From the Framingham Heart Study, National Heart, Lung, and Blood
Institute, Framingham, Mass (P.W.F.W., D.L.); Boston University Mathematics
Department, Boston, Mass (R.B.D., A.M.B., H.S.); and Framingham Heart Study,
Boston University School of Medicine, Framingham, Mass (W.B.K.).
Methods and Results—This work was designed as a prospective,
single-center study in the setting of a community-based cohort. The
patients were 2489 men and 2856 women 30 to 74 years old at baseline
with 12 years of follow-up. During the 12 years of follow-up, a total
of 383 men and 227 women developed CHD, which was significantly
associated with categories of blood pressure, total
cholesterol, LDL cholesterol, and HDL
cholesterol (all P<.001). Sex-specific
prediction equations were formulated to predict CHD risk according to
age, diabetes, smoking, JNC-V blood pressure categories, and NCEP total
cholesterol and LDL cholesterol categories. The
accuracy of this categorical approach was found to be comparable to CHD
prediction when the continuous variables themselves were used.
After adjustment for other factors,
Conclusions—Recommended guidelines of blood pressure, total
cholesterol, and LDL cholesterol effectively
predict CHD risk in a middle-aged white population sample. A simple
coronary disease prediction algorithm was developed using
categorical variables, which allows physicians to predict
multivariate CHD risk in patients without overt CHD.
The present article develops a simplified coronary
prediction model, building on the blood pressure,
cholesterol, and LDL-C categories proposed by the JNC-V and
NCEP ATP II.7 9 10 The analysis evaluates
the utility and accuracy of blood pressure, cholesterol,
and LDL-C recommended categories in multivariable CHD prediction,
using a Framingham Heart Study sample that pooled information for the
original and offspring cohorts and followed them for 12 years. This
approach emphasizes the established, powerful, independent, and
biologically important factors. Family history for heart disease,
physical activity, and obesity are not included because these factors
work to a large extent through the major risk factors, and their unique
contribution to CHD prediction can be difficult to quantify. The
prediction of initial CHD events in a free-living population not on
medication is emphasized. Consequently, ERT for postmenopausal women,
treatment of high blood pressure, and therapy for high blood
cholesterol are not included in the formulations.
At the 1971–1974 examination, a medical history was taken and a
physical examination was performed by a physician. Persons who smoked
regularly during the previous 12 months were classified as smokers.
Height and weight were measured, and body mass index
(kg/m2) was calculated. Two blood pressure
determinations were made after the participant had been sitting at
least 5 minutes, and the average was used for analyses.
Hypertension was categorized according to blood pressure readings by
JNC-V definitions10 : optimal (systolic
<120 mm Hg and diastolic <80 mm Hg), normal
blood pressure (systolic 120 to 129 mm Hg or
diastolic 80 to 84 mm Hg), high normal blood pressure
(systolic 130 to 139 mm Hg or diastolic 85 to
89 mm Hg), hypertension stage I (systolic 140 to 159
mm Hg or diastolic 90 to 99 mm Hg), and hypertension
stage II–IV (systolic
Diabetes was considered present if the participant was under
treatment with insulin or oral hypoglycemic agents, if casual blood
glucose determinations exceeded 150 mg/dL at two clinic visits in the
original cohort, or if fasting blood glucose exceeded 140 mg/dL at the
initial examination of the Offspring Study participants. Blood was
drawn at the baseline examination after an overnight fast, and EDTA
plasma was used for all cholesterol and
triglyceride measurements. Cholesterol was
determined according to the Abell-Kendall
technique,13 and HDL-C was measured after
precipitation of VLDL and LDL proteins with heparin-magnesium according
to the Lipid Research Clinics Program protocol.14
When triglycerides were <400 mg/dL, the concentration of
LDL-C was estimated indirectly by use of the Friedewald
formula15 ; for triglycerides
Cutoffs for TC (<200, 200 to 239, 240 to 279, and
Statistical tests included age-adjusted linear regression or logistic
regression to test for trends across blood pressure, TC, LDL-C, and
HDL-C categories.18 Age-adjusted Cox proportional
hazards regression and its accompanying c statistic were used to test
for the relation between various independent variables and the CHD
outcome and to evaluate the discriminatory ability of various
prediction models.19 20 The 12-year follow-up was
used in the proportional hazards models, and results were adapted to
provide 10-year CHD incidence estimates. Separate score sheets were
developed for each sex using TC and LDL-C categories. These sheets
adapted the results of proportional hazards regressions by use of a
system that assigned points for each risk factor based on the value for
the corresponding ß-coefficient of the regression
analyses.
