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JOURNAL OF CLINICAL CARDIOLOGY

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RESEARCH ARTICLE OPEN ACCESS

VOLUME 5 | ISSUE 2 | DOI: HTTPS://DOI.ORG/10.33696/CARDIOLOGY.5.057

EXTERNAL VALIDATION OF FOUR CARDIOVASCULAR RISK PREDICTION MODELS

ALAUDDIN BHUIYAN1,2,*, ARUN GOVINDAIAH1, R THEODORE SMITH2

 * 1iHealthScreen Inc., Richmond Hill, NY, USA
 * 2Icahn School of Medicine at Mount Sinai, New York, NY, USA
   

+ Affiliations - Affiliations


*Corresponding Author

Alauddin Bhuiyan, alauddin.bhuiyan@gmail.com

Received Date: July 04, 2024

Accepted Date: July 19, 2024

Citation

Bhuiyan A, Govindaiah A, Smith RT. External Validation of Four Cardiovascular
Risk Prediction Models. J Clin Cardiol. 2024;5(2):73-80.


Copyright
© 2024 Bhuiyan A, et al. This is an open-access article distributed under the
terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author
and source are credited.


ABSTRACT

Background and Purpose: Cardiovascular disease (CVD) is a leading cause of death
and disability in the world. Many CVD risk prediction models have been created,
but those most widely used in clinical settings have not been externally
validated, a significant gap addressed herein.

Methods: Using the Multi-Ethnic Study of Atherosclerosis (MESA), we have
externally validated the Framingham Risk Score, ASSIGN (Assessing the
cardiovascular disease risk using SIGN) risk score, Atherosclerotic
Cardiovascular Disease (ASCVD) risk score, and the European SCORE model, which
were selected based on popularity among clinicians and frequency of clinical
use. The models were implemented in a computer program based on published
algorithms, and 100 incident CVD and 100 non-incident CVD subjects from MESA
were selected for testing the repeatability. The outcome in both models achieved
100% correlation. The individual model accuracy was tested by computing their
sensitivity, specificity, accuracy, and C-statistics.

Results: For discrimination, ASCVD showed the highest C-statistic (0.717) and
SCORE the lowest (0.677). The Framingham score provided the most consistent
results among the models, with a sensitivity of 69% and a specificity of 62%,
but overestimated the risks as shown by the calibration results. SCORE was
relatively inconsistent, with a sensitivity of 34% and specificity of 85%, for a
risk threshold set at the top 20%.

Conclusions: Calibration experiments showed that the transportability of the
risk was generally poor in all models, with Framingham and SCORE particularly
overestimating the risks for the American population There is clearly a need for
better models to predict CVD risk in the multi-ethnic American population.


KEYWORDS

Cardiac biomarkers, Cardiovascular risk reduction, Congestive heart failure

INTRODUCTION

Cardiovascular diseases (CVDs) such as heart disease, stroke, etc. are the most
deadly health issues in the world [1-3] producing a huge financial burden on
societies [4-6]. Predicting the risk of CVD is essential for optimizing
management and treatment guidelines. Extensive research has been conducted to
develop prediction CVD models [7-9], yet their validity is a cause of concern.

The current standard of care for CVD prevention is based on risk prediction
models with multiple risk factors such as age, gender, hypertension,
dyslipidemia, smoking, etc. There are well-known models such as the Framingham
Risk Score [10], the Prospective Cardiovascular Münster (PROCAM) score [11], the
Dundee risk disk [12], the British Regional Heart Study [13,14], Korean heart
study [15], SCORE [16] and the QRISK3 prediction model [17]. However, many of
the risk scores used in clinics are either developed locally or are non-local
models calibrated to suit the local population [18] and determine preventative
measures [19]. These models have been adopted in primary care settings as
simplified charts, tables, computer programs, and web-based tools, and are
routinely referred to in policy documents and guidelines.

These models do not capture all cardiovascular risks: the Framingham risk score
is superior to any single risk factor or model but can only predict up to 50% of
CVD cases. Also, recent systematic reviews [16,20] reported that the Framingham
risk score has a variable performance with under-prediction and over–prediction
of the risk in high-risk and lower-risk populations, respectively. Risk
overestimation causes increased costs, and risk underestimation leads to missing
vulnerable cases.

