Risk functions for prediction of cardiovascular disease in elderly Australians: the Dubbo Study

Predictive performance of cardiovascular disease risk prediction models in older adults: a validation and updating study

BACKGROUND

Current cardiovascular disease (CVD) risk prediction models tailored for older adults are inadequate. This study aimed to validate, update and assess the utility of widely used CVD risk prediction models including American College of Cardiology/American Heart Association, 2008 Framingham, GloboRisk, National Vascular Disease Prevention Alliance and Predict1 originally developed for middle-aged population, as well as an age-specific Systematic COronary Risk Evaluation 2-Older Person model, in Australian and the US community-dwelling older adults.

METHODS

Participants, without history of CVD events, dementia or physical disability, enrolled in the ASPREE (ASPirin in Reducing Events in the Elderly) clinical trial and ASPREE-eXTention observational post-trial follow-up, were considered for CVD risk prediction. The main outcome was predicted CVD risk from adjudicated CVD events. The performance of the original, recalibrated (adjusting models' intercept and slope) and updated (adjusting models' coefficients) models was evaluated by discrimination (C statistic), calibration (calibration plots) and clinical utility (decision curves). Models were extended by incorporating predictors including serum creatinine, depression and socioeconomic status index (Index of Relative Socio-economic Advantage and Disadvantage, IRSAD) into models' equation, and the changes in discrimination were evaluated.

RESULTS

Among 15 618 adults (mean age 75 (4.4) years), 520 men and 498 women experienced CVD events over a median follow-up of 6.3 (IQR: 5.2-7.7) years. Following updating, the discrimination power of models increased for both sexes (C statistics ranged 0.62-0.64 for men and 0.68-0.69 for women). Updated models indicated good calibration, with an added net benefit at the risk thresholds ranging from 4%-10% for women to 5%-12% for men. Incorporating IRSAD, depression and serum creatinine did not improve CVD risk discrimination of updated models.

CONCLUSIONS

Updating models, by adjusting model coefficients to better reflect the characteristics and risk factors of older adults, improves CVD risk prediction in a large cohort of relatively healthy Caucasian population aged 70+. Further external validation in diverse older populations including those with frailty and multimorbidity is recommended before clinical implementation.

Influence of intelligent management mode based on Internet of Things on self-management ability and prognosis of elderly patients with hypertensive heart disease: An observational study

Hypertensive heart disease was difficult to cure with drugs, and most patients had poor compliance, leading to recurrent disease and poor quality of life. The intelligent management mode based on the Internet of Things avoided the excessive dependence of the elderly patients on medical institutions in the traditional medical model and enabled patients to monitor themselves. This study aimed to explore the impact on self-management ability and prognosis of elderly patients with hypertensive heart disease. A total of 150 elderly patients with hypertensive heart disease who received treatment from April 2020 to April 2022 were selected and divided into control group (n = 75 cases) and observation group (n = 75 cases) by random number table method. The control group was given routine intervention, and the observation group was given intelligent management mode based on the Internet of Things. Blood pressure fluctuation, self-management ability, and prognosis of the 2 groups were compared after intervention. After the intervention of the intelligent management mode based on the Internet of Things, the systolic and diastolic blood pressure levels in the observation group were lower than those in the control group (P < .05). After intervention, the scores of self-management ability in diet control, self-care skills, rehabilitation exercise, and self-monitoring in observation group were higher than those in control group (P < .05). After intervention, the total incidence of chest tightness, dyspnea, arrhythmia, edema, and nausea in the observation group was 5 (6.67%), which was significantly lower than that in the control group 12 (16.00%) (P < .05). The application of intelligent management mode based on the Internet of Things could effectively improve patients' blood pressure level, improve patients' self-management ability, and significantly improve the prognosis, which was worthy of popularization and application.

