General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart
`Study
`Ralph B. D'Agostino, Sr, Ramachandran S. Vasan, Michael J. Pencina, Philip A. Wolf, Mark
`Cobain, Joseph M. Massaro and William B. Kannel
`
`Circulation
`
`Circulation.
`
`2008;117:743-753; originally published online January 22, 2008;
`doi: 10.1161/CIRCULATIONAHA.107.699579
`is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231
`Copyright © 2008 American Heart Association, Inc. All rights reserved.
`Print ISSN: 0009-7322. Online ISSN: 1524-4539
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`http://circ.ahajournals.org/content/118/4/e86.full.pdf
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`http://circ.ahajournals.org/content/suppl/2008/01/22/CIRCULATIONAHA.107.699579.DC1.html
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`

`

`Epidemiology
`
`General Cardiovascular Risk Profile for Use in Primary Care
`The Framingham Heart Study
`
`Ralph B. D’Agostino, Sr, PhD; Ramachandran S. Vasan, MD; Michael J. Pencina, PhD;
`Philip A. Wolf, MD; Mark Cobain, PhD; Joseph M. Massaro, PhD; William B. Kannel, MD
`
`Background—Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic
`cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease,
`and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD
`and of its constituents.
`Methods and Results—We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event
`in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between
`30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions (“general CVD” algorithms)
`were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for
`hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting
`individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of
`follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted
`CVD risk (multivariable-adjusted P⬍0.0001). The general CVD algorithm demonstrated good discrimination (C
`statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms
`allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional
`risk factors and the other based on non–laboratory-based predictors.
`Conclusions—A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and
`risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The
`estimated absolute CVD event rates can be used to quantify risk and to guide preventive care. (Circulation. 2008;117:
`743-753.)
`
`Key Words: cardiovascular diseases 䡲 coronary disease 䡲 heart failure 䡲 risk factors 䡲 stroke
`
`I t is widely accepted that age, sex, high blood pressure,
`
`smoking, dyslipidemia, and diabetes are the major risk
`factors for developing cardiovascular disease (CVD).1 It also
`is recognized that CVD risk factors cluster and interact
`multiplicatively to promote vascular risk.2 This knowledge
`led to the development of multivariable risk prediction
`algorithms incorporating these risk factors that can be used by
`primary care physicians to assess in individual patients the
`risk of developing all atherosclerotic CVD3–12 or specific
`components of CVD,
`ie, coronary heart disease,9,13–17
`stroke,18 peripheral vascular disease,19 or heart failure.20
`Multivariable assessment has been advocated to estimate
`absolute CVD risk and to guide treatment of risk factors.2,6
`For instance,
`the Framingham formulation for predicting
`coronary heart disease (CHD) was incorporated into the Third
`
`Report of the Expert Panel on Detection, Evaluation, and
`Treatment of High Blood Cholesterol
`in Adults (Adult
`Treatment Panel III).9 The Framingham CHD risk assessment
`tool has been validated in whites and blacks in the United
`States9,10,21 and are transportable (with calibration) to cultur-
`ally diverse populations in Europe, the Mediterranean region,
`and Asia.9,10,22,23 Similar CHD risk prediction algorithms
`have been developed by other investigators worldwide and
`have been demonstrated to perform well.14,15,17
`
`Clinical Perspective p 753
`
`Despite the availability of several validated risk prediction
`their use has lagged in primary care.24 One
`algorithms,
`potential reason for physician inertia in using risk prediction
`instruments is the multiplicity of such algorithms, each for
`
`Received February 27, 2007; accepted November 30, 2007.
`From Boston University, Department of Mathematics and Statistics (R.B.D., M.J.P.), School of Medicine (R.S.V., P.A.W., W.B.K.), and Department
`of Biostatistics (J.M.M.), Boston, Mass; Framingham Heart Study, Framingham, Mass (R.B.D., R.S.V., M.J.P., P.A.W., J.M.M., W.B.K.); and Unilever
`Research, Corporate Biology, Colworth Park, UK (M.C.).
`Guest Editor for this article was Eric B. Rimm, ScD.
`The online Data Supplement can be found with this article at http://circ.ahajournals.org/cgi/content/full/CIRCULATIONAHA.107.
`699579/DC1.