Our literature review-based observations suggest many reasons that have led to a
lack of reliable applied prediction models for heart disease: i) no
comprehensive overview of the models has been undertaken, ii) only a few were
externally validated on their predictive performance, and iii) lack of external
validation makes them almost valueless for practitioners, policymakers, and
guideline developers. Therefore, despite many studies [21-23] on these models,
the critical element of external validation to assess performance when
transported to other regions has been lacking. In this paper, we validate four
major CVD risk prediction models externally on the MESA dataset.

METHODOLOGY

Selection of prediction models for external validation

We used a published review on the latest use of prediction models for
cardiovascular disease and how to choose the right one [18]. The selected models
are among the most promising and are implemented practically in clinical
settings. In addition, we used the model mentioned in the American Heart
Association and the American Cardiovascular Association (AHA/ACA) guidelines
[24]. In total, four CVD risk prediction models are used in this study.

Prediction models included for analysis

The various CVD risk prediction models that are selected are briefly described
below. The common practice for building models is to use Cox
proportional-hazards regression to form an equation that outputs the risk
percentage. It is, sometimes, further simplified by making it a points-based
scoring system that is easy for clinical use. In brief, the natural logs of the
predictors (age, total cholesterol, HDL cholesterol, Systolic BP, etc.) are
calculated. These values are then multiplied by the coefficients from the
equation (usually for the specific race-gender group of the individual). The sum
of these products for all factors is calculated. The estimated 10-year risk of a
first hard ASCVD event is formally calculated as 1 minus the survival rate at
ten years raised to the power of the exponent of the sum minus the race- and
sex-specific overall mean sum. This general method is used to calculate the
risks in most models.



Where S is the baseline survival, IndX’B is the coefficient X value, and MeanX’B
is the overall mean of race-specific and sex-specific coefficient X value.

Framingham risk score for cardiovascular disease (FRS-CVD)

Framingham risk scores were originally developed on the data obtained from the
Framingham Heart Study to estimate the 10-year risk of developing coronary heart
disease, with additional risk scores for other cardiovascular diseases and
events developed later [25]. In our study, we use the Framingham scores for
10-year cardiovascular disease risk [26]. The Framingham CVD risk score is a
gender-specific Cox proportional-hazards regression based on age (30-74), total
cholesterol (100-405 mg/dL), cigarette smoking status, HDL cholesterol (10-100
mg/dL) and systolic blood pressure (90-200 mmHg) to estimate the risk of an
event in 8491 Framingham study participants between the ages 30 and 74 and free
of CVD at recruitment. The point-based system to calculate the risk score is
outlined in Appendices 1 and 2 for women and men.

ASSIGN risk score

The ASSIGN risk score [27] was developed on the data and cardiovascular outcomes
in the Scottish Heart Health Extended Cohort (SHHEC) [28] to assess the 10-year
percentage risk of cardiovascular disease (any manifestation of coronary heart
disease or cerebrovascular disease including transient ischemic attacks).
Participants were 6,540 men and 6,757 women aged 30-74, initially free of
cardiovascular disease, ranked for social deprivation by postcode. The unique
aspect of the ASSIGN score is the addition of social deprivation and family
history to traditional risk factors found in the Framingham Score. It was shown
to be marginally better in discriminating cases and non-cases than the
Framingham Score [18]. The study provides mean scores for the components when
the data is insufficient to produce a risk score. Since the MESA study does not
have a social deprivation factor known as the Scottish Index of Multiple
Deprivation (SIMD), we use 20 instead, suggested as the median value for SIMD by
ASSIGN. The components in the model are age (25-90), sex, Scottish postcode for
SIMD (or a median score of 20 for non-Scottish population), family history of
CHD/Stroke, diabetes status, cigarettes smoked daily (0-100), systolic blood
pressure (80-250 mmHg), total cholesterol (2-12.5 mmol/l), and HDL cholesterol
(0.3 – 3.5 mmol/l).

Table 1 shows the published results based on the respective datasets used in the
four risk prediction models within the performance measured by sensitivity,
specificity, accuracy, and C-statistic. The top quintile is chosen for analyzing
the performance measures, and for SCORE, a threshold of the top 5% (in low-risk
regions of Europe) or above is shown. SCORE did not report top quintile
performance in its original paper. The values are averages across population
groups, and/or gender. The Framingham score is an average for four individual
diseases (coronary heart disease, stroke, congestive heart failure, and
intermittent claudication).