The relative risk, but not the attributable risk, for TC and CHD
declines with advancing age.21 Quadratic terms
for age were considered in the models for the score sheets.
Furthermore, CHD risk is associated with HDL-C in the
elderly,22 23 24 and interaction terms for TC and
age were also considered in the development of the prediction
models.22 Among women, an age-squared term was
found to be significant in the prediction models and was incorporated
into the score sheets. Neither agexTC nor agexLDL-C was found to be
significant in either sex.
Score sheets for prediction of CHD using TC and LDL-C categorical
variables were developed from the ß-coefficients of Cox
proportional hazards models. The TC range was expanded in 40-mg/dL
increments to include
The age-adjusted means for various risk factors according to
blood pressure categories are shown for men and women in Table 2
Age-adjusted 10-year CHD rates for blood pressure and
cholesterol categories are shown for men and women in Table 3
Multivariable risk calculations for TC categories are shown
in Table 4
Multivariable risk calculations for LDL-C categories are shown in
Table 5
The efficacy of prediction with continuous variables was
compared with that obtained with categorical variables and a risk
factor sum (Figs 1
Score sheets were developed to predict CHD in men (Fig 3
An illustrative example for Fig 3
The present study builds on the prior experience of CHD prediction
with continuous variables and integrates the categorical approaches
that have become part of the framework of blood pressure (JNC-V) and
cholesterol (NCEP) programs in the United
States.6 7 10 As suggested in an earlier NCEP
report,27 our approach integrates blood pressure
and cholesterol information and estimates both relative and
absolute CHD risk with a risk factor weighting approach.
The NCEP ATP II guidelines defined hypertension as a yes/no
variable, and it can be seen from Tables 3
The predictive capability of the continuous model described here is
similar to the accelerated failure model used in an earlier Framingham
CHD prediction equation,11 and the continuous
variable and categorical variable approaches have c-statistic
values that are nearly identical, suggesting that predictability of the
models is nearly the same in either instance. This result is in
contradistinction to a comparison of the NCEP ATP II algorithm (<10
unique patterns) with a continuous variable approach in which the
latter (using Framingham models) was thought to be statistically
superior.29 A risk factor sum model, considering
7 dichotomous variables, was used for comparison in the present
study and showed a significant falloff in the level of the c statistic
with this approach compared with formulations using categorical or
continuous levels.
TC- and LDL-C–based approaches, whether continuous or categorical
variables are used, are similar in their ability to predict initial
CHD events in the models presented. This may result from
indirect estimation of LDL-C, leading to reduced accuracy and precision
of LDL-C estimates from single blood
measurements.31 32 The CHD estimates in the
present article represent the experience of a free-living
population sample, and different results may be obtained when blood
pressure or blood cholesterol has been treated
aggressively.