`Correspondence to R.B. D’Agostino, PhD, Chairman, Professor of Mathematics/Statistics and Public Health, Boston University, Department of
`Mathematics and Statistics, 111 Cummington St, Boston, MA 02215.
`© 2008 American Heart Association, Inc.
`
`Circulation is available at http://circ.ahajournals.org
`
`DOI: 10.1161/CIRCULATIONAHA.107.699579
`
`
`
`http://circ.ahajournals.org/Downloaded from
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`743
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`744
`
`Circulation
`
`February 12, 2008
`
`predicting an individual CVD component. Indeed, there are
`occasions when a physician would like to target risk assess-
`ment and preventive measures to a specific cardiovascular
`end point such as myocardial infarction or stroke depending,
`for example, on an individual patient’s family history, age,
`diabetic status, or predisposition to a particular outcome by
`valve disease. However, with this exception, primary care
`physicians engaged in preventive health maintenance want to
`assess risk of developing any major atherosclerotic CVD
`event using a general CVD risk assessment tool. Accordingly,
`the purpose of the present investigation was to formulate a
`single multivariable risk assessment tool that would enable
`physicians to identify high-risk candidates for any and all
`initial atherosclerotic CVD events using measurements
`readily available at the clinic or office.
`
`Methods
`
`Study Design and Sample
`The design and selection criteria for the original Framingham Heart
`Study and the Framingham Offspring Study have been detailed
`elsewhere.25,26 Detailed descriptions of the examination procedures
`and criteria for CVD events also have been reported.27 Participants
`were eligible for the present investigation if they attended the 11th
`biennial examination cycle of original cohort (1968 to 1971, when
`measurement of high-density lipoprotein [HDL] cholesterol was
`available) or the first (1971 to 1975) or third (1984 to 1987)
`examination cycles of the Offspring cohort and were free of CVD.
`All participants provided written informed consent, and the study
`protocol was approved by the Institutional Review Board at the
`Boston Medical Center.
`The study sample consisted of attendees of the baseline examina-
`tions free of prevalent CVD who were 30 to 74 years of age with
`nonmissing data on covariates. After exclusions, 8491 participants
`(mean age, 49 years; 4522 women) remained eligible.
`
`Measurement of CVD Risk Factors
`At each heart study examination, participants underwent a physical
`examination, anthropometry, blood pressure determination, and
`phlebotomy for vascular risk factors. Blood pressure measurements
`were made on the left arm of the seated participants with a
`mercury-column sphygmomanometer and an appropriately sized
`cuff; the average of 2 physician-obtained measures constituted the
`examination blood pressure. Serum total and HDL cholesterol levels
`were determined with standardized enzymatic methods. Cigarette
`smoking status was ascertained by self-report. Diabetes was defined
`as fasting glucose ⱖ126 mg/dL (offspring cohort) or 140 mg/dL
`(original cohort) or use of insulin or oral hypoglycemic medications.
`Antihypertensive medication use was ascertained by the physician
`examiner at the heart study and based on self-report.
`
`Follow-Up and Outcome Events
`All study participants were under continuous surveillance for the
`development of CVD events and death. The Framingham Heart
`Study defines CVD as a composite of CHD (coronary death,
`myocardial infarction, coronary insufficiency, and angina), cerebro-
`vascular events (including ischemic stroke, hemorrhagic stoke, and
`transient ischemic attack), peripheral artery disease (intermittent
`claudication), and heart failure.1 Information about CVD events on
`follow-up was obtained with the aid of medical histories, physical
`examinations at the study clinic, hospitalization records, and com-
`munication with personal physicians. All suspected new events were
`reviewed by a panel of 3 experienced investigators who evaluated all
`pertinent medical records. A separate review committee that in-
`cluded a neurologist adjudicated cerebrovascular events, and a heart
`study neurologist examined most participants with suspected stroke.
`
`Statistical Analyses
`
`Multivariable Models and Estimation of General CVD
`Risk Functions
`We used sex-specific Cox proportional-hazards regressions28 to
`relate risk factors to the incidence of a first CVD event during a
`maximum follow-up period of 12 years after confirming that the
`assumption of proportionality of hazards was met. From these
`models, we estimated mathematical CVD risk functions,28 referred to
`as a general CVD risk function (Appendix); these functions were
`used to estimate 10-year absolute CVD risk.
