Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC)

Woodward, Mark; Brindle, Peter; Tunstall‐Pedoe, Hugh

doi:10.1136/hrt.2006.108167

Cited by 584 publications

(506 citation statements)

References 12 publications

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“…Predictors included in existing models vary in their number as well as in how they are treated in the models [6]. Some risk prediction equations, including those derived from the Framingham Study, have treated diabetes status as just one of many prognostic variables [7,8]. The reliability of the Framingham equations for predicting risk of CVD in people with diabetes has been questioned [9], as the original equation was derived from a population with only 337 diabetic patients [10] and included no specific measure of glycaemia.…”

Section: Introductionmentioning

confidence: 99%

The Framingham and UK Prospective Diabetes Study (UKPDS) risk equations do not reliably estimate the probability of cardiovascular events in a large ethnically diverse sample of patients with diabetes: the Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation (ADVANCE) Study

et al. 2010

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Aims/hypothesis Available multivariable equations for cardiovascular risk assessment in people with diabetes have been derived either from the general population or from populations with diabetes. Their utility and comparative performance in a contemporary group of patients with type 2 diabetes are not well established. Diabetologia (2010) 53:821-831 DOI 10.1007/s00125-010-1681 Framingham and the UKPDS risk equations in 7,502 individuals with type 2 diabetes without prior known CVD at their enrolment in the trial. Results The risk of major CVD was overestimated by 170% (95% CI 146-195%) and 202% (176-231%) using the two Framingham equations. The risk of major coronary heart disease was overestimated by 198% (162-238%) with the UKPDS, and by 146% (117-179%) and 289% (243-341%) with the two different Framingham equations, respectively. The risks of stroke events were also overestimated with the UKPDS and one of the Framingham equations. The ability of these equations to rank risk among ADVANCE participants was modest, with c-statistics ranging from 0.57 to 0.71. Results stratified by sex, treatment allocation and ethnicity were broadly similar. Conclusions/interpretation Application of the Framingham and UKPDS risk equations to a contemporary treated group of patients with established type 2 diabetes is likely to substantially overestimate cardiovascular risk.

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Section: Introductionmentioning

confidence: 99%

The Framingham and UK Prospective Diabetes Study (UKPDS) risk equations do not reliably estimate the probability of cardiovascular events in a large ethnically diverse sample of patients with diabetes: the Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation (ADVANCE) Study

et al. 2010

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“…7,8 Further risk equations continue to be developed, usually using national data sources. [9][10][11] To date, the Framingham cardiovascular equation remains the most widely used method of assessing risk. This equation was derived from a large cohort study in the United States.…”

Section: Introductionmentioning

confidence: 99%

The effect of blood pressure and cholesterol variability on the precision of Framingham cardiovascular risk estimation: a simulation study

Marshall

2010

J Hum Hypertens

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This simulation study investigates the effects of withinindividual variability in estimated cardiovascular risk on categorization of patients as high risk. Published estimates of within-individual blood pressure and cholesterol variability were used to generate blood pressure and cholesterol levels for hypothetical subjects at a range of ages. These were used to calculate the estimated cardiovascular risk of each individual. The relationship between an individual's mean cardiovascular risk and within-individual coefficient of variation for cardiovascular risk was determined. Using the derived relationship, mean cardiovascular risk and withinindividual variation in risk was calculated for 5018 adults from a population health survey. From this, was determined their probability of being classified as high risk (420% 10-year cardiovascular risk) and the test characteristics of risk estimation at a range of ages. Within-individual variability in cardiovascular risk and potential for misclassification are both greater in lowerrisk populations. At age 35-44 years, the positive predictive value of a diagnosis of high risk is 0.61 (95% confidence interval (CI): 0.59-0.64), and at age 65-74 years, it is 0.94 (95% CI: 0.91-0.96). About 39% of adults under 45 years diagnosed as high risk are not at high risk. Cardiovascular risk assessment should be targeted at high-risk populations.

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“…[7][8][9] Smoking acts synergistically with other major risk factors of CVD such as age, sex, high blood pressure, dyslipidemia and diabetes. 10 This has led to the development and validation of a number of multivariable risk models which can be used by primary care physicians to assess the risk in individual patients of developing all atherosclerotic CVD [11][12][13][14][15][16][17][18][19][20] or specific types of CVD, namely, coronary heart disease, 10,[20][21][22][23][24][25] stroke, 26 peripheral vascular disease, 27 or heart failure. 28 Multivariable assessment has been advocated to estimate absolute CVD risk and guide treatment of risk factors, 2,29 and the Framingham CVD risk assessment tool and other similar risk assessment tools have been validated 22,23,25 and also re-calibrated in other ethnically diverse populations.…”

Section: Introductionmentioning

confidence: 99%

Framingham Risk Prediction Equations for Incidence of Cardiovascular Disease Using Detailed Measures for Smoking

Mannan

Stevenson

Peeters

et al. 2010

Heart, Lung and Circulation

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Current prediction models for risk of cardiovascular disease (CVD) incidence incorporate smoking as a dichotomous yes/no measure. However, the risk of CVD associated with smoking also varies with the intensity and duration of smoking and there is a strong association between time since quitting and the risk of disease onset. This study aims to develop improved risk prediction equations for CVD incidence incorporating intensity and duration of smoking and time since quitting.The risk of developing a first CVD event was evaluated using a Cox's model for participants in the Framingham offspring cohort who attended the fourth examination between the ages of 30 and 74 years and were free of CVD (n=3751). The full models based on the smoking variables and other risk factors, and reduced models based on the smoking variables and non-laboratory risk factors demonstrated good discrimination, calibration and global fit. The incorporation of both time since quitting among past smokers and packyears among current smokers resulted in better predictive performance as compared to a dichotomous current/non-smoker measure and a current/quitter/never smoker measure. Compared to never smokers, the risk of CVD incidence increased with pack-years. Risk among those quitting more than five years prior to the baseline exam and within five years prior to the baseline exam were similar and twice as high as that of never smokers. A CVD risk equation incorporating the effects of pack-years and time since quitting provides an improved tool to quantify risk and guide preventive care.

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Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC)

Cited by 584 publications

References 12 publications

The effect of blood pressure and cholesterol variability on the precision of Framingham cardiovascular risk estimation: a simulation study

Framingham Risk Prediction Equations for Incidence of Cardiovascular Disease Using Detailed Measures for Smoking

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