95% confidence interval: −0.2 to 1.0%; p = 0.26).
Conclusions In this first test of the clinical utility of HDP and parity in CVD risk prediction, additional inclusion of HDP and parity in an established risk score did not improve discrimination or reclassification in this low-risk population; this might be because of the known associations between HDP and established CVD risk factors in the reference model.
Cardiovascular disease (CVD) is the leading cause of death among women in the United States (1). Although established CVD risk factors, including age, total and high-density lipoprotein (HDL) cholesterol, systolic blood pressure (SBP), treatment for high blood pressure, smoking, and diabetes mellitus, predict CVD in both men and women, several risk factors specific to women have emerged; these include pregnancy complications, such as hypertensive disorders of pregnancy (HDP; gestational hypertension and pre-eclampsia) (2–4). During their lifetime, 10% to 15% of parous women will develop HDP in at least 1 pregnancy, and these women carry a 2-fold increased risk of CVD (5–7). The American Heart Association (AHA) and American Stroke Association recognize HDP as a major risk factor for CVD, and since 2011, the AHA has recommended clinicians screen women for a history of these complications (8,9).
Clinical risk scores have long been used to direct targeted intervention and prevention. The pooled cohort risk equations (PCEs) were introduced and endorsed by the American College of Cardiology and the AHA in 2013, and since that time, they have been incorporated into clinical practice to predict 10-year risk of a first hard atherosclerotic CVD (ASCVD) event (10). Although these equations advanced prediction of CVD risk by incorporating stroke as an endpoint and creating sex- and race/ethnicity-specific equations, they have received criticism, and it has been suggested that the inclusion of novel risk markers could add value to the PCEs in clinical practice (11). Compared with other pregnancy complications, such as gestational diabetes and preterm delivery, HDP is consistently and, in general, more strongly related to future maternal CVD (3,5,6,12). Although previous studies have demonstrated differences in predicted CVD risk between women with and without a history of HDP and called for incorporation of HDP as an independent risk marker (7,13), to date, no study has tested the clinical utility of incorporating a history of HDP in CVD risk prediction.
To determine whether HDP predicts CVD above and beyond traditionally screened risk factors, or before those risk factors arise, we compared the performance of the PCE, an established CVD risk prediction model, to this model additionally including HDP; given the increased opportunity to develop HDP with each additional pregnancy, we additionally included parity in the model. We hypothesized that: 1) incorporating history of HDP into an established CVD risk score might capture high-risk women undetected by current CVD screening; and 2) the clinical utility of HDP in risk prediction would be strongest at, or even isolated to, younger ages, before the development of CVD risk factors known to be associated with HDP history and already included in the PCE.
The Nurses’ Health Study II (NHSII) is a longitudinal cohort study of 116,429 female U.S. registered nurses with repeated assessments of health-related behaviors, reproductive history, and ascertainment of incident disease through biennial questionnaires. Participants were enrolled in 1989, at 25 to 42 years of age.
HDP and parity exposures
Pregnancy history, including the number and years of births, pregnancy outcome, gestation length, complications, and infant characteristics, was self-reported for all lifetime pregnancies in 2009. History of HDP was defined as self-report of “pregnancy-related high blood pressure” (i.e., gestational hypertension) or “pre-eclampsia/toxemia.” To assess the validity of self-reported pre-eclampsia, we reviewed medical records for 598 women who reported pre-eclampsia on biennial questionnaires from 1991 to 2001 for provider report of pre-eclampsia or evidence of gestational hypertension (new-onset high blood pressure [SBP ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg] after 20 weeks’ gestation) and proteinuria (≥300 mg/24 h urine, protein-creatinine ratio ≥0.3, or dipstick reading of ≥1+) (14). There were 411 cases of medical record–confirmed pre-eclampsia, for a positive predictive value of 69%. After excluding medical records with insufficient information to confirm or reject a diagnosis (n = 136), the positive predictive value increased to 89%.