`Covariates included in Cox models were age, total cholesterol,
`HDL cholesterol, systolic blood pressure, antihypertensive medica-
`tion use, current smoking, and diabetes status. Other variables such
`as diastolic blood pressure, body mass index, and triglycerides also
`were considered, but they were not statistically significant. The use
`of low-density lipoprotein cholesterol did not improve model fit or
`performance. All the continuous variables were naturally logarith-
`mically transformed to improve discrimination and calibration of the
`models and to minimize the influence of extreme observations. We
`adjusted for the use of antihypertensive medication by modeling the
`impact of a participant’s systolic blood pressure differently on the
`basis of use of such medications.
`
`Assessment of Model Performance
`We evaluated the ability of the risk prediction model to discriminate
`persons who experience a CVD event from those who do not using
`an overall c statistic,29,30 expanding on a suggestion by Harrell et
`al.31 This c statistic is analogous to the area under the receiver-
`operating characteristic curve. Briefly, 2 subjects are described as
`comparable if we can determine which one survived longer and
`concordant if their predicted probabilities of survival and survival
`times go in the same direction, and we can define the overall c
`statistic as the probability of concordance given comparability. The
`degree of overoptimism resulting from model assessment on the
`same data on which it was developed was estimated on the basis of
`bootstrap resampling of the original set.
`We evaluated the calibration of our risk prediction model, a
`measure of agreement between observed and predicted events within
`10 years, using a modified Hosmer-Lemeshow ␹2 statistic with 9
`df.29 For this purpose, we used the Kaplan-Meier estimator to obtain
`the observed incidence of CVD events, which was then compared
`with the CVD risk predicted by the model and classified into
`deciles.29 We also calculated the proportion of CVD events that
`occurred in the top quintile of predicted risk (ie, sensitivity of the top
`quintile of predicted risk for identifying CVD events) and the
`proportion of individuals without events who are not in the top
`quintile of predicted risk (ie, specificity of the top quintile for CVD
`events).
`The performance of the new CVD risk prediction model presented
`here was compared with that of another popular Framingham risk
`score developed by Wilson et al.16 Because the latter score was
`developed for predicting CHD and not CVD, we performed a simple
`recalibration by multiplying the risk of each individual by the ratio
`of CVD incidence rate and the mean predicted risk based on the
`CHD risk function. Thus, we assessed how well the Framingham
`CHD risk functions16 predicted CVD relative to the new CVD
`prediction model. A test for difference in 2 correlated c statistics
`proposed by Antolini et al32 was used, along with the net reclassi-
`fication improvement proposed by Pencina et al.33 Reclassification
`improvement is defined as an increase in risk category for individ-
`uals who develop events and as a decrease for those who do not. Net
`reclassification improvement accounts for movement between cate-
`gories in the wrong direction and applies different weights to events
`and nonevents. We used 0% to 6%, 6% to 20%, and ⬎20% as risk
`categories.
`
`Performance of General CVD Risk Prediction Model for
`Predicting Individual CVD Components
`After generating sex-specific general CVD risk functions as detailed
`above, we applied them to predict the risk of individual components
`
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`

`D’Agostino et al
`
`General Cardiovascular Risk Profile
`
`745
`
`Table 1. Summary Statistics for Risk Factors Used in Risk
`Models
`
`Characteristics
`
`Women
`(n⫽4522, 28% FOC)
`
`Men
`(n⫽3969, 22% FOC)
`
`Age, mean (SD), y
`
`49.1 (11.1)
`
`Total-C, mean (SD), mg/dL
`
`215.1 (44.1)
`
`HDL-C, mean (SD), mg/dL
`
`57.6 (15.3)
`
`Systolic BP, mean (SD),
`mm Hg
`
`BP treatment, n (%)
`
`Smoking, n (%)
`
`Diabetes, n (%)
`
`125.8 (20.0)
`
`532 (11.76)
`
`1548 (34.23)
`
`170 (3.76)
`
`Incident CVD events, n (%)
`
`456 (10.08)
`
`48.5 (10.8)
`
`212.5 (39.3)
`
`44.9 (12.2)
`
`129.7 (17.6)
`
`402 (10.13)
`
`1398 (35.22)
`
`258 (6.50)
`
`718 (18.09)
`
`FOC indicates Framingham original cohort; Total-C, total cholesterol; HDL-C,
`HDL cholesterol; and BP, blood pressure.