A Decisive Metaheuristic Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risk Assessment

Advanced predictive analytics coupled with an effective attribute selection method plays a pivotal role in the precise assessment of chronic disorder risks in patients. Traditional attribute selection approaches suffer from premature convergence, high complexity, and computational cost. On the contrary, heuristic-based optimization to supervised methods minimizes the computational cost by eliminating outlier attributes. In this study, a novel buffer-enabled heuristic, a memory-based metaheuristic attribute selection (MMAS) model, is proposed, which performs a local neighborhood search for optimizing chronic disorders data. It is further filtered with unsupervised K-means clustering to remove outliers. The resultant data are input to the Naive Bayes classifier to determine chronic disease risks' presence. Heart disease, breast cancer, diabetes, and hepatitis are the datasets used in the research. Upon implementation of the model, a mean accuracy of 94.5% using MMAS was recorded and it dropped to 93.5% if clustering was not used. The average precision, recall, and F-score metric computed were 96.05%, 94.07%, and 95.06%, respectively. The model also has a least latency of 0.8 sec. Thus, it is demonstrated that chronic disease diagnosis can be significantly improved by heuristic-based attribute selection coupled with clustering followed by classification. It can be used to develop a decision support system to assist medical experts in the effective analysis of chronic diseases in a cost-effective manner.

All-cause mortality and cardiovascular events in a Spanish nonagenarian cohort according to type 2 diabetes mellitus status and established cardiovascular disease

Background

Despite the progressive aging of the population in industrialized countries, few studies have focused on the natural history of cardiovascular disease in the very old, and recommendations on prevention of cardiovascular disease in this population are lacking. We aimed to analyze all-cause mortality and cardiovascular events according to prevalent type 2 diabetes mellitus and established cardiovascular disease in nonagenarians from a Mediterranean population.

Methods

We analyzed the primary health records of all nonagenarians living in the Community of Madrid (N = 59,423) and collected data for 4 groups: Group 1, individuals without T2DM or established CVD (T2DM-, CVD-); Group 2, individuals without T2DM but with established CVD (T2DM-, CVD +); Group 3, individuals with T2DM but without established CVD (T2DM + , CVD-); and Group 4, individuals with both T2DM and established CVD (T2DM + , CVD +), taking into account the influence of sex on the outcomes. Follow-up was 2.5 years. The primary outcomes were cumulative incidence and incidence density rates for all-cause mortality, non-fatal myocardial infarction, non-fatal stroke (the first composite primary outcome [CPO1]), combined with heart failure (CPO2). We evaluated the adjusted effect of each group on all-cause mortality (Cox regression).

Results

Mean age was 93.3 ± 2.8 years (74.2% women). Hypertension, dyslipidemia, heart failure, albuminuria, and estimated glomerular filtration rate < 60 mL/min/1.73 m2 were significantly more prevalent in G4 than in the other groups (all p values < 0.001). We observed significantly higher cumulative incidence rates for all-cause mortality, CPO1, and CPO2 in participants belonging to G4 (all p values ≤ 0.001). People in G2 presented higher rates of all-cause mortality, heart failure, CPO1, and CPO2 than people in G3 (all p values ≤ 0.001). In the fully adjusted model, G4 independently predicted all-cause mortality (HR = 1.48 [95% CI, 1.40 to 1.57] vs reference G1 [p < 0.01]). In addition, significant HRs were recorded for cardiovascular disease alone (G2) and type 2 diabetes mellitus alone (G3) (1.13 and 1.14, respectively; both p values < 0.01).

Conclusions

In Spanish nonagenarians, established cardiovascular disease and type 2 diabetes mellitus conferred a modest risk of all-cause mortality. However, the simultaneous presence of both conditions conferred the highest risk of all-cause mortality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-02893-z.

Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier

There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity and the quality of daily life. The treatment of these disorders is a challenging task for medical professionals. Dimensionality reduction techniques make it possible to handle big data samples, providing decision support in relation to chronic diseases. These datasets contain a series of symptoms that are used in disease prediction. The presence of redundant and irrelevant symptoms in the datasets should be identified and removed using feature selection techniques to improve classification accuracy. Therefore, the main contribution of this paper is a comparative analysis of the impact of wrapper and filter selection methods on classification performance. The filter methods that have been considered include the Correlation Feature Selection (CFS) method, the Information Gain (IG) method and the Chi-Square (CS) method. The wrapper methods that have been considered include the Best First Search (BFS) method, the Linear Forward Selection (LFS) method and the Greedy Step Wise Search (GSS) method. A Decision Tree algorithm has been used as a classifier for this analysis and is implemented through the WEKA tool. An attribute significance analysis has been performed on the diabetes, breast cancer and heart disease datasets used in the study. It was observed that the CFS method outperformed other filter methods concerning the accuracy rate and execution time. The accuracy rate using the CFS method on the datasets for heart disease, diabetes, breast cancer was 93.8%, 89.5% and 96.8% respectively. Moreover, latency delays of 1.08 s, 1.02 s and 1.01 s were noted using the same method for the respective datasets. Among wrapper methods, BFS’ performance was impressive in comparison to other methods. Maximum accuracy of 94.7%, 95.8% and 96.8% were achieved on the datasets for heart disease, diabetes and breast cancer respectively. Latency delays of 1.42 s, 1.44 s and 132 s were recorded using the same method for the respective datasets. On the basis of the obtained result, a new hybrid Attribute Evaluator method has been proposed which effectively integrates enhanced K-Means clustering with the CFS filter method and the BFS wrapper method. Furthermore, the hybrid method was evaluated with an improved decision tree classifier. The improved decision tree classifier combined clustering with classification. It was validated on 14 different chronic disease datasets and its performance was recorded. A very optimal and consistent classification performance was observed. The mean values for accuracy, specificity, sensitivity and f-score metrics were 96.7%, 96.5%, 95.6% and 96.2% respectively.

Prediction models for cardiovascular disease risk in the hypertensive population: a systematic review

OBJECTIVE

The aim of this study was to identify, describe, and evaluate the available cardiovascular disease risk prediction models developed or validated in the hypertensive population.

METHODS

MEDLINE and the Web of Science were searched from database inception to March 2019, and all reference lists of included articles were reviewed.

RESULTS

A total of 4766 references were screened, of which 18 articles were included in the review, presenting 17 prediction models specifically developed for hypertensive populations and 25 external validations. Among the 17 prediction models, most were constructed based on randomized trials in Europe or North America to predict the risk of fatal or nonfatal cardiovascular events. The most common predictors were classic cardiovascular risk factors such as age, diabetes, sex, smoking, and SBP. Of the 17 models, only one model was externally validated. Among the 25 external validations, C-statistics ranged from 0.58 to 0.83, 0.56 to 0.75, and 0.64 to 0.78 for models developed in the hypertensive population, the general population and other specific populations, respectively. Most of the development studies and validation studies had an overall high risk of bias according to PROBAST.

CONCLUSION

There are a certain number of cardiovascular risk prediction models in patients with hypertension. The risk of bias assessment showed several shortcomings in the methodological quality and reporting in both the development and validation studies. Most models developed in the hypertensive population have not been externally validated. Compared with models developed for the general population and other specific populations, models developed for the hypertensive population do not display a better performance when validated among patients with hypertension. Research is needed to validate and improve the existing cardiovascular disease risk prediction models in hypertensive populations rather than developing completely new models.

Predictive value of traditional risk factors for cardiovascular disease in older people: A systematic review