Although the impact of TC and LDL-C on estimates of CHD risk is
similar in Framingham data, such results may be more relevant to
populations than to individuals. Extensive clinical data and clinical
trial results suggest that LDL-C is the major atherogenic lipoprotein
and that measurement of LDL-C levels in the clinical setting provides
an advantage.33 34 35 High or low levels of HDL-C
within individuals can produce discrepancies between TC and LDL-C
levels. In addition, TC and LDL-C levels are not always concordant in
persons with hypertriglyceridemia. Thus,
measurement of TC is only a crude surrogate for LDL-C in risk
assessment or in estimating initial response to therapy, although it
can be useful in initial detection or long-term monitoring of
response.31
Several candidate variables were not used in the prediction
equations. A family history of premature CHD, previously shown in the
Framingham Study to increase the relative odds of CHD to
Postmenopausal ERT was not used in the prediction algorithm, because
estrogen dose was typically higher in the early
1970s41 and the cardioprotective effects of
hormonal replacement therapy that have been universally observed in
more recent times42 43 44 45 were not experienced by
all Framingham women from the early 1970s to the mid
1980s.46 47 48
Persons who exercise typically have a lower risk of
CHD.49 50 51 Information on physical activity was
not available at the baseline examinations used to develop this CHD
risk prediction algorithm, but cigarette smoking, low HDL-C levels, and
diabetes are less common among those who are physically
active.52 53 54 55 Regular and vigorous exercise is
often associated with higher levels of HDL-C, an important determinant
for reduced CHD risk.56 57 58 Similarly, body mass
index, an obesity index that expresses weight in kilograms divided by
height in meters squared, has been considered a candidate variable
for the CHD prediction algorithm. Greater obesity has been associated
with higher TC, lower HDL-C, higher blood pressure, and diabetes, and
the residual impact of obesity on CHD has typically been slight after
incorporation of these other variables into the regression
model.8
Clinicians should exercise caution in generalizing from experience of
the Framingham Study, a community sample of white subjects drawn from a
suburb west of Boston. Use of the prediction models would be most
appropriate for individuals who resemble the study sample. However,
reasonable accuracy in predicting CHD has been demonstrated in the
past, when earlier Framingham CHD prediction equations were applied to
population samples from Honolulu, Puerto Rico, Albany, Chicago, Los
Angeles, Minneapolis, Tecumseh, the Western Collaborative Group, and a
national cohort.59 60 61 62 Follow-up from the
Framingham Study was also used to estimate CHD experience in men
participating in the Multiple Risk Factor Intervention
Trial.63
Coronary prediction estimates tend to be most reliable when the
data are most concentrated and can be particularly useful when subjects
have multiple mild abnormalities that act synergistically to increase
CHD risk. It is uncommon for persons to have four or five risk factors,
and estimates of CHD risk tend to be more precise for individuals with
fewer risk factors. Score sheet approaches have been used to target
persons for the primary prevention of coronary disease by use
of a tabular format called a Sheffield table, in which the estimated
absolute risk for CHD is used to establish a threshold for aggressive
intervention.64 The average CHD rates reported in
those tables are roughly comparable to the myocardial infarction and
coronary death rates among middle-aged men who participated in
the West of Scotland trial of cholesterol
lowering.35 65 In contrast, our prediction
equations estimate coronary disease risk over a period of 10
years for a larger age range and include total CHD (angina pectoris,
myocardial infarction, and coronary death).
A study that considered CHD prediction using TC, LDL-C, TC/HDL-C ratio,
and LDL-C/HDL-C ratio66 concluded that "total
cholesterol/HDL is a superior measure of risk for CHD
compared with either total cholesterol or LDL
cholesterol, and that current practice guidelines could be
more efficient if risk stratification was based on this ratio rather
than primarily on the LDL cholesterol level." Such an
approach appears attractive, but at the extremes of the TC or LDL-C
distribution, equal ratios may not signify the same CHD risk. Moreover,
use of a ratio may make it harder for the physician to focus on the
separate values for TC, LDL-C, and HDL-C that have to be borne in mind
to make appropriate clinical decisions concerning therapy. The current
approach builds on established blood pressure (JNC-V) and
cholesterol (NCEP ATP II) foundations, requires fasting
samples only if LDL-C score sheets are used, and is easy to implement
as part of a screening program.
Estimation of CHD and other cardiovascular events
is a dynamic field. The present formulation has attempted to
provide a simplified approach to predict risk for initial CHD events in
outpatients free of disease, drawing on national programs for treatment
of elevated blood pressure and TC, without a loss in accuracy. Other
factors, such as fibrinogen, lipoprotein(a), ERT, family history of
premature CHD, and hypertensive therapy have been or will be evaluated
as baseline data and greater follow-up experience become available.
(Equation 1): L_Cholmen=0.04826xage-0.65945 (if
cholesterol <160) +0.0 (if cholesterol 160 to
199) +0.17692 (if cholesterol 200 to 239) +0.50539 (if
cholesterol 240 to 279) +0.65713 (if
cholesterol
The function is evaluated at the values of the means for each
variable. Call it G, where (Equation 1):
G_Cholmen
=0.04826x48.5926-0.65945x0.07433+0.17692x0.38851+0.50539x0.16673+0.65713x0.05826+0.49744x0.19285+0.24310x0.35476-0.05107x0.19646-0.48660x0.10727-0.00226x0.20048+0.28320x0.20048+0.52168x0.22820+0.61859x0.13057+0.42839x0.05223+0.52337x0.40458=3.0975. Similarly, for women,
G_Chol=9.92545. For the LDL score sheets, G_LDL for men is 3.00069 and
for women 9.914136.
This value of G is subtracted from function L to produce function A
(Equation 2), which is then exponentiated, to produce B (Equation 3).
The latter represents the relative odds for CHD. The survival
value s(t) is exponentiated by B and subtracted from 1.0 to calculate
the 10-year probability of CHD (Equation 4).
(Equation 2): A=L-G (where G_Chol=3.0975 for men, 9.92545 for
women; similarly for Table 7
(Equation 3): B=eA.
(Equation 4): P=1-[s(t)]B
[where s(t)_Chol 10 years=0.90015 for men, 0.96246 for women;
similarly for Table 7
Consider a 55-year-old man with cholesterol of 250
mg/dL, HDL-C of 39 mg/dL, blood pressure (146/88 mm Hg) that
falls into stage I hypertension, and no diabetes, who is a smoker. In
this instance, after Equation 1,
L=55x0.04826+0.50539+0.24310+0.52168+0.52337 =4.4478. After Equation
2, A=4.4478-3.0975=1.3503, and after Equation 3,
B=e1.3503=3.85874. Finally, after Equation
4, P=1-0.900153.85874=1-0.66637=0.3336, for a 33% chance of developing
CHD over 10 years. According to the point score sheet, 55 years old (4
points)+cholesterol of 250 mg/dL (2 points)+HDL-C of 39
mg/dL (1 point)+stage I blood pressure (2 points)+smoker (2 points)=11
points, corresponding to a 31% chance of developing CHD over 10 years.
An average 55-year-old man has a 16% risk, and an ideal man has a 7%
risk. Similar calculations can be done for women and for the LDL-C
prediction models and score sheets.
Score sheets are on the internet at http://www.nhlbi.nih.gov/nhlbi/fram/
© 1998 American Heart Association, Inc.
Special Reports
Prediction of Coronary Heart Disease Using Risk Factor Categories
Abstract
Top
Abstract
Introduction
Methods
Results
Discussion
Appendix 1
References
Background—The objective of this
study was to examine the association of Joint National Committee
(JNC-V) blood pressure and National Cholesterol Education
Program (NCEP) cholesterol categories with coronary
heart disease (CHD) risk, to incorporate them into coronary
prediction algorithms, and to compare the discrimination properties of
this approach with other noncategorical prediction functions. 
28% of CHD events in men and
29% in women were attributable to blood pressure levels that exceeded
high normal (
130/85). The corresponding multivariable-adjusted
attributable risk percent associated with elevated total
cholesterol (200 mg/dL) was 27% in men and 34% in
women.
Key Words: coronary disease • prediction • hypertension • cholesterol
Introduction
Top
Abstract
Introduction
Methods
Results
Discussion
Appendix 1
References
Coronary heart
disease continues to be a leading cause of morbidity and mortality
among adults in Europe and North America.1 Risk
factors have included blood pressure, cigarette smoking, cholesterol
(TC), LDL-C, HDL-C, and diabetes.2 3 4 Factors
such as obesity, left ventricular hypertrophy,
family history of premature CHD, and ERT have also been considered in
defining CHD risk.5 6 7 Data from population
studies enabled prediction of CHD during a follow-up interval of
several years, based on blood pressure, smoking history, TC and HDL-C
levels, diabetes, and left ventricular
hypertrophy on the ECG. These prediction algorithms have
been adapted to simplified score sheets that allow physicians to
estimate multivariable CHD risk in middle-aged
patients.8
Methods
Top
Abstract
Introduction
Methods
Results
Discussion
Appendix 1
References
The population-based sample used for this report included 2489
men and 2856 women 30 to 74 years old at the time of their Framingham
Heart Study examination in 1971 to 1974. Participants attended either
the 11th examination of the original Framingham
cohort11 or the initial examination of the
Framingham Offspring Study.12 Similar research
protocols were used in each study, and persons with overt CHD at the
baseline examination were excluded. 160 or diastolic
100 mm Hg). When systolic and diastolic
pressures fell into different categories, the higher category was
selected for the purposes of classification. Blood pressure
categorization was made without regard to the use of antihypertensive
medication. 400
mg/dL, the LDL-C was estimated directly after
ultracentrifugation of plasma and measurement of
cholesterol in the bottom fraction (plasma density
<1.006).16 280 mg/dL), LDL-C
(<130, 130 to 159, and 160 mg/dL), HDL-C (<35, 35 to 59, and 60
mg/dL), cigarette smoking, diabetes, and age were considered in this