`
`of CVD (CHD, stroke, intermittent claudication, congestive heart
`failure) after multiplication of the probability predicted by the
`general risk function by the proportion of all CVD events that were
`constituted by an individual component (ratio of Kaplan-Meier event
`rates). These were contrasted with models that we developed for
`individual CVD components using the same predictors.
`
`Sex-Specific General CVD Risk Scores Sheets
`and Heart Age
`General CVD risk functions were translated into sex-specific risk
`score sheets by use of previously described methods.34 To facilitate
`easier understanding of the concept of risk, we also constructed
`“heart age” sheets. An individual’s heart age is calculated as the age
`of a person with the same predicted risk but with all other risk factor
`levels in normal ranges. Although called heart age for simplicity of
`risk communication in primary care, the heart age really reflects
`vascular age. In the following, we use heart age/vascular age.
`
`Simpler CVD Risk Prediction Models Using
`Nonlaboratory Predictors Routinely Ascertained
`in Primary Care
`In addition to the main CVD risk prediction models described above,
`we developed simplified sex-specific models that used simple
`
`office-based predictors that are routinely obtained in primary care
`and do not require laboratory testing. These variables included age,
`body mass index, systolic blood pressure, antihypertensive medica-
`tion use, current smoking, and diabetes status. The same modeling
`principles and model assessment techniques were applied to these
`simplified models.
`The authors had full access to and take full responsibility for the
`integrity of the data. All authors have read and agree to the
`manuscript as written.
`
`Results
`The risk factor characteristics of men and women in our
`sample at the baseline examinations are shown in Table 1. In
`our middle-aged sample, mean levels of serum total choles-
`terol and systolic blood pressure were similar in men and
`women, as were the prevalences of cigarette smoking and use
`of antihypertensive treatment. The prevalence of diabetes was
`substantially higher in men, whereas mean serum HDL levels
`were higher in women.
`
`General CVD Risk Prediction Models
`The multivariable-adjusted regression coefficients and hazard
`ratios for incident CVD events are presented in Table 2. We
`observed highly statistically significant relations of all risk
`factors evaluated and incident CVD.
`The sex-specific CVD functions performed well in terms
`of both model discrimination and calibration. The c statistics
`for the risk function ranged from 0.763 (95% confidence
`interval [CI], 0.746 to 0.780) in men to 0.793 (95% CI, 0.772
`to 0.814) in women. The degree of overoptimism was
`estimated at 0.001 for men and 0.003 for women, partly
`reflecting a large number of events and the potential limita-
`tion of the bootstrap resampling approach for assessing
`overoptimism.
`The calibration ␹2 statistics for the CVD prediction models
`were 13.48 in men and 7.79 for the women,
`indicating
`excellent goodness of fit (for the lack of fit, P⫽0.14 and
`
`Table 2. Regression Coefficients and Hazard Ratios
`
`Variable
`
`␤*
`
`P
`
`Hazard Ratio
`
`95% CI
`
`Women 关So(10)⫽0.95012兴
`
`Log of age
`
`Log of total cholesterol
`
`Log of HDL cholesterol
`
`2.32888
`
`1.20904
`
`⫺0.70833
`
`⬍0.0001
`
`⬍0.0001
`
`⬍0.0001
`
`10.27
`
`3.35
`
`0.49
`
`(5.65–18.64)
`
`(2.00–5.62)
`
`(0.35–0.69)
`
`Log of SBP if not treated
`
`Log of SBP if treated
`
`Smoking
`
`Diabetes
`
`Men 关So(10)⫽0.88936兴
`
`Log of age
`
`Log of total cholesterol
`
`Log of HDL cholesterol
`
`Log of SBP if not treated
`
`Log of SBP if treated
`
`Smoking
`
`Diabetes
`
`2.76157
`
`2.82263
`
`0.52873
`
`0.69154
`
`3.06117
`
`1.12370
`
`⫺0.93263
`
`1.93303
`
`1.99881
`
`0.65451
`
`0.57367
`
`⬍0.0001
`
`⬍0.0001
`
`⬍0.0001
`
`⬍0.0001
`
`⬍0.0001
`
`⬍0.0001
`
`⬍0.0001
`
`⬍0.0001
`
`⬍0.0001
`
`⬍0.0001
`
`⬍0.0001
`
`So(10) indicates 10-year baseline survival; SBP, systolic blood pressure.
`*Estimated regression coefficient
`
`15.82
`
`16.82
`
`1.70
`
`2.00
`
`21.35
`
`3.08
`
`0.39
`
`6.91
`
`7.38
`
`1.92
`
`1.78
`
`(7.86–31.87)
`
`(8.46–33.46)
`
`(1.40–2.06)
`
`(1.49–2.67)
`
`(14.03–32.48)
`
`(2.05–4.62)
`
`(0.30–0.52)
`
`(3.91–12.20)
`
`(4.22–12.92)
`
`(1.65–2.24)
`
`(1.43–2.20)
`
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`
`Performance Summary: Modified CVD Model Versus
`Table 3.
`Event-Specific Own Model for Women
`
`CHD (n⫽216)
`
`C
`
`95% CI for C
`
`␹2
`
`P for ␹2
`
`Sensitivity of top quintile
`
`Specificity of top quintile
`
`Calibration factor
`
`So(10)
`
`Stroke (n⫽84)
`
`C
`
`95% CI for C
`
`␹2
`
`P for ␹2
`
`Sensitivity of top quintile
`
`Specificity of top quintile
`
`Calibration Factor
`
`So(10)
`
`CHF (n⫽44)
`
`CVD Model
`
`Own Model
`
`0.787
`
`0.789
`
`(0.762–0.812)
`
`(0.764–0.815)
`
`14.79
`
`0.097
`
`57.55
`
`81.94
`
`0.6086
`
`17.52
`
`0.041
`
`56.38
`
`81.88
`
`0.9704
`
`0.769
`
`0.774
`
`(0.715–0.822)
`
`(0.721–0.828)
`
`5.26
`
`0.811
`
`61.56
`
`80.82
`
`0.2385
`
`6.86
`
`0.651
`
`63.91
`
`80.86
`
`0.9898
`
`Figure. Calibration by decile for CVD function for women (A)
`and men (B). Vertical bars represent observed (Kaplan-Meier
`[km]; black) and model-based predicted (decile specific means;
`gray) probabilities of CVD event in 10 years in deciles of model-
`based predicted probabilities.
`
`P⫽0.56, respectively). The Figure displays the calibration
`plots comparing predicted deciles of risk and actual observed
`risk in men and women. The top sex-specific quintiles of
`predicted risk identified ⬇49% of men and 60% of women
`who experienced a first CVD event on follow-up (sensitivity).
`Proportions of men and women without CVD events who
`were not in the top quintile of predicted risk were 85% and
`84%, respectively (specificity).
`The Framingham CHD risk functions (Wilson et al16)
`performed less well for predicting CVD risk: The c statistics
`were lower (0.756 [95% CI, 0.739, 0.773] in men; for
`difference compared with our new model, P⫽0.051; 0.778
`[95% CI, 0.756, 0.799] in women; for difference compared
`with our new model, P⫽0.003) and calibration was worse
`(␹2⫽32.37 in men and 12.42 in women) relative to that noted
`above for the new CVD risk prediction models. The sensi-
`tivity of the top quintile of predicted risk using the CHD risk
`functions was slightly lower (47% in men and 56% in
`women) although specificity was similar (85% in men and
`83% in women). The net reclassification improvement from
`using the new model was statistically significant for both men
`and women and reached 6.65% (P⬍0.001) and 7.95%
`(P⫽0.003), respectively.