Table 1. Overall performances of the four CVD prediction models which were
reported originally and in different population groups.

Study

Population

Incident CVD

Sensitivity at High Risk (Top 20%/5% risk group)

Specificity at High Risk (Top 20%)

Accuracy at High Risk (top 20%)

C-Statistics

Framingham CVD

8491

1174

66.46% (>20% risk)

81.02%

(>20% risk)

NA

0.733 to 0.851

ASCVD

24626

2689

NA

NA

NA

0.717 to 0.818

ASSIGN

13297

1165

46.30%

(>20% risk)

82.50%

(>20% risk)

79.30%

(>20% risk)

0.727 to 0.765

SCORE (European CVD model)

205178

7934

20-43%

(> 5% risk)

88-96% (>5% risk)

NA

0.74 to 0.84

ASCVD risk score

The atherosclerotic cardiovascular disease (ASCVD) risk score is based on the
recommendations and guidelines provided by the 2013 American Heart Association
(AHA) and American College of Cardiology (ACC) report [24]. A total of 11,240
white women, 9,098 white men, 2,641 African-American women, and 1,647
African-American men initially free of any fatal CVD were included. The
components included in this risk calculation method are age (40-79), diabetes
status, gender, race, smoking status, total cholesterol (mg/dL), HDL cholesterol
(mg/dL), systolic blood pressure (mmHg), and treatment for hypertension. Gender-
and race-specific equations were developed to predict the risk of the first
ASCVD event. The formula for calculating the ASCVD score is provided by Goff et
al. 2014 [24].

SCORE (European CVD risk score)

The SCORE project [29] created a risk-scoring system for clinical use among the
European population. It combined data from 12 European cohort studies, collected
patient data from 250,000 subjects, and recorded about 7,000 fatal CV events
[30]. The score was developed differently for two regions of Europe,
categorizing them as low-risk countries (predominantly Western Europe and
Scandinavia) and high-risk countries (predominantly Eastern Europe). The model
used age, gender, systolic blood pressure (mmHg), total cholesterol (mmol/L),
and smoking status as inputs to the final percentage risk score. SCORE provides
a simple and easy-to-use chart for reference and use in clinical practice. The
procedure for calculating the risk percentage using the SCORE model is based on
the European guidelines for CVD Prevention in Clinical Practice [31]. We used
the low-risk version of SCORE to evaluate it on the MESA dataset because the
MESA demographic (USA) matches the low-risk European countries as defined by
SCORE.

The population of the external validation dataset

The Multi-Ethnic Study of Atherosclerosis (MESA) [32], with a primary objective
of determining characteristics related to the progression of subclinical
cardiovascular disease to clinical cardiovascular disease, is a research study
of over 6,000 men and women from different ethnicities in the United States. The
study is sponsored by the National Heart, Lung, and Blood Institute of the NIH.
In the baseline was a diverse population sample of 6,814 men and women aged
45-84 without any known CVD conditions, but who could have risk factors. About
38% of the participants were white, 28% African American, 22% Hispanic, and 12%
Asian. Females comprised 53% (3,601) of the study population, whereas males
comprised 47% (3,213). Participants were followed for identification and
characterization of cardiovascular disease events, including acute myocardial
infarction and other forms of coronary heart disease (CHD), stroke, congestive
heart failure, mortality, and cardiovascular disease interventions. The subjects
had their first follow-up examinations over two years from July 2000 to July
2002, with a follow-up period of approximately 17-20 months, and were followed
until their fifth exam before the dataset was made available. The fifth
examination took place from April 2010 to January 2012. The baseline
characteristics of the study participants who did/did not have incident CVD in
the follow-up visits are shown in Table 2.

Table 2. Baseline characteristics of the MESA population who did/did not have
incident CVD.

