Framingham risk prediction equations for incidence of cardiovascular disease using detailed measures for smoking

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 (1988–92) 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 pack-years 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.

The addition of depression to the Framingham Risk Equation model for predicting coronary heart disease risk in women

BACKGROUND: Depression is widely considered to be an independent and robust predictor of Coronary Heart Disease (CHD), however is seldom considered in the context of formal risk assessment. We assessed whether the addition of depression to the Framingham Risk Equation (FRE) improved accuracy for predicting 10-year CHD in a sample of women.

DESIGN: A prospective, longitudinal design comprising an age-stratified, population-based sample of Australian women collected between 1993 and 2011 (n=862).

METHODS: Clinical depressive disorder was assessed using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID-I/NP), using retrospective age-of-onset data. A composite measure of CHD included non-fatal myocardial infarction, unstable angina coronary intervention or cardiac death. Cox proportional-hazards regression models were conducted and overall accuracy assessed using area under receiver operating characteristic (ROC) curve analysis.

RESULTS: ROC curve analyses revealed that the addition of baseline depression status to the FRE model improved its overall accuracy (AUC:0.77, Specificity:0.70, Sensitivity:0.75) when compared to the original FRE model (AUC:0.75, Specificity:0.73, Sensitivity:0.67). However, when calibrated against the original model, the predicted number of events generated by the augmented version marginally over-estimated the true number observed.

CONCLUSIONS: The addition of a depression variable to the FRE equation improves the overall accuracy of the model for predicting 10-year CHD events in women, however may over-estimate the number of events that actually occur. This model now requires validation in larger samples as it could form a new CHD risk equation for women.

Fracture risk (FRISK) score : Geelong Osteoporosis Study

Purpose: To develop and evaluate a fracture risk (FRISK) score based on multiple-site bone mineral density (BMD) measurements and other risk factors, to enable prediction of future fracture occurrence.

Materials and Methods: All participants gave written informed consent, and the study was approved by the Barwon Health Research and Ethics Advisory Committee. BMD was measured at the femoral neck and spine in two concurrently recruited groups: women 60 years of age or older who had sustained a low-trauma fracture of the hip, spine, humerus or distal forearm during a 2-year ascertainment period (n = 231; mean age, 74 years ± 7 [standard deviation]) and a population-based random sample of women who had not sustained a fracture during the recruitment period (n = 448; mean age, 72 years ± 8). Falls in the previous year and the number of self-reported fractures in adult life were recorded. Coefficients of a multiple logistic regression model were used as weightings for a combined model. A longitudinal population-based sample was used to assess the fracture risk equation (n = 600; median age, 74 years; interquartile range, 67–82 years).

Results: The FRISK score was obtained from the following equation: 9.304 − 4.735BMDSP − 4.530BMDFN + 1.127FS + 0.344NPF + 0.037W, where BMDSP is spinal BMD (in grams per square centimeter), BMDFN is femoral neck BMD, FS is falls score, NPF is number of previous fractures, and W is weight (in kilograms). The FRISK score successfully predicted 75% of fractures 2 years after baseline measurements in subjects in the longitudinal study with 68% specificity.

Conclusion: This study resulted in the derivation of a fracture risk score that successfully predicted 75% of fractures 2 years after baseline.

Objectively measured sedentary time, physical activity, and metabolic risk the Australian diabetes, obesity and lifestyle tudy (AusDiab)

OBJECTIVE—We examined the associations of objectively measured sedentary time and physical activity with continuous indexes of metabolic risk in Australian adults without known diabetes.

RESEARCH DESIGN AND METHODS—An accelerometer was used to derive the percentage of monitoring time spent sedentary and in light-intensity and moderate-to-vigorous–intensity activity, as well as mean activity intensity, in 169 Australian Diabetes, Obesity and Lifestyle Study (AusDiab) participants (mean age 53.4 years). Associations with waist circumference, triglycerides, HDL cholesterol, resting blood pressure, fasting plasma glucose, and a clustered metabolic risk score were examined.

RESULTS—Independent of time spent in moderate-to-vigorous–intensity activity, there were significant associations of sedentary time, light-intensity time, and mean activity intensity with waist circumference and clustered metabolic risk. Independent of waist circumference, moderate-to-vigorous–intensity activity time was significantly beneficially associated with triglycerides.

CONCLUSIONS—These findings highlight the importance of decreasing sedentary time, as well as increasing time spent in physical activity, for metabolic health.

Laboratory and non-laboratory-based risk prevention models for secondary prevention of cardiovascular disease : the LIPID study

Aims : The aims of this study were to examine whether risk prediction models for recurrent cardiovascular disease (CVD) events have prognostic value, and to particularly examine the performance of those models based on non-laboratory data. We also aimed to construct a risk chart based on the risk factors that showed the strongest relationship with CVD.

Methods and results : Cox proportional hazards models were used to calculate a risk score for each recurrent event in a CVD patient who was enrolled in a very large randomized controlled clinical trial. Patients were then classified into groups according to quintiles of their risk score. These risk models were validated by calibration and discrimination analyses based on data from patients recruited in New Zealand for the same study. Non-laboratory-based risk factors, such as age, sex, body mass index, smoking status, angina grade, history of myocardial infarction, revascularization, stroke, diabetes or hypertension and treatment with pravastatin, were found to be significantly associated with the risk of developing a recurrent CVD event. Patients who were classified into the medium and high-risk groups had two-fold and four-fold the risk of developing a CVD event compared with those in the low-risk group, respectively. The risk prediction models also fitted New Zealand data well after recalibration.

Conclusion : A simpler non-laboratory-based risk prediction model performed equally as well as the more comprehensive laboratory-based risk prediction models. The risk chart based on the further simplified Score Model may provide a useful tool for clinical cardiologists to assess an individual patient's risk for recurrent CVD events.

Engaging community pharmacists in the primary prevention of cardiovascular disease : protocol for the pharmacist assessment of adherence, risk and treatment in cardiovascular disease (PAART CVD) pilot study

Background: Cardiovascular disease (CVD) is the leading cause of death globally. Community pharmacist intervention studies have demonstrated clinical effectiveness for improving several leading individual CVD risk factors. Primary prevention strategies increasingly emphasise the need for consideration of overall cardiovascular risk and concurrent management of multiple risk factors. It is therefore important to demonstrate the feasibility of multiple risk factor management by community pharmacists to ensure continued currency of their role.
Methods/Design: This study will be a longitudinal pre- and post-test pilot study with a single cohort of up to 100 patients in ten pharmacies. Patients aged 50-74 years with no history of heart disease or diabetes, and taking antihypertensive or lipid-lowering medicines, will be approached for participation. Assessment of cardiovascular risk, medicines use and health behaviours will be undertaken by a research assistant at baseline and following the intervention (6 months). Validated interview scales will be used where available. Baseline data will be used by accredited medicines management pharmacists to generate a report for the treating community pharmacist. This report will highlight individual patients’ overall CVD risk and individual risk factors, as well as identifying modifiable
health behaviours for risk improvement and suggesting treatment and behavioural goals. The treating community pharmacist will use this information to finalise and implement a treatment plan in conjunction with the patient and their doctor. Community pharmacists will facilitate patient improvements in lifestyle, medicines adherence, and medicines management over the course of five counselling sessions with monthly intervals. The primary outcome will be the change to average overall cardiovascular risk, assessed using the Framingham risk equation.
Discussion: This study will assess the feasibility of implementing holistic primary CVD prevention programs into community pharmacy, one of the most accessible health services in most developed countries.

The projected impact of population and high-risk strategies for risk-factor control on coronary heart disease and stroke events

Objective: To model the impact of both population and high-risk strategies on cardiovascular disease (CVD) outcomes.

Design, setting and participants: A CVD risk-factor survey was carried out in rural south-eastern Australia from 2004 to 2006. Using a stratified random sample, data for 1116 participants aged 35–74 years were analysed. Applying the Framingham risk equations to risk-factor data, 5-year probabilities of a coronary heart disease event, stroke and cardiovascular event were calculated. The effect of different changes in risk factors were modelled to assess the extent to which cardiovascular diseases can be prevented by changing the risk factors at a population level (population strategy), among the high-risk individuals (high-risk strategy) or both.

Results: Among men, a population strategy could reduce cardiovascular events by 19.3% (193 per 1000 per 5 years), the high-risk strategy by 12.6% (126 per 1000) and a combined strategy by 24.1% (241 per 1000); and among women, by 21.9% (219 per 1000), 19.0% (190 per 1000) and 28.7% (287 per 1000), respectively.

Conclusions: For prevention of CVD in Australia, it is important both to treat high-risk individuals and to reduce the mean risk-factor levels in the population. We show how risk-factor survey data can be used to set targets for prevention and to monitor progress in line with the recommendations of the National Preventative Health Taskforce.

Increased cardiometabolic risk is associated with increased TV viewing time

Purpose: Television viewing time, independent of leisure time physical activity, has cross-sectional relationships with the metabolic syndrome and its individual components. We examined whether baseline and 5-yr changes in self-reported television viewing time are associated with changes in continuous biomarkers of cardiometabolic risk (waist circumference, triglycerides, HDL-cholesterol, systolic and diastolic blood pressure, fasting plasma glucose, and a clustered cardiometabolic risk score) in Australian adults.


Methods: The Australian Diabetes, Obesity and Lifestyle Study (AusDiab) is a prospective, population-based cohort study with biological, behavioral, and demographic measures collected in 1999-2000 and 2004-2005. Noninstitutionalized adults aged >=25 yr were measured at baseline (11,247; 55% of those completing an initial household interview); 6400 took part in the 5-yr follow-up biomedical examination, and 3846 met the inclusion criteria for this analysis. Multiple linear regression analysis was used, and unstandardized B coefficients (95% confidence intervals (CI)) are provided.


Results: Baseline television viewing time (10 h·wk-1 unit) was not significantly associated with change in any of the biomarkers of cardiometabolic risk. Increases in television viewing time over 5 yr (10 h·wk-1 unit) were associated with increases in waist circumference (men: 0.43 cm, 95% CI = 0.08-0.78 cm, P = 0.02; women: 0.68 cm, 95% CI = 0.30-1.05, P < 0.001), diastolic blood pressure (women: 0.47 mm Hg, 95% CI = 0.02-0.92 mm Hg, P = 0.04), and the clustered cardiometabolic risk score (women: 0.03, 95% CI = 0.01-0.05, P = 0.007). These associations were independent of baseline television viewing time and baseline and change in physical activity and other potential confounders.


Conclusions: These findings indicate that an increase in television viewing time is associated with adverse cardiometabolic biomarker changes. Further prospective studies using objective measures of several sedentary behaviors are required to confirm causality of the associations found.

Assessing risk post intervention for an acute coronary syndrome: a review of the risk assessment tools and their development

Considerable variability in survival rate after an acute myocardial infarction exists and accurate risk stratification is of significant importance. The American College of Cardiology and the American Heart Association has recommended early risk stratification using several clinical risk scoring instruments to identify high risk patients. The aim of this paper is to identify secondary cardiovascular risk scoring instruments that could be utilized at the time of intervention for acute coronary syndromes and compare their psychometric properties as they were developed. A search using Medline, Cumulative Index to Nursing and Allied Health Literature and the Psychology and Behavioral Sciences Collection data-bases identified studies published between January 1990 and January 2010 used to measure risk after intervention for acute coronary syndrome. Four validated secondary risk prediction scoring instruments were identified for comparison.Secondary risk prediction scoring instruments for the acute coronary syndrome patient population are evidence based, valid and reliable. Use of the instruments by cardiac focused clinicians will aid in the determination of treatment strategies, and estimation of short and long term events and mortality.

FRAX (Aus) and falls risk: Association in men and women

Purpose: The WHO fracture risk prediction tool (FRAX®) utilises clinical risk factors to estimate the probability of fracture over a 10-year period. Although falls increase fracture risk, they have not been incorporated into FRAX. It is currently unclear if FRAX captures falls risk and whether addition of falls would improve fracture prediction. We aimed to investigate the association of falls risk and Australian-specific FRAX. Methods: Clinical risk factors were documented for 735 men and 602 women (age 40-90. yr) assessed at follow-up (2006-2010 and 2000-2003, respectively) of the Geelong Osteoporosis Study. FRAX scores with and without BMD were calculated. A falls risk score was determined at the time of BMD assessment and self-reported incident falls were documented from questionnaires returned one year later. Multivariable analyses were performed to determine: (i) cross-sectional association between FRAX scores and falls risk score (Elderly Falls Screening Test, EFST) and (ii) prospective relationship between FRAX and time to a fall. Results: There was an association between FRAX (hip with BMD) and EFST scores (. β=. 0.07, p<. 0.001). After adjustment for sex and age, the relationship became non-significant (. β=. 0.00, p=. 0.79). The risk of incident falls increased with increasing FRAX (hip with BMD) score (unadjusted HR 1.04, 95% CI 1.02, 1.07). After adjustment for age and sex, the relationship became non-significant (1.01, 95% CI 0.97, 1.05). Conclusions: There is a weak positive correlation between FRAX and falls risk score, that is likely explained by the inclusion of age and sex in the FRAX model. These data suggest that FRAX score may not be a robust surrogate for falls risk and that inclusion of falls in fracture risk assessment should be further explored.

Screen-based behaviors of children and cardiovascular risk factors

OBJECTIVE: To determine whether the amount of time spent in screen-based behaviors (SBBs; television viewing, computer use, and playing electronic games) is independently associated with individual and clustered cardiovascular disease (CVD) risk factors among elementary school children. STUDY DESIGN: Baseline data were used from 264 children (age 7-10 years) participating in the Transform-Us! cluster-randomized controlled trial. Time (h/d) spent in SBBs was obtained using a parent proxy-report questionnaire. Anthropometrics, blood pressure (BP), and lipids were measured using standard techniques. A clustered CVD risk score was calculated as the average of the standardized values of the subcomponents (waist circumference [WC], systolic BP, diastolic BP, and lipids). RESULTS: After adjusting for sex, parent education, physical activity (accelerometry), diet, and WC (when adiposity was not the outcome), television viewing time was positively associated with body mass index z-score (P = .002), WC (P = .02), and systolic BP (P = .05). Electronic games was positively associated with low density lipoprotein levels (P = .05), and total screen-time was positively associated with body mass index (P = .02). CONCLUSIONS: Differential associations were observed between types of SBBs and CVD risk factors, indicating that not all SBBs are adversely associated with obesity and CVD risk. There is a need to differentiate between types of SBBs when evaluating the CVD risk associated with screen behaviors in children. TRIAL REGISTRATION: International Standard Randomized Controlled Trial: ISRCTN83725066; Australian New Zealand Clinical Trials Registry: ACTRN12609000715279.