`
`Performance of General CVD Risk Prediction
`Model for Predicting Individual CVD Components
`Tables 3 and 4 assess the performances of the sex-specific
`general CVD risk functions by comparing them with disease-
`
`C
`
`95% CI for C
`
`␹2
`
`P for ␹2
`
`Sensitivity of top quintile
`
`Specificity of top quintile
`
`Calibration factor
`
`So(10)
`
`IC (n⫽66)
`
`C
`
`0.847
`
`0.851
`
`(0.803–0.891)
`
`(0.804–0.897)
`
`9.32
`
`0.408
`
`76.49
`
`80.58
`
`0.1250
`
`8.82
`
`0.454
`
`83.73
`
`80.65
`
`0.9962
`
`0.829
`
`0.848
`
`95% CI for C
`
`(0.786–0.872)
`
`(0.810–0.887)
`
`␹2
`
`P for ␹2
`
`Sensitivity of top quintile
`
`Specificity of top quintile
`
`Calibration factor
`
`So(10)
`
`11.33
`
`0.254
`
`70.25
`
`80.77
`
`0.1862
`
`11.63
`
`0.235
`
`70.07
`
`80.76
`
`0.9918
`
`C indicates model discrimination (c statistic); Sensitivity of top quintile,
`percent events captured by the top quintile of predicted risk; Specificity of top
`quintile, percent nonevents captured by the bottom 4 quintiles of predicted risk;
`So(10), baseline survival rate at 10 years; and IC, intermittent claudication.
`
`specific algorithms for predicting risk of CHD, stroke, inter-
`mittent claudication, and heart failure. To apply the CVD
`functions for a specific component, the CVD-predicted prob-
`abilities were multiplied by the “calibration factor” given in
`Tables 3 and 4. For example,
`to compute the 10-year
`probability of CHD from the general CVD risk function in
`women, the CVD probability is calculated and then multi-
`plied by 0.61, the proportion of first CVD events in women
`that were CHD events.
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`5
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`

`

`D’Agostino et al
`
`General Cardiovascular Risk Profile
`
`747
`
`Table 4. Performance Summary: Modified CVD Model Versus
`Event-Specific Own Model for Men
`
`and smoking was more strongly associated with intermittent
`claudication (data not shown).
`
`Derivation of CVD Prediction Scores and Heart
`Age/Vascular Age
`Tables 5 and 6 and 7 and 8 provide score sheets that can be used
`for estimating the multivariable risk of CVD for women and
`men, respectively. Tables 9 and 10 give a different quantification
`of the same risk in the form of heart age/vascular age. We
`illustrate the use of these tables in the Appendix, and they are
`available at www.framinghamheartstudy.org/risk/index.html.
`
`CHD (n⫽425)
`
`C
`
`95% CI for C
`
`␹2
`
`P for ␹2
`
`Sensitivity of top quintile
`
`Specificity of top quintile
`
`Calibration factor
`
`So(10)
`
`Stroke (n⫽93)
`
`C
`
`95% CI for C
`
`␹2
`
`P for ␹2
`
`Sensitivity of top quintile
`
`Specificity of top quintile
`
`Calibration factor
`
`So(10)
`
`CVD Model
`
`Own Model
`
`0.733
`
`0.735
`
`(0.712–0.754)
`
`(0.714–0.756)
`
`18.20
`
`0.033
`
`45.94
`
`83.23
`
`0.7174
`
`18.36
`
`0.031
`
`45.70
`
`83.20
`
`0.9167
`
`0.826
`
`0.835
`
`(0.789–0.863)
`
`(0.797–0.874)
`
`26.11
`
`0.002
`
`71.64
`
`81.30
`
`0.1590
`
`9.21
`
`0.418
`
`76.05
`
`81.41
`
`0.9883
`
`Simpler CVD Risk Prediction Models Using
`Nonlaboratory Predictors
`The simple office-based CVD risk prediction function that
`incorporated body mass index (instead of total and HDL
`cholesterol) performed reasonably well (Table I of the online
`Data Supplement). The discrimination c statistics was 0.749
`(95% CI, 0.731, 0.767) for men and 0.785 (95% CI, 0.764,
`0.806) for women (for difference compared with our full
`model, P⬍0.001 and 0.013, respectively). Calibration ␹2
`statistics were 13.61 (for the lack of fit, P⫽0.14) for men and
`10.24 for women (for the lack of fit, P⫽0.33). The top
`sex-specific quintiles of predicted CVD risk identified ⬇48%
`of men and 58% of women who experienced a first CVD
`event on follow-up (sensitivity). Proportions of men and
`women without events who were not in the top quintile of risk
`were 85% and 83%, respectively (specificity). Tables IIA
`through IIC and IIIA through IIIC in the Data Supplement
`provide score sheets that can be used to estimate the multi-
`variable risk of CVD and heart age/vascular age for women
`and men, respectively, using the office-based nonlaboratory
`predictors.