report. The cholesterol and LDL-C cutoffs are similar to
those used for the NCEP ATP II guidelines and were partly dictated by
the number of persons with higher levels of TC or LDL-C. For those
reasons, we have provided information for cholesterol
categories of 240 to 279 and 280 mg/dL and for LDL-C 160 mg/dL. Too
few persons had LDL-C 190 mg/dL to provide stable estimates for CHD
risk. Study subjects were followed up over a 12-year period for the
development of CHD (angina pectoris, recognized and unrecognized
myocardial infarction, coronary insufficiency, and
coronary heart disease death) according to previously published
criteria. "Hard CHD" events included total CHD without angina
pectoris.17 Surveillance for CHD consisted of
regular examinations at the Framingham Heart Study clinic and review of
medical records from outside physician office visits and
hospitalizations. 160 mg/dL and 280 mg/dL, the HDL-C range 35
to 59 mg/dL was partitioned to provide three levels for each sex, and
both optimal and normal blood pressure categories were included. The
score sheets provide comparison 10-year absolute risks for persons of
the same age and sex for average total CHD, average hard CHD (total CHD
without angina pectoris), and low-risk total CHD. Risk factors are
shaded, ranging from very low relative risk to very high. Such
distinctions are arbitrary but provide a foundation to determine the
need for clinical intervention.
Results
Top
Abstract
Introduction
Methods
Results
Discussion
Appendix 1
References
At initial examination, study subjects ranged in age from 30
to 74 years, and the mean age±SD was 48.6±11.7 years for 2489 men and
49.8±12.0 years for 2856 women. Because there were relatively few
persons at the higher stages of hypertension in the Framingham sample,
stages II, III, and IV hypertension were combined into a single
category in the analyses (Table 1
). Approximately half of the subjects
for each sex had blood pressure levels in the normal or optimal
range.
View this table:
[in a new window]
Table 1. Characteristics of Participants According to JNC-V
Hypertension Categories1 . Therapy for hypertension
(P<.001 men, P<.001 women), more frequent
diabetes (P<.001 men, P<.001 women), greater
body mass index (P<.001 men, P<.001 women), and
higher TC level (P=.004 men, P<.001 women) were
consistently associated with higher blood pressure
categories in both sexes. Cigarette smoking was inversely associated
with blood pressure in men (P=.010), but only a borderline
association was present in women (P=.071). The
lipoprotein fractions HDL-C (P<.001) and LDL-C
(P=.031) were significantly associated with blood pressure
category in women but not in men.
View this table:
[in a new window]
Table 2. Age-Adjusted Mean Levels and Prevalence of Risk
Factors According to Blood Pressure Category . In prediction models, the CHD rates
were significantly associated with the specified categories of blood
pressure, TC, HDL-C, and LDL-C (all P<.001 for both sexes).
The number of CHD events arising at each blood pressure and
cholesterol category is also given. For blood pressure, the
greatest number of CHD cases arose from the stage I hypertension
category for both sexes. Conversely, the greatest number of CHD cases
arose from the highest lipoprotein cholesterol levels
(LDL-C 160 mg/dL or cholesterol 240 mg/dL).
View this table:
[in a new window]
Table 3. CHD Risk According to Blood Pressure and Lipid
Categories . Normal or optimal blood
pressure was used as the reference level, and estimated relative risk
rose from 1.00 for normal or optimal blood pressure to 1.84 in men and
2.12 in women with stage II–IV hypertension. Similarly, for TC, the
estimated relative risk rose from 1.00 for levels <200 mg/dL to 1.90
in men and 1.72 in women with TC 240 mg/dL. When typical HDL-C levels
(35 to 59 mg/dL) were used as a reference, CHD risk was increased among
men and women with low HDL-C (<35 mg/dL) and CHD risk was
correspondingly decreased among subjects with high HDL-C (60 mg/dL).
The population-attributable risk percent associated with hypertension
was 6% for high normal, 13% for stage I, and 9% for stage II–IV
hypertension among men. The corresponding values were 5% for high
normal, 13% for stage I, and 12% for stage II–IV hypertension among
women. An overall estimate of the attributable risk percent for blood
pressure level greater than normal was 28% in men and 29% in women.
When cholesterol <200 mg/dL was used as the reference
range, attributable risks were 10% for TC 200 to 239 mg/dL and 17%
for TC 240 mg/dL in men and 12% for TC 200 to 239 mg/dL and 22% for
TC 240 mg/dL in women. The overall estimate of the attributable risk
percent for TC level 200 mg/dL was 27% in men and 34% in women.
View this table:
[in a new window]
Table 4. Multivariable-Adjusted Relative Risks for CHD
According to TC Categories , and these results parallel the
presentation in Table 4
. When LDL-C <130 mg/dL is used as
the reference range, a greater absolute CHD risk is associated with
higher LDL-C categories, but the magnitude of the relative risk and its
statistical significance are very similar to that observed for the
categories of TC (Table 4).
View this table:
[in a new window]
Table 5. Multivariate-Adjusted Relative Risks
for CHD According to LDL-C Categories and 2 for men and women, respectively). For
calculation of the risk factor sum, the levels considered were age
(45 years for men, 55 years for women), hypertension
(systolic blood pressure 140 mm Hg,
diastolic blood pressure 90 mm Hg, or use of
antihypertensive medication), smoking, diabetes, elevated
cholesterol (cholesterol 240 mg/dL or LDL-C
160 mg/dL), and HDL-C <35 mg/dL. One point was given for each risk
factor, for a possible score of 0 to 7 points. A greater area under the
curve indicated better predictive capability. The curves were nearly
identical for the continuous and categorical formulations, TC and LDL-C
categories had similar effects, and the risk factor sums tended to have
the lowest predictive potential. The c statistic, a measure of the
discriminatory ability of a model, equal to the area under the receiver
operating characteristic curve, provides a guide to interpret the
results plotted in Figs 1 and 2. The c statistics associated with TC
categories were 0.74 in men and 0.77 in women for continuous
variables by proportional hazards or accelerated failure
models,11 0.73 in men and 0.76 in women for
categorical variables, and 0.69 in men and 0.72 in women for the
risk factor sum. The corresponding c statistics associated with LDL-C
categories were 0.74 in men and 0.77 in women for continuous
variables by proportional hazards or accelerated failure
models,11 0.73 in men and 0.77 in women for
categorical variables, and 0.68 in men and 0.71 in women for the
risk factor sum.
View larger version (16K):
[in a new window]
Figure 1. Receiver operating characteristic curves for
prediction of CHD in Framingham men over a period of 12 years. Separate
plots were used for continuous, categorical, and risk factor sum
models, according to whether TC or calculated LDL-C was used.
View larger version (16K):
[in a new window]
Figure 2. Receiver operating characteristic curves for
prediction of CHD in Framingham women over a period of 12 years.
Separate plots were used for continuous, categorical, and risk factor
sum models, according to whether TC or calculated LDL-C were
used. ) and women (Fig 4) from the ß-coefficients of Cox
proportional hazards models (Table 6). Among women, an age-squared term
was found to be significant and was incorporated into the score sheets.
The average CHD risk over a period of 10 years tends to plateau
slightly in the oldest men and women.
View larger version (71K):
[in a new window]
Figure 3. CHD score sheet for men using TC or LDL-C
categories. Uses age, TC (or LDL-C), HDL-C, blood pressure, diabetes,
and smoking. Estimates risk for CHD over a period of 10 years based on
Framingham experience in men 30 to 74 years old at baseline. Average
risk estimates are based on typical Framingham subjects, and estimates
of idealized risk are based on optimal blood pressure, TC 160 to 199
mg/dL (or LDL 100 to 129 mg/dL), HDL-C of 45 mg/dL in men, no diabetes,
and no smoking. Use of the LDL-C categories is appropriate when fasting
LDL-C measurements are available. Pts indicates points.
View larger version (71K):
[in a new window]
Figure 4. CHD score sheet for women using TC or LDL-C
categories. Uses age, TC, HDL-C, blood pressure, diabetes, and smoking.
Estimates risk for CHD over a period of 10 years based on Framingham
experience in women 30 to 74 years old at baseline. Average risk
estimates are based on typical Framingham subjects, and estimates of
idealized risk are based on optimal blood pressure, TC 160 to 199 mg/dL
(or LDL 100 to 129 mg/dL), HDL-C of 55 mg/dL in women, no diabetes, and
no smoking. Use of the LDL-C categories is appropriate when fasting
LDL-C measurements are available. Pts indicates points.
View this table:
[in a new window]
Table 6. ß-Coefficients Underlying CHD Prediction Sheets
Using TC Categories follows. The subject is a 55-year-old
man with a TC of 250 mg/dL, HDL-C of 39 mg/dL, and blood pressure of
146/88 who is diabetic and a nonsmoker. Proceeding through the steps
gives us the following results: Step 1: Age 55=4 points. Step 2: TC 250
mg/dL=2 points. Step 3: HDL-C 39 mg/dL=1 point. Step 4: Blood pressure
146/88 mm Hg=2 points. Step 5: Diabetic=2 points. Step 6:
Nonsmoker=0 points. Step 7: Point total was 4+2+1+2+2+0=11. Step 8:
Estimated 10-year CHD risk is 31%. Step 9: The average and
"low-risk" risks of CHD over a period of 10 years for a 55-year-old
man are 16% and 7%, respectively (low risk was calculated for a
person the same age, optimal blood pressure, TC 160 to 199 mg/dL, HDL-C
45 mg/dL for men or 55 mg/dL for women, nonsmoker, and no diabetes).
Dividing the subject's risk by the average risk provides an estimate
of the relative risk: 31% divided by 16%=1.94. Use of the LDL-C
approach in the score sheets is appropriate when fasting LDL-C
estimates are available, by use of ultracentrifugation
techniques, the Friedewald formula, or newer LDL-C
assays.15 25 26 The approach is analogous to that
shown for TC categories.
Discussion
Top
Abstract
Introduction
Methods
Results
Discussion
Appendix 1
References
For the past two decades it has been possible to estimate CHD risk
by use of regression equations derived from observational studies, and
the present study demonstrates similar results, predicting later
CHD in a middle-aged white population sample. Prediction models have
typically been based on the logistic function, although the Weibull
distribution has also been used.11 22
Formulations have often included age, sex, blood pressure, TC, HDL-C,
smoking, diabetes, and left ventricular
hypertrophy.11 The prediction of CHD
has taken the form of sex-specific equations that were developed from a
single study and applied to other populations or individuals. Age, TC,
HDL-C, and blood pressure were used in the equations as continuous
variables, in contrast to dichotomous variables (yes/no) such
as smoking, diabetes, and left ventricular
hypertrophy. , 4, and 5 that
additional blood pressure categories are important in predicting CHD
risk. Higher levels of blood pressure are typically associated with
abnormal cholesterol levels, greater body mass index, and
an increased prevalence of diabetes (Table 2). Data from Tables 3 and 4
demonstrate that blood pressure, TC, LDL-C, and HDL-C categories are
predictive of CHD and suggest that risk factor prevention and
intervention programs should be integrated, as recently
suggested.28 29 30 Three reasons probably account
for similar results when continuous or categorical formulations are
used: (1) a large enough number of categories has been used to
adequately describe the clinical data; (2) coronary prediction
equations have limitations in their precision and accuracy; and (3) in
the final steps of the prediction score sheet, the data are summarized,
by use of point score totals, providing fewer than 20 combinations for
CHD risk prediction. 1.3,36 was not uniformly available among the
second-generation participants. Fibrinogen is now recognized as a CHD
risk factor,37 and levels were available for
1000 original cohort participants at a 1968–70
examination,38 39 but fibrinogen measurements
were not available for the Offspring Study participants. In addition,
established methods for measuring fibrinogen are lacking, and the
precise mechanism linking elevated fibrinogen levels to CHD is unclear.
Other risk factors, such as smoking, diabetes, and hypertension, are
often associated with abnormal fibrinogen levels, and fibrinogen
measurements vary greatly within
individuals.37 40 Left ventricular
hypertrophy on the ECG was used in previous CHD prediction
algorithms, but it is highly associated with hypertension and was not
included in the present formulation for a variety of reasons,
including lack of standard universally accepted ECG
criteria.11
Selected Abbreviations and Acronyms
CHD
=
coronary heart disease
ERT
=
estrogen replacement therapy
HDL-C
=
HDL cholesterol
JNC-V
=
Fifth Joint National Committee on Hypertension
LDL-C
=
LDL cholesterol
NCEP ATP II
=
National Cholesterol Education Program, Adult Treatment
Panel II
TC
=
total cholesterol
VLDL-C
=
VLDL cholesterol
View this table:
[in a new window]
Table 7. ß-Coefficients Underlying CHD Prediction Sheets
Using LDL-C Categories
Appendix 1
Top
Abstract
Introduction
Methods
Results
Discussion
Appendix 1
References
Application of Tables 6 and 7
The ß-coefficients given in Table 6 are used to compute a linear function.
The latter is corrected for the averages of the participants' risk
factors, and the subsequent result is exponentiated and used to
calculate a 10-year probability of CHD after insertion into a survival
function. The following explanation and an example treat each of these
steps in a serial fashion, using Table 6 for the illustration
below. 280) +0.49744 (if HDL-C<35) +0.24310 (if
HDL-C 35 to 44) +0.0 (if HDL-C 45 to 49) -0.05107 (if HDL-C 50 to 59)
-0.48660 (if HDL-C 60) -0.00226 (if blood pressure [BP] optimal)
+0.0 (if BP normal) +0.28320 (if BP high normal) +0.52168 (if BP stage
I hypertension) +0.61859 (if BP stage II hypertension) +0.42839 (if
diabetes present) +0.0 (if diabetes not present) +0.52337 (if
smoker) +0.0 (if not smoker). ,
G_LDL=3.00069 for men, 9.914136 for women). , s(t)_LDL 10 years=0.90017 for men, 0.9628 for
women].
Acknowledgments
This work is from the National Heart, Lung, and Blood
Institute's Framingham Heart Study, supported by NIH/NHLBI contract
N01-HC-38038. The authors would like to acknowledge the careful review
and helpful criticism by Dr James Cleeman, Coordinator of the National
Cholesterol Education Program at the National Heart, Lung,
and Blood Institute.
Footnotes
Reprint requests to Dr Peter W.F. Wilson, Framingham Heart Study, National Heart, Lung, and Blood Institute, 5 Thurber St, Framingham, MA 01701.
References
Top
Abstract
Introduction
Methods
Results
Discussion
Appendix 1
References
This article has been cited by other articles:
 |
|
 |
|
  K. McGeechan, P. Macaskill, L. Irwig, G. Liew, and T. Y. Wong Assessing New Biomarkers and Predictive Models for Use in Clinical Practice: A Clinician's Guide Arch Intern Med, November 24, 2008; 168(21): 2304 - 2310. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. Schillaci, G. V.L. De Socio, G. Pucci, M. R. Mannarino, J. Helou, M. Pirro, and E. Mannarino Aortic Stiffness in Untreated Adult Patients With Human Immunodeficiency Virus Infection Hypertension, August 1, 2008; 52(2): 308 - 313. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Qasim, N. N. Mehta, M. G. Tadesse, M. L. Wolfe, T. Rhodes, C. Girman, and M. P. Reilly Adipokines, insulin resistance, and coronary artery calcification. J. Am. Coll. Cardiol., July 15, 2008; 52(3): 231 - 236. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Ankle Brachial Index Collaboration Ankle Brachial Index Combined With Framingham Risk Score to Predict Cardiovascular Events and Mortality: A Meta-analysis JAMA, July 9, 2008; 300(2): 197 - 208. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. K. Grinspoon, C. Grunfeld, D. P. Kotler, J. S. Currier, J. D. Lundgren, M. P. Dube, S. E. Lipshultz, P. Y. Hsue, K. Squires, M. Schambelan, et al. State of the Science Conference: Initiative to Decrease Cardiovascular Risk and Increase Quality of Care for Patients Living With HIV/AIDS: Executive Summary Circulation, July 8, 2008; 118(2): 198 - 210. [Full Text] [PDF]
|
|
|
|
|
|
J. S. Currier, J. D. Lundgren, A. Carr, D. Klein, C. A. Sabin, P. E. Sax, J. T. Schouten, M. Smieja, and for Working Group 2 Epidemiological Evidence for Cardiovascular Disease in HIV-Infected Patients and Relationship to Highly Active Antiretroviral Therapy Circulation, July 8, 2008; 118(2): e29 - e35. [Full Text] [PDF]
|
|
|
|
|
|