Established CVD risk factors
Current smoking and antihypertensive medication use were self-reported on all biennial questionnaires. Self-reported “current usual blood pressure (if checked within 2 years)” was provided within categories on the 1989 and 1999 biennial questionnaires; the midpoint of each category was assigned as the continuous value for analysis. Previous medical record review of self-reported high blood pressure in NHSII indicated good agreement, with 94% sensitivity and 85% specificity (15). Diabetes was self-reported as “diabetes: not during pregnancy” on the 1989 baseline questionnaire, and subsequent questionnaires captured incident diagnoses. Diagnoses were confirmed through a supplemental questionnaire, which provided information on diagnostic test results, symptoms, and treatment. Cases were then classified into the categories proposed by the National Diabetes Data Group and the American Diabetes Association, as described elsewhere (16–18). In a related cohort, type 2 diabetes mellitus diagnosis was confirmed through medical record review in more than 98% of women (19).
Measured plasma total and HDL cholesterol values were available for a subset of NHSII women; these values were used to derive predicted total and HDL cholesterol values for all NHSII participants (Online Appendix). To validate the predicted total and HDL cholesterol values, we compared measured values to predicted values for the 1997 and 1999 biennial questionnaires, which were most proximal to the time of the blood draw. Correlation coefficients between measured and predicted cholesterol values were 0.55 and 0.57 for total cholesterol and 0.52 and 0.50 for HDL cholesterol in 1997 and 1999, respectively.
At NHSII baseline in 1989, participants reported history of physician-diagnosed “myocardial infarction (MI) or angina” or “stroke (cerebrovascular accident) or transient ischemic attack (TIA).” Subsequent biennial questionnaires captured incident CVD events. Participants or next of kin granted permission for medical record review of incident CVD events during active follow-up. MI was confirmed by applying the World Health Organization criteria of acute symptoms and diagnostic electrocardiographic results or elevated cardiac enzymes (20,21). Fatal coronary heart disease (CHD) was confirmed by hospital or autopsy records or if CHD was listed as the cause of death on the death certificate for an individual with a history of CHD. Stroke was confirmed and classified according to evidence of neurological deficit with sudden or rapid onset that persisted for more than 24 hours or until death of a vascular cause, according to National Survey of Stroke criteria (22). Silent strokes discovered through radiologic imaging alone and cerebrovascular pathology that resulted from infection, trauma, or malignancy were not included. CVD events (nonfatal MI, fatal CHD, nonfatal or fatal stroke) confirmed by medical record review were considered definite cases, whereas events endorsed by the NHSII participant or a relative but for which medical records could not be obtained or permission for release was not provided were considered probable cases. Definite and probable cases of CHD and stroke constituted our CVD outcome of interest.
To fully utilize NHSII data in 10-year CVD risk prediction, we divided active follow-up into 3 independent time periods—1989 to 1993, 1994 to 2003, and 2004 to 2013—and allowed each woman to contribute person-time to the analysis from 1 or more periods. These 3 time periods (spanning 25 total years) were constructed backwards from 2013 to enable the 2 full 10-year time periods to come from the years when women would be at highest risk. Exclusion criteria were applied at the beginning of each time period. Because formal estimation of 10-year CVD risk is not recommended before age 40 years, women were excluded from follow-up during that interval if they were <40 years of age (23,24). Women were additionally excluded if they did not complete the 2009 biennial questionnaire (which ascertained lifetime pregnancy history and permitted dating of HDP exposure), had an extreme maternal age at delivery (≤13 or >50 years), were missing a valid year of pregnancy or were missing covariate information required for the reference prediction model, or developed a CVD event, died, or were lost to follow-up before the start of the interval (Figure 1). These exclusions yielded 106,230 observations contributed by 67,406 women across the 3 time periods. This analysis was approved by the Partners Human Research Committee, Brigham and Women’s Hospital.
Because the NHSII is composed predominantly of white women, the PCE for white women served as the reference model (Model A: established CVD risk factors) and included age (years), total cholesterol (mg/dl), HDL cholesterol (mg/dl), SBP (mm Hg), and indicators for antihypertensive medication use, current smoking, and diabetes mellitus (Online Appendix) (10).