`
`Discussion
`It is widely accepted that CVD constitutes a major public
`health problem in the United States35 and worldwide.36 The
`lifetime risk of CVD is substantial,37 and the condition is
`often silent or may strike without warning, underscoring the
`importance of prevention. Investigators have identified key
`risk factors that account for most CVD burden in the
`community, and numerous reports have demonstrated the
`clustering and conjoint influences of multiple risk factors in
`mediating disease vascular risk.2,4,6,7,38 – 41 Consequently, re-
`searchers have devised multivariable risk prediction tools that
`synthesize vascular risk factor information to yield estimates
`of absolute CVD risk (also referred to as global CVD risk) in
`individual patients.4,7,8,10 –12,42 The estimation of global CVD
`risk facilitates the matching of the intensity of risk factor
`lowering with the estimated probability of disease, thereby
`rendering treatment most cost-effective.38,42– 44 For instance,
`national cholesterol guidelines link treatment thresholds and
`goals to global coronary heart disease risk.9 In addition to
`reducing the number needed to treat to prevent a CVD event,
`multivariable risk assessment also avoids overlooking high-
`risk CVD candidates with multiple marginal risk factors and
`avoids needlessly alarming persons with only 1 isolated risk
`factor. Furthermore, analyses that fail to examine risk factors
`
`CHF (n⫽67)
`
`C
`
`0.841
`
`0.845
`
`95% CI for C
`
`(0.799–0.883)
`
`(0.802–0.888)
`
`15.30
`
`0.083
`
`82.59
`
`81.13
`
`0.9927
`
`␹2
`
`P for ␹2
`
`Sensitivity of top quintile
`
`Specificity of top quintile
`
`Calibration factor
`
`27.23
`
`0.001
`
`80.55
`
`81.09
`
`0.1148
`
`So(10)
`
`IC (n⫽105)
`
`C
`
`0.813
`
`0.820
`
`95% CI for C
`
`(0.780–0.847)
`
`(0.787–0.853)
`
`␹2
`
`P for ␹2
`
`Sensitivity of top quintile
`
`Specificity of top quintile
`
`Calibration factor
`
`So(10)
`
`Abbreviations as in Table 3.
`
`19.05
`
`0.025
`
`60.29
`
`81.15
`
`0.1804
`
`8.18
`
`0.516
`
`66.65
`
`81.34
`
`0.9852
`
`From the comparison of discrimination and ␹2 statistics, it
`is evident that the general CVD risk formulation provides
`discrimination of individual CVD outcomes that is as good as
`the individual disease-specific multivariable risk formula-
`tions and is well calibrated. Similarly, the sensitivity of the
`upper quintile of the CVD risk function is comparable to that
`of the top quintile of disease-specific functions in both sexes
`(Tables 3 and 4). In the analyses of individual components,
`the regression coefficients for cholesterol were higher for
`CHD and intermittent claudication (relative to that for stroke
`and congestive heart failure [CHF]; data not shown). Systolic
`blood pressure was more strongly associated with stroke and
`CHF (compared with CHD and intermittent claudication),
`
`Downloaded from
`
`http://circ.ahajournals.org/
`
` at California Institute of Technology on October 6, 2014
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`6
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`

`748
`
`Circulation
`
`February 12, 2008
`
`Table 5. CVD Points for Women
`
`Points
`
`Age, y
`
`HDL
`
`Total Cholesterol
`
`SBP Not Treated
`
`SBP Treated
`
`Smoker
`
`Diabetic
`
`60⫹
`
`50–59
`
`45–49
`
`35–44
`
`⬍35
`
`⬍160
`
`160–199
`
`200–239
`
`240–279
`
`280⫹
`
`⬍120
`
`120–129
`
`130–139
`
`140–149
`
`150–159
`
`160⫹
`
`No
`
`No
`
`Yes
`
`Yes
`
`⬍120
`
`120–129
`
`130–139
`
`140–149
`
`150–159
`
`160⫹
`
`⫺3
`
`⫺2
`
`⫺1
`
`0
`
`1
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7
`
`8
`
`9
`
`10
`
`11
`
`30–34
`
`35–39
`
`40–44
`
`45–49
`
`50–54
`
`55–59
`
`60–64
`
`65–69
`
`70–74
`
`12
`
`75⫹
`
`Points allotted
`
`SBP indicates systolic blood pressure.