Baseline Characteristics of the Validation Cohort

CVD (n=940)

Non-CVD (n=5873)

Missing data (%)

Age, years ± SD

68 ± 10

62 ± 10

0

Male, n (%)

378 (40)

2649 (45)

0

Race/ethnicity

   

0

Non-Hispanic white American

380 (40%)

2241 (38%)

-

Chinese American

86 (9%)

718 (12%)

-

African American

260 (28%)

1632 (29%)

-

Hispanic American

214 (23%)

1282 (21%)

-

SBP, mmHg ± SD

136 ± 23

126 ± 22

0.3

DBP, mmHg ± SD

74 ± 11

72 ± 11

0.3

Diabetes, n (%)

357 (38%)

2588 (44%)

0.4

Current smoker, n (%)

528 (56%)

2846 (48%)

0.3

Body mass index, kg/m2 ± SD

28.72 ± 5

28.2 ± 6

0

Total cholesterol, mg/dL ± SD

193 ± 33

194 ± 35

0.4

High-density lipoprotein cholesterol, mg/dL ± SD

48 ± 14

51 ± 15

0.4

Low-density lipoprotein cholesterol, mg/dL ± SD

117 ± 29

117 ± 32

0.4

‘n’ is the number of subjects, SD is the standard deviation, SBP is Systolic
Blood Pressure, and DBP is Diastolic Blood Pressure.

Assessment of cardiovascular diseases

In the MESA study, the data regarding cardiovascular diseases were collected
from a variety of sources, including death certificates, medical records,
in-person interviews, autopsy reports, and actively contacting kin or doctor of
the subject in case of death or occurrence of severe CVD. Fatal and non-fatal
cardiovascular events are defined based on MESA criteria. Fatal events are
further classified as definite fatal CHD, definite fatal MI, possible fatal CHD,
stroke death, non-coronary/non-stroke death, and other cardiovascular disease
death. Non-fatal events include MI, resuscitated cardiac arrest, angina,
congestive heart failure, peripheral vascular disease, stroke, and TIA. In our
study, we assess prediction models for overall CVD (fatal and non-fatal events)
in the MESA dataset.

Assessment of predictors

The prediction models selected for this study use a variety of
features/component variables for predicting CVD, such as socio-demographic data,
medical history, family history, and social deprivation. Most of these
parameters are readily available in the MESA dataset. The missing data were
replaced by population means and by extrapolating data whenever possible. For,
when age is unknown at a particular visit, it is extrapolated from the previous
visit. If a subject has any missing critical information that cannot be
estimated by reliable means, then the subject is removed from the assessment.
Otherwise, the population means segregated by age and gender were used to
replace the parameters, as shown in Appendix 4.

Framingham risk score takes the age (30-79), total cholesterol (mg/dL), HDL
(mg/dL), systolic blood pressure (mmHg), and smoking status as input for the CVD
risk model. This model is race-specific and is defined for non-Hispanic whites
and African Americans. However, the study recommends using the risk model
developed for non-Hispanic whites for an estimation of risk in populations other
than African Americans and non-Hispanic whites.

The ASSIGN CVD score uses age 30-74, sex, family history of CHD/stroke, diabetes
status, cigarette smoking status, systolic blood pressure (100-200 mmHg,
extrapolated if used beyond this range), total cholesterol, HDL, and a derived
social deprivation factor called SIMD. For the local Scottish population,
rheumatoid arthritis is also a component. However, the score has been built
without an arthritis element for outsiders, and we make use of that score. In
our study data, the social deprivation factor is not available, and hence, a
suggested score of 20 (middle of the most deprived fifths of the population) is
used in place. The provisional values for unknown components are provided by the
ASSIGN study and outlined in Appendix 3.

The ASCVD score based on ACC/AHA uses age (40-79), diabetes status, sex, race
(African-American and Non-Hispanic White), smoking status, total cholesterol
(mg/dL), HDL cholesterol (mg/dL), systolic blood pressure (mmHg) and treatment
for hypertension as predictors. ASCVD equations are sex- and race-specific
equations. They should be used for non-Hispanic whites and African Americans.
However, the study recommends that equations for non-Hispanic whites may be
considered for an estimation of risk in patients from populations other than
African Americans and non-Hispanic whites.

The European SCORE model uses age, gender, systolic blood pressure (mmHg), total
cholesterol (mmol/L), and smoking status as input variables. Subjects with
missing data for these predictors are removed.

We implemented each model in a computer program based on published algorithms,
and 100 incident CVD and 100 non-incident CVD subjects from MESA were selected
for testing. To be sure that our simulation accurately reflected real-world
clinical applications, the same MESA data were also tested on the original
models available on the web and results were compared to our implemented model.