For generalizability of our new model, we included both parous and nulliparous women in our primary analysis. Among parous women, each subsequent pregnancy presents an additional opportunity to develop HDP. Furthermore, the severity and outcome of a pregnancy complicated by HDP can affect the likelihood of future pregnancies. Previous research has also shown that the increased risk of cardiovascular death among women with pre-eclampsia in their first pregnancy is driven primarily by women who do not have another birth (25). Therefore, to flexibly model the complex interrelationship of HDP and parity and to avoid a zero cell (positivity) issue when modeling them separately (because nulliparous women cannot have a history of HDP), we cross-classified these variables into a 7-category exposure variable: nulliparous; normotensive para 1; normotensive para 2 (reference group); normotensive para ≥3; HDP para 1; HDP para 2; and HDP para ≥3. Thus, 6 indicator variables for HDP and parity were included in our new model (Model B: established CVD risk factors plus HDP and parity).
To assess the potential benefit of incorporating the novel risk markers (history of HDP and parity) into an established CVD risk prediction model, we followed the AHA’s scientific statement on evaluation of novel markers of cardiovascular risk (26). First, we fit a Cox proportional hazards model to estimate associations between established CVD risk factors with CVD events (Model A) (27). To assess the independence of HDP and parity in the presence of the established risk factors, we fit the same model but additionally including HDP and parity (Model B).
Women contributed person-time to the analysis from the start of each eligible interval (1989 to 1993, 1994 to 2003, and/or 2004 to 2013) until the CVD event of interest (non-fatal MI, fatal CHD, nonfatal or fatal stroke), death, last returned questionnaire, or end of the interval. The Cox proportional hazards model is able to accommodate varying lengths of time contributed by participants in estimating 10-year CVD risk (27). We tested for interactions between HDP and parity with both age and calendar time through likelihood ratio tests, comparing a model with and without multiplicative interaction terms between the HDP and parity exposure and 1) age and 2) time. No interactions or violations of the proportional hazards assumption were identified (p = 0.17 for age; p = 0.22 for time). Model fit, discrimination, and calibration statistics were calculated for each model, and the models were compared through the difference in the C indices and reclassification statistics, using publicly available macros (28) (Online Appendix). Given our pre-specified hypothesis that the model incorporating HDP and parity might perform better at younger ages, we also compared Model A and Model B stratified by age (40 to 49 and 50 to 59 years old at the start of the interval). Analyses were conducted using SAS software (version 9.4, SAS Institute, Inc., Cary, North Carolina).
Table 1 presents the age-adjusted baseline characteristics of study participants by time period of follow-up. Participants had a mean age of 47.2 ± 5.1 years. Eighty-two percent of participants were parous, and approximately 10% had a history of HDP. Women who contributed person-time from a later time period had higher total cholesterol levels, were more likely to be diabetic, and were less likely to be current smokers than women who contributed person-time from an earlier time period. Online Table 1 summarizes participant baseline characteristics by age at the start of follow-up (40 to 49 or 50 to 59 years).
By the end of follow-up, when participants were a median age of 60.6 years (interquartile range: 57.8 to 64.3 years), there were 685 first CVD events: 359 (52.4%) occurred among women ages 40 to 49 years at start of follow-up, and 326 (47.6%) occurred among women 50 to 59 years old at start of follow-up. The median age at CVD event was 56.3 years (interquartile range: 52.3 to 60.8 years).
HDP and parity were associated with 10-year CVD risk in crude analyses (Table 2). Specifically, there were statistically significant increased rates of CVD for women with a history of normotensive pregnancy and 1 birth (hazard ratio [HR]: 1.42; 95% confidence interval [CI]: 1.12 to 1.80) and for women with a history of HDP and 2 births (HR: 2.02; 95% CI: 1.50 to 2.72) compared with women with a history of normotensive pregnancies and 2 births. When HDP and parity were added to a model with established CVD risk factors, the increased risks associated with 1) a history of normotensive pregnancy and 1 birth and 2) a history of HDP and 2 births were attenuated but persisted overall and at ages 40 to 49 years (Table 3, Model B). However, HDP and parity were not independently associated with CVD risk in the older age range (50 to 59 years).