`
`in combinations usually greatly overestimate the population-
`attributable risks associated with individual risk factors.45
`Researchers also have developed disease-specific formula-
`tions to predict risk of developing specific CVD events such
`
`Table 6. CVD Risk for Women
`
`Points
`
`ⱕ⫺2
`
`⫺1
`
`0
`
`1
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7
`
`8
`
`9
`
`10
`
`11
`
`12
`
`13
`
`14
`
`15
`
`16
`
`17
`
`18
`
`19
`
`20
`
`Risk, %
`
`⬍1
`
`1.0
`
`1.2
`
`1.5
`
`1.7
`
`2.0
`
`2.4
`
`2.8
`
`3.3
`
`3.9
`
`4.5
`
`5.3
`
`6.3
`
`7.3
`
`8.6
`
`10.0
`
`11.7
`
`13.7
`
`15.9
`
`18.5
`
`21.5
`
`24.8
`
`28.5
`
`21⫹
`
`⬎30
`
`Total
`
`as CHD events or stroke.13–16,18 –20 The present investigation
`is based on the premise that although the impacts of risk
`factors vary from 1 specific CVD type to another, there is
`sufficient commonality of risk factors to warrant generating a
`single general CVD risk prediction instrument that could
`accurately predict global CVD risk and the risk of individual
`components. Our study was motivated by our presumption of
`a need to simplify risk prediction in office-based practices by
`replacing disease-specific algorithms with a single general
`CVD prediction tool.
`Framingham investigators formulated a general CVD risk
`function several years ago.46 Using a multivariable-logistic
`regression model, we reported that an algorithm that identi-
`fied persons at high risk of atherosclerotic CVD in general
`also was effective for identifying persons at risk for each of
`the specific events, including CHD, stroke, intermittent clau-
`dication, and heart failure. However, that risk formulation
`was developed in 1976; was based on a limited number of
`events; did not include HDL cholesterol, a powerful influence
`on lipid atherogenesis; and did not focus on estimates of
`absolute risk. A subsequent CVD risk function used a
`parametric model, but that investigation did not evaluate the
`ability of a general CVD risk profile to predict individual
`outcomes.3
`investigation extends and expands on the
`The present
`previous general CVD risk formulation on the basis of a
`larger number of events, incorporates HDL cholesterol, and
`estimates absolute CVD risk. We propose a general CVD risk
`function that demonstrates very good discrimination and
`calibration both for predicting CVD and for predicting risk of
`individual CVD components (comparable to disease-specific
`algorithms). The parallelism between atherosclerosis in dif-
`ferent vascular territories in terms of sharing a common set of
`risk factors explains why the general CVD risk function
`performs well for predicting the individual components. The
`
`Downloaded from
`
`
`
` at California Institute of Technology on October 6, 2014http://circ.ahajournals.org/
`
`7
`
`

`

`D’Agostino et al
`
`General Cardiovascular Risk Profile
`
`749
`
`HDL
`
`60⫹
`
`50–59
`
`45–49
`
`35–44
`
`⬍35
`
`Total Cholesterol
`
`SBP Not Treated
`
`SBP Treated
`
`Smoker
`
`Diabetic
`
`⬍120
`
`120–129
`
`130–139
`
`140–159
`
`160⫹
`
`⬍160
`
`160–199
`
`200–239
`
`240–279
`
`280⫹
`
`⬍120
`
`No
`
`No
`
`120–129
`
`130–139
`
`140–159
`
`160⫹
`
`Yes
`
`Yes
`
`Table 7. CVD Points for Men
`
`Age, y
`
`30–34
`
`35–39
`
`40–44
`
`45–49
`
`50–54
`
`55–59
`
`60–64
`
`65–69
`
`70–74
`
`75⫹
`
`Points
`
`⫺2
`
`⫺1
`
`0
`
`1
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7 8
`
`9
`
`10
`
`11
`
`12
`
`13
`
`14
`
`15
`
`Points allotted
`
`Total
`
`C statistic for the general CVD risk prediction models ranged
`from 0.76 to 0.79