Statistical analyses

To compare the various models, we used 933 subjects with incident CVD and 5,785
subjects without any incident CVD. We computed the results with measures such as
the area under the curve (AUC or C-statistic), sensitivity, specificity, and
accuracy to estimate the performance. First, we analyzed the AUC to assess the
discriminatory capability of the risk scores to correctly differentiate between
two subjects, one who will develop the condition within ten years and one who
will not. Second, we assess the agreement between the risks predicted by the
models and the observed outcomes in the study by measuring the sensitivity and
specificity of the models.

RESULTS

Implemented models compared to public web-based applications

The outcomes in each implementation of a given risk score achieved 100%
correlation with the results of public web-based applications.

Discrimination

The models showed similar C-statistics or AUC at the 10-year prediction of the
disease with little variation. ASCVD showed slightly better discriminative
ability with an AUC of 0.717 and SCORE showed the least (AUC 0.677) although the
difference is not very big. Figure 1 shows the receiver operating
characteristics of the four models and the associated areas under curve (AUC or
C-statistics).



Figure 1. Receiver Operating Characteristics of the four models - ASCVD,
Framingham, ASSIGN, and SCORE - on the MESA study data.

Calibration

Figure 2 shows the calibration plots on each of the risk scores. The calibration
curve is very helpful in evaluating a model's performance and reliability in
each population, especially when the score is developed on another population.
These curves are obtained by plotting actual probabilities vs. predicted
probabilities. Figure 2 depicts the calibration curve where the x-axis
represents the predicted probabilities in 8 divided groups (also called bins
that equal range of probabilities) are plotted against the y-axis which contains
true probabilities of each of the groups. The true probabilities are obtained by
taking the fraction of positives (CVD cases) in the respective groups. For
example, if a group has a total of 10 subjects and 2 of them are CVD cases and
the other 8 are non-CVD cases, then the true probability for that group is 20%
(or 0.2). For an ideal model, this probability is a non-decreasing quantity
matching the predicted probabilities (along the x-axis) represented by the black
straight line with slope one starting at the origin of the graph.



Figure 2. Calibration plots for the four cardiovascular disease risk scores
showing true probabilities vs. predicted probabilities.

The four calibration curves for the four prediction models are shown in Figure 2
drawn in a different color for different models. From the curve for the
Framingham score, we can see that as true probabilities increase, the predicted
probabilities increase disproportionately. This shows that the Framingham score
overestimates the CVD risk in the MESA population. This is also the same in
SCORE, whose predicted probabilities are higher than true probabilities, but
slightly better than Framingham, by being closer to the ideal calibration curve.
On the other hand, ASCVD and ASSIGN scores are in closer agreement with the
ideal calibration curve (the black line), and hence, the performance is better
for the two scores in this population.

The models are also assessed by computing the sensitivity and specificity by
taking the “high-risk” categories in each of the four models. We use a threshold
value of the top 20% (predicted probability risk) to evaluate the calibration of
the models. Table 3 shows an example of the discrimination capabilities of the
models within the different sensitivities and specificities for this threshold.
At this cut-off, the Framingham model shows a high sensitivity of 69.67% but
with a low specificity of 62.21%. The ASCVD model shows a sensitivity of 46.73%
and a specificity of 79.69%. These results contrast with the European models'
SCORE and ASSIGN, which show relatively low sensitivities of 43.19% and 34.94%,
but with higher specificities of 80.07% and 85.12%, respectively, for the said
threshold. We note that the results will vary for different percentages of the
threshold. The strict comparison can be depicted in Figure 2.

Table 3. Sensitivity and specificity results for the four 10-year CVD risk
prediction models evaluated using the 20% risk as the cut-off for “high-risk.”  