Table 4 presents the primary results of a comparison of Model A, based on established CVD risk factors, to Model B, which additionally included HDP and parity, in terms of model fit, discrimination, calibration, and reclassification for 10-year CVD risk prediction. Both models were adequately calibrated with p ≥ 0.05 (Table 4, Online Figure 1). The inclusion of HDP and parity did not obtain a better model fit overall or at ages 50 to 59 years but did obtain a significantly better fit to the data at ages 40 to 49 years (likelihood ratio test: p = 0.03). The change in the C index was not statistically significant in any model. The overall net reclassification index (NRI) was 0.4% (95% CI: −0.2% to 1.0%; p = 0.26) for risk reclassification across 3 risk groups (<5%, 5% to <10%, and ≥10%). The NRI was not statistically significant overall or in age-stratified models. However, the integrated discrimination improvement was statistically significant overall (0.02%; p < 0.001) and in age-stratified models (40 to 49 years: 0.04%; p < 0.001; 50 to 9 years: 0.006%; p = 0.04).
Table 5 illustrates the net reclassification of 10-year CVD risk into low-risk (<5%), intermediate-risk (5% to <10%), and high-risk (≥10%) groups among women who developed incident CVD and those who did not. The majority of women (99.7%) were classified as low risk by both models. Model B correctly reclassified 0.6% of previously low-risk women who developed CVD (n = 4) into a higher-risk group, yet it incorrectly reclassified 8.3% (n = 1) of previously intermediate-risk women as low risk. Online Tables 2 and 3 present the detailed reclassification results stratified by age. Online Tables 4 to 6 present the net reclassification of 10-year CVD risk into low- and high-risk groups based on the dichotomous cut point of 7.5% predicted CVD risk overall and by age.
To correct for potential overfitting, we adjusted for optimism using 1,000 bootstrap samples; conclusions did not change, with the exception that the integrated discrimination improvement statistic was no longer statistically significant (Online Table 7). Given the retrospective report of pregnancy history in 2009, we conducted a sensitivity analysis with follow-up from 2009 to 2013. HRs for HDP and parity in univariate models were similar and remained significant for women with a history of HDP and 2 births (HR: 1.75; 95% CI: 1.07 to 2.84) but was no longer significant for women with normotensive pregnancy and 1 birth (HR: 1.30; 95% CI: 0.89 to 1.91). When HDP and parity were added to the reference model in 2009 (when participants were 45 to 62 years of age), they were no longer statistically significant. To account for the fact that women were able to contribute multiple observations to the analysis across the 25 years of active follow-up, we also ran the Cox proportional hazards regression models using robust sandwich estimates for correlated data, and the CIs and p values were unchanged (data not shown) (29). Restricting the analysis to parous women, using only definite CVD cases, and evaluating pre-eclampsia, gestational hypertension, and parity separately in Model B did not change conclusions (data not shown). Given the low CVD risk observed in this study population, we also calculated a 2-category NRI with the cut point at the event rate, but conclusions were also unchanged (data not shown) (30).
To the best of our knowledge, this is the first study to investigate the clinical utility of incorporating history of HDP into CVD risk prediction. HDP and parity remained independently associated with 10-year risk of a first CVD event when added to a model with established CVD risk factors (age, total cholesterol, HDL cholesterol, SBP, treatment for high blood pressure, current smoking, and diabetes mellitus). These associations appeared largely driven by the stronger magnitude of relative risks observed among women 40 to 49 years of age. Inclusion of HDP and parity did not improve model discrimination or reclassification between risk groups in this prospective cohort study of 67,406 U.S. women at overall low risk for CVD (Central Illustration).
Because no single statistical measure is able to assess all contributions of a novel risk marker, we tested this through several methods: 1) confirming an independent association between HDP and parity with CVD, both alone and in the presence of established CVD risk factors; 2) reporting the discrimination of the new marker; and 3) reporting the accuracy of the new marker, with regard to calibration and reclassification (26). Because the C index is largely insensitive to change, it was not surprising to find that inclusion of HDP and parity did not improve predictive discrimination, despite being statistically significant and independently associated with CVD risk in this population (31,32). Measures of model fit based on the likelihood are more sensitive than the C index (33). Although the Bayesian information criterion was larger for the model that included HDP and parity, in part because of the penalty imposed for adding 6 new indicator variables, the likelihood ratio test indicated that the model with HDP and parity obtained a significantly better fit among women at ages 40 to 49 years.