With increasing age, associations between traditional risk factors (TRFs) and cardiovascular disease (CVD) shift. It is unknown which mid-life risk factors remain relevant predictors for CVD in older people. We systematically searched PubMed and EMBASE on August 16th 2019 for studies assessing predictive ability of >1 of fourteen TRFs for fatal and non-fatal CVD, in the general population aged 60+. We included 12 studies, comprising 11 unique cohorts. TRF were evaluated in 2 to 11 cohorts, and retained in 0-70% of the cohorts: age (70%), diabetes (64%), male sex (57%), systolic blood pressure (SBP) (50%), smoking (36%), high-density lipoprotein cholesterol (HDL) (33%), left ventricular hypertrophy (LVH) (33%), total cholesterol (22%), diastolic blood pressure (20%), antihypertensive medication use (AHM) (20%), body mass index (BMI) (0%), hypertension (0%), low-density lipoprotein cholesterol (0%). In studies with low to moderate risk of bias, systolic blood pressure (SBP) (80%), smoking (80%) and HDL cholesterol (60%) were more often retained. Model performance was moderate with C-statistics ranging from 0.61 to 0.77. Compared to middle-aged adults, in people aged 60+ different risk factors predict CVD and current prediction models perform only moderate at best. According to most studies, age, sex and diabetes seem valuable predictors of CVD in old-age. SBP, HDL cholesterol and smoking may also have predictive value. Other blood pressure and cholesterol related variables, BMI, and LVH seem of very limited or no additional value. Without competing risk analysis, predictors are overestimated.

A cardiovascular risk prediction model for older people: Development and validation in a primary care population

Cardiovascular risk prediction is mainly based on traditional risk factors that have been validated in middle‐aged populations. However, associations between these risk factors and cardiovascular disease (CVD) attenuate with increasing age. Therefore, for older people the authors developed and internally validated risk prediction models for fatal and non‐fatal CVD, (re)evaluated the predictive value of traditional and new factors, and assessed the impact of competing risks of non‐cardiovascular death. Post hoc analyses of 1811 persons aged 70‐78 year and free from CVD at baseline from the preDIVA study (Prevention of Dementia by Intensive Vascular care, 2006‐2015), a primary care‐based trial that included persons free from dementia and conditions likely to hinder successful long‐term follow‐up, were performed. In 2017‐2018, Cox‐regression analyses were performed for a model including seven traditional risk factors only, and a model to assess incremental predictive ability of the traditional and eleven new factors. Analyses were repeated accounting for competing risk of death, using Fine‐Gray models. During an average of 6.2 years of follow‐up, 277 CVD events occurred. Age, sex, smoking, and type 2 diabetes mellitus were traditional predictors for CVD, whereas total cholesterol, HDL‐cholesterol, and systolic blood pressure (SBP) were not. Of the eleven new factors, polypharmacy and apathy symptoms were predictors. Discrimination was moderate (concordance statistic 0.65). Accounting for competing risks resulted in slightly smaller predicted absolute risks. In conclusion, we found, SBP, HDL, and total cholesterol no longer predict CVD in older adults, whereas polypharmacy and apathy symptoms are two new relevant predictors. Building on the selected risk factors in this study may improve CVD prediction in older adults and facilitate targeting preventive interventions to those at high risk.

Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis

BACKGROUND

The Framingham risk models and pooled cohort equations (PCE) are widely used and advocated in guidelines for predicting 10-year risk of developing coronary heart disease (CHD) and cardiovascular disease (CVD) in the general population. Over the past few decades, these models have been extensively validated within different populations, which provided mounting evidence that local tailoring is often necessary to obtain accurate predictions. The objective is to systematically review and summarize the predictive performance of three widely advocated cardiovascular risk prediction models (Framingham Wilson 1998, Framingham ATP III 2002 and PCE 2013) in men and women separately, to assess the generalizability of performance across different subgroups and geographical regions, and to determine sources of heterogeneity in the findings across studies.

METHODS

A search was performed in October 2017 to identify studies investigating the predictive performance of the aforementioned models. Studies were included if they externally validated one or more of the original models in the general population for the same outcome as the original model. We assessed risk of bias for each validation and extracted data on population characteristics and model performance. Performance estimates (observed versus expected (OE) ratio and c-statistic) were summarized using a random effects models and sources of heterogeneity were explored with meta-regression.