Sensitivity (95% CI)

Specificity (95% CI)

Accuracy (95% CI)

FRAMINGHAM

69.67%

66.61% to 72.61%

62.21%

60.95% to 63.46%

63.93%

61.96% to 66.01%

ASCVD

46.73%

43.49% to 49.99%

79.69%

78.63% to 80.72%

71.82%

70.21% to 72.55%

SCORE

43.19%

39.99% to 46.44%

80.07%

79.02% to 81.09%

68.09%

67.16% to 69.50%

ASSIGN

34.94%

31.88% to 38.10%

85.12%

84.17% to 86.02%

67.15%

66.04% to 68.77%

CONCLUSIONS

In this study, we have shown the performance of various cardiovascular risk
scores in a multi-ethnic dataset. This validation study showed that the
performance of four models for predicting CVD in a multi-ethnic population was
nearly identical to each other. We observed that the high-risk candidates from
one prediction model could easily be left out in another prediction model. This
also shows that no model could precisely and accurately predict the absolute
risk of cardiovascular diseases.

The discriminative abilities of the models varied minimally. Calibration
problems can be mitigated by moving the thresholds, although this may lead to
over-treatment in the case of identification of too many cases or
under-treatment in case of too few cases identified. Compared to the Framingham
model, all other models show poor calibration and need to be updated if it is
used for the demographic represented by MESA study participants.

Our study found that the transportability of the predicted risks was generally
poor from European scores to the American population. With the highest AUC and a
good calibration curve compared to the other scores, ASCVD, which is based on
the recommendation of the American Heart Association performs the best in our
calibration experiments.


REFERENCES

1. WHO, "Cardiovascular diseases: Avoiding heart attacks and strokes " World
Health Organization, vol.
https://www.who.int/news-room/questions-and-answers/item/cardiovascular-diseases-avoiding-heart-attacks-and-strokes.
2015. Last accessed on March 20, 2024.

2. Thomas H, Diamond J, Vieco A, Chaudhuri S, Shinnar E, Cromer S, et al. Global
Atlas of Cardiovascular Disease 2000-2016: The Path to Prevention and Control.
Glob Heart. 2018 Sep;13(3):143-63.

3. Timmis A, Vardas P, Townsend N, Torbica A, Katus H, De Smedt D, et al.
European Society of Cardiology: cardiovascular disease statistics 2021. Eur
Heart J. 2022 Feb 22;43(8):716-99.

4. Joseph P, Leong D, McKee M, Anand SS, Schwalm JD, Teo K, et al. Reducing the
Global Burden of Cardiovascular Disease, Part 1: The Epidemiology and Risk
Factors. Circ Res. 2017 Sep 1;121(6):677-94.

5. Leong DP, Joseph PG, McKee M, Anand SS, Teo KK, Schwalm JD, et al. Reducing
the Global Burden of Cardiovascular Disease, Part 2: Prevention and Treatment of
Cardiovascular Disease. Circ Res. 2017 Sep 1;121(6):695-710.

6. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al.
Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update
From the GBD 2019 Study. J Am Coll Cardiol. 2020 Dec 22;76(25):2982-3021.

7. Damen JA, Hooft L, Schuit E, Debray TP, Collins GS, Tzoulaki I, et al.
Prediction models for cardiovascular disease risk in the general population:
systematic review. BMJ. 2016 May 16;353:i2416.

8. Pylypchuk R, Wells S, Kerr A, Poppe K, Riddell T, Harwood M, et al.
Cardiovascular disease risk prediction equations in 400,000 primary care
patients in New Zealand: a derivation and validation study. Lancet. 2018 May
12;391(10133):1897-907.

9. Alaa AM, Bolton T, Di Angelantonio E, Rudd JHF, van der Schaar M.
Cardiovascular disease risk prediction using automated machine learning: A
prospective study of 423,604 UK Biobank participants. PLoS One. 2019 May
15;14(5):e0213653.

10. Schnabel RB, Sullivan LM, Levy D, Pencina MJ, Massaro JM, D'Agostino RB Sr,
et al. Development of a risk score for atrial fibrillation (Framingham Heart
Study): a community-based cohort study. Lancet. 2009 Feb 28;373(9665):739-45.

11. Assmann G, Cullen P, Schulte H. Simple scoring scheme for calculating the
risk of acute coronary events based on the 10-year follow-up of the prospective
cardiovascular Münster (PROCAM) study. Circulation. 2002 Jan 22;105(3):310-5.

12. Tunstall-Pedoe H. The Dundee coronary risk-disk for management of change in
risk factors. BMJ. 1991 Sep 28;303(6805):744-7

13. British-study, "British Regional Heart Study (BRHS),"
https://www.ucl.ac.uk/iehc/research/primary-care-and-population-health/research/brhs/study-design.

14. Walker M, Whincup PH, Shaper AG. The British Regional Heart Study 1975-2004.
Int J Epidemiol. 2004 Dec;33(6):1185-92.

15. Jee SH, Jang Y, Oh DJ, Oh BH, Lee SH, Park SW, et al. A coronary heart
disease prediction model: the Korean Heart Study. BMJ Open. 2014 May
21;4(5):e005025.

16. Barroso LC, Muro EC, Herrera ND, Ochoa GF, Hueros JI, Buitrago F.
Performance of the Framingham and SCORE cardiovascular risk prediction functions
in a non-diabetic population of a Spanish health care centre: a validation
study. Scand J Prim Health Care. 2010 Dec;28(4):242-8.

17. Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3
risk prediction algorithms to estimate future risk of cardiovascular disease:
prospective cohort study. BMJ. 2017 May 23;357:j2099.

18. Tunstall-Pedoe H. Cardiovascular Risk and Risk Scores: ASSIGN, Framingham,
QRISK and others: how to choose. Heart. 2011 Mar;97(6):442-4.

19. Cooney MT, Dudina AL, Graham IM. Value and limitations of existing scores
for the assessment of cardiovascular risk: a review for clinicians. J Am Coll
Cardiol. 2009 Sep 29;54(14):1209-27.

20. Rodondi N, Locatelli I, Aujesky D, Butler J, Vittinghoff E, Simonsick E, et
al. Framingham risk score and alternatives for prediction of coronary heart
disease in older adults. PloS one. 2012 Mar 28;7(3):e34287.

21. Siontis GC, Tzoulaki I, Siontis KC, Ioannidis JP. Comparisons of established
risk prediction models for cardiovascular disease: systematic review. BMJ. 2012
May 24;344:e3318.

22. Dahagam CR, Goud A, D'Souza J, Dhaliwal N. A Comprehensive Review of
Predictive Risk Models for Cardiovascular Disease. American College of
Radiology. 2016.
https://www.acc.org/latest-in-cardiology/articles/2016/08/03/13/47/a-comprehensive-review-of-predictive-risk-models-for-cardiovascular-disease.
Last accessed on November 16, 2019.

23. Wessler BS, Lai Yh L, Kramer W, Cangelosi M, Raman G, Lutz JS, et al.
Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive
Analytics and Comparative Effectiveness Clinical Prediction Model Database. Circ
Cardiovasc Qual Outcomes. 2015 Jul;8(4):368-75.

24. Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, et
al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of
the American College of Cardiology/American Heart Association Task Force on
Practice Guidelines. Circulation. 2014 Jun 24;129(25 Suppl 2):S49-73.

25. Dawber TR, Meadors GF, Moore FE. Epidemiological Approaches to Heart
Disease: The Framingham Study. Joint Session of the Epidemiology, Health
Officers, Medical Care, and Statistics in the Seventy-eighth Annual Meeting the
American Public Health Association, St. Louis, Mo, 1950.

26. D’Agostino Sr RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et
al. General cardiovascular risk profile for use in primary care: the Framingham
Heart Study. Circulation. 2008 Feb 12;117(6):743-53.

27. Woodward M, Brindle P, Tunstall-Pedoe H; SIGN group on risk estimation.
Adding social deprivation and family history to cardiovascular risk assessment:
the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart.
2007 Feb;93(2):172-6.

28. Tunstall-Pedoe H, Woodward M, Tavendale R, A'Brook R, McCluskey MK.
Comparison of the prediction by 27 different factors of coronary heart disease
and death in men and women of the Scottish Heart Health Study: cohort study.
BMJ. 1997 Sep 20;315(7110):722-9.

29. Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al.
Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE
project. Eur Heart J. 2003 Jun;24(11):987-1003.

30. The European Cardiovascular Disease Risk Assessment Model.
https://www.escardio.org/Education/Practice-Tools/CVD-prevention-toolbox/SCORE-Risk-Charts.
Last accessed on October 31, 2019.

31. Giannuzzi P, Wood DA, Saner H. European Guidelines on CVD Prevention in
Clinical Practice. Eur Heart J. 2007;28:2375-414.

32. Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, et al.
Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol.
2002 Nov 1;156(9):871-81.


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