Reclassification tables and statistics provide information more relevant to clinical decisions than the C index, which, although popular, should not be relied on solely in the evaluation of the contributions of a novel risk marker (31). HDP and parity did not improve net reclassification across categories of predicted risk, but this should be considered in light of the low underlying risk of CVD in NHSII participants. Because only 0.2% of women (n = 204) had a predicted 10-year CVD risk ≥5% based on Model A, the NHSII population also had little distribution across risk groups.
The increased CVD risk observed among normotensive women with 1 birth is consistent with previous literature demonstrating a J-shaped relationship between parity and CVD, with the nadir of risk falling around 2 births (34). In contrast to our finding, a recent study tested candidate reproductive risk factors for inclusion in a CVD risk prediction model and found that parity was not independently associated with CHD (35); however, information on HDP was not available in this study. Given the interaction between HDP and parity identified in the current study, it could be that the independent association of parity and CVD was not able to be captured without jointly considering a woman’s parity and history of HDP.
The primary limitation of our study is reliance on the nurse participants’ self-reported SBP and the use of predicted, rather than measured, total and HDL cholesterol levels. However, the misclassification induced by assigning the midpoint of self-reported categories as the continuous SBP value and using predicted, rather than measured, cholesterol would be expected to be nondifferential with respect to the other risk factors in the models and CVD. Furthermore, self-report of high blood pressure in the NHSII has demonstrated good agreement with medical record values (15), and predicted total and HDL cholesterol appeared well validated compared with measured values among women who provided blood samples. Self-reported pregnancy history could result in recall bias and exposure misclassification; however, a validation study of pre-eclampsia self-report in the cohort showed that the majority of women who self-reported pre-eclampsia had medical record evidence of the condition. Additionally, the proportions of pregnancies complicated by either gestational hypertension or pre-eclampsia are consistent with estimates observed elsewhere (7). Although pregnancy history, including HDP and parity, was retrospectively reported in 2009, length of recall has not been consistently associated with accuracy of maternal recall of HDP (36).
Despite the fact that inclusion in our analytic sample was dependent on survival to 2009, which can induce survivor bias, 98.2% of NHSII participants were alive in 2009. Although our sensitivity analysis with follow-up from 2009 to 2013 resulted in some attenuation, HDP and parity remained associated with CVD but were no longer independently associated once we controlled for established CVD risk factors. This sensitivity analysis is likely underpowered and also not surprising given the age range of 45 to 62 years in 2009 and the fact that our analysis among 50- to 59-year-olds similarly did not demonstrate an independent association of HDP and parity with CVD risk. Because the NHSII cohort includes primarily white nurses, these findings might not be generalizable. The use of the PCE for white women was largely appropriate for the NHSII population, but the impact of HDP and parity in CVD risk prediction could differ in other racial or ethnic groups.
Although HDP and parity did not contribute to model discrimination or reclassification of women across pre-established risk groups, they offer practical advantages over some traditional risk factors (ease of ascertainment, low cost, and availability earlier in a woman’s life) that might justify their inclusion in clinical assessment, particularly before women develop clinical CVD risk factors included in the PCE. In a related analysis, we found that the vast majority of the association between HDP and CVD was accounted for by chronic hypertension, type 2 diabetes mellitus, hypercholesterolemia, and weight changes after pregnancy (e.g., proportion mediated for pre-eclampsia: 71%; 95% CI: 13% to 98%) (37). This finding, taken together with the fact that HDP and parity are independently associated with CVD at ages 40 to 49 years but not at ages 50 to 59 years, suggests that these women could be identified at early ages to prevent development of established CVD risk factors. The magnitude of associations observed with HDP and parity are also comparable to those observed with other CVD risk factors or markers not incorporated in the PCE, such as family history of CVD. The contribution of HDP and parity in CVD risk prediction model discrimination and reclassification merits further investigation in populations with a greater risk distribution and overall higher CVD risk than the NHSII participants. It might also be the case that other pregnancy complications, such as pre-term delivery, that are also associated with CVD but for which we do not see the same extent of mediation of CVD risk by established risk factors might be better positioned to contribute to risk prediction in women (12).
In this first test of the clinical utility of HDP and parity in predicting a woman’s future risk of CVD, the additional inclusion of HDP and parity in risk prediction models did not improve model discrimination or risk reclassification across categories of predicted CVD risk, despite the fact that these novel markers were associated with CVD independent of established risk factors and improved model fit at ages 40 to 49 years.
COMPETENCY IN MEDICAL KNOWLEDGE: Hypertensive disorders of pregnancy and parity are associated with cardiovascular events independent of other risk factors, particularly at younger ages.
TRANSLATIONAL OUTLOOK: Further research is needed to test the impact of hypertensive disorders of pregnancy in diverse populations over the entire lifespan.
The National Institutes of Health funded this research through these grants: UM1 CA176726, R01 HL088521, R01 HL34594, and R01 CA67262. This work was supported by awards from the American Heart Association (12PRE9110014, 13GRNT17070022). Dr. Stuart was supported by training grant T32HL098048 from the National Heart, Lung, and Blood Institute, and by training grant T32HD060454 from the National Institute of Child Health and Human Development. Dr. Tanz was supported by F31HL131222 from the National Heart, Lung, and Blood Institute under the Ruth L. Kirschstein National Research Service Award. All authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- atherosclerotic cardiovascular disease
- coronary heart disease
- confidence interval
- cardiovascular disease
- high-density lipoprotein
- hypertensive disorders of pregnancy
- hazard ratio
- myocardial infarction
- Nurses’ Health Study II
- net reclassification improvement
- pooled cohort risk equation
- systolic blood pressure
- Received May 1, 2018.
- Revision received May 17, 2018.
- Accepted May 21, 2018.
- 2018 American College of Cardiology Foundation
- Benjamin E.J.,
- Virani S.S.,
- Callaway C.W.,
- et al.,
- on behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee
- Sattar N.,
- Greer I.A.
- Ahmed R.,
- Dunford J.,
- Mehran R.,
- Robson S.,
- Kunadian V.
- Bellamy L.,
- Casas J.P.,
- Hingorani A.D.,
- Williams D.J.
- Fraser A.,
- Nelson S.M.,
- Macdonald-Wallis C.,
- et al.
- Bushnell C.,
- McCullough L.D.,
- Awad I.A.,
- et al.,
- on behalf of the American Heart Association Stroke Council, Council on Cardiovascular and Stroke Nursing,
- Council on Clinical Cardiology,
- Council on Epidemiology and Prevention, and Council for High Blood Pressure Research
- Mosca L.,
- Benjamin E.J.,
- Berra K.,
- et al.
- Goff D.C. Jr..,
- Lloyd-Jones D.M.,
- Bennett G.,
- et al.
- Tanz L.J.,
- Stuart J.J.,
- Williams P.L.,
- et al.
- National Diabetes Data Group
- ↵(1997) Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 20:1183–1197.
- Alpert J.S.,
- Thygesen K.,
- Antman E.,
- Bassand J.P.
- World Health Organization
- Walker A.E.,
- Robins M.,
- Weinfeld F.D.
- Lloyd-Jones D.M.,
- Leip E.P.,
- Larson M.G.,
- et al.
- Pencina M.J.,
- D’Agostino R.B. Sr..,
- Larson M.G.,
- Massaro J.M.,
- Vasan R.S.
- Skjaerven R.,
- Wilcox A.J.,
- Klungsøyr K.,
- et al.
- Hlatky M.A.,
- Greenland P.,
- Arnett D.K.,
- et al.,
- on behalf of the American Heart Association Expert Panel on Subclinical Atherosclerotic Diseases and Emerging Risk Factors and the Stroke Council
- Cox D.R.,
- Oakes D.
- ↵Cook N. SAS macros. Risk Prediction Modeling: Division of Preventive Medicine (Brigham & Women’s Hospital). Available at: http://ncook.bwh.harvard.edu/sas-macros.html. Accessed March 13, 2017.
- Pencina M.J.,
- Steyerberg E.W.,
- D’Agostino R.B. Sr..
- Cook N.R.
- Harrell F.E. Jr..
- Parikh N.I.,
- Jeppson R.P.,
- Berger J.S.,
- et al.
- ↵Stuart JJ, Tanz LJ, Rimm EB, et al. Hypertensive disorders in first pregnancy and maternal cardiovascular disease: mediation by postpartum cardiovascular risk factors (abstr). Presented at: Society for Epidemiologic Research Annual Meeting; June 22, 2017; Seattle, WA.