RESULTS

The search identified 1585 studies, of which 38 were included, describing a total of 112 external validations. Results indicate that, on average, all models overestimate the 10-year risk of CHD and CVD (pooled OE ratio ranged from 0.58 (95% CI 0.43-0.73; Wilson men) to 0.79 (95% CI 0.60-0.97; ATP III women)). Overestimation was most pronounced for high-risk individuals and European populations. Further, discriminative performance was better in women for all models. There was considerable heterogeneity in the c-statistic between studies, likely due to differences in population characteristics.

CONCLUSIONS

The Framingham Wilson, ATP III and PCE discriminate comparably well but all overestimate the risk of developing CVD, especially in higher risk populations. Because the extent of miscalibration substantially varied across settings, we highly recommend that researchers further explore reasons for overprediction and that the models be updated for specific populations.

Assessment of the clinical utility of adding common single nucleotide polymorphism genetic scores to classical risk factor algorithms in coronary heart disease risk prediction in UK men

Background:

Risk prediction algorithms for coronary heart disease (CHD) are recommended for clinical use. However, their predictive ability remains modest and the inclusion of genetic risk may improve their performance.

Methods:

QRISK2 was used to assess CHD risk using conventional risk factors (CRFs). The performance of a 19 single nucleotide polymorphism (SNP) gene score (GS) for CHD including variants identified by genome-wide association study and candidate gene studies (weighted using the results from the CARDIoGRAMplusC4D meta-analysis) was assessed using the second Northwick Park Heart Study (NPHSII) of 2775 healthy UK men (284 cases). To improve the GS, five SNPs with weak evidence of an association with CHD were removed and replaced with seven robustly associated SNPs – giving a 21-SNP GS.

Results:

The weighted 19 SNP GS was associated with lipid traits (p<0.05) and CHD after adjustment for CRFs, (OR=1.31 per standard deviation, p=0.03). Addition of the 19 SNP GS to QRISK2 showed improved discrimination (area under the receiver operator characteristic curve 0.68 vs. 0.70 p=0.02), a positive net reclassification index (0.07, p=0.04) compared to QRISK2 alone and maintained good calibration (p=0.17). The 21-SNP GS was also associated with CHD after adjustment for CRFs (OR=1.39 per standard deviation, 1.42×10

Conclusions:

The 19-SNP GS is robustly associated with CHD and showed potential clinical utility in the UK population.

Vascular risk assessment in older adults without a history of cardiovascular disease

Modern cardiovascular risk prediction tools, which have their genesis in the Framingham Heart Study, have allowed more accurate risk stratification and targeting of treatments worldwide over the last seven decades. Better cardiovascular risk factor control during this time has led to a reduction in cardiovascular mortality and, at least in part, to improved life expectancy. As a result, western societies as a whole have seen a steady increase in the proportion of older persons in their populations. Unfortunately, several studies have shown that the same tools which have contributed to this increase cannot be reliably extrapolated for use in older generations. Recent work has allowed recalibration of existing models for use in older populations but these modified tools still require external validation before they can be confidently applied in clinical practice. Another complication is emerging evidence that aggressive risk factor modification in older adults, particularly more frail individuals, may actually be harmful. This review looks at currently available cardiovascular risk prediction models and the specific challenges faced with their use in older adults, followed by analysis of recent attempts at recalibration for this cohort. We discuss the issue of frailty, looking at our evolving understanding of its constituent features and various tools for its assessment. We also review work to date on the impact of frailty on cardiovascular risk modification and outline its potentially central role in determining the most sensible approach in older patients. We summarise the most promising novel markers of cardiovascular risk which may be of use in improving risk prediction in older adults in the future. These include markers of vascular compliance (such as aortic pulse wave velocity and pulse wave analysis), of endothelial function (such as flow mediated dilation, carotid intima-media thickness and coronary artery calcium scores), and also biochemical and circulating cellular markers.

Lipid-modifying therapy in the elderly

Cardiovascular disease (CVD) mortality and morbidity increases with increasing age, largely as a result of increased lifetime exposure as well as increased prevalence of CVD risk factors. Hospitalization for CVD increases by a factor of over 18× for those aged 85+ years versus those aged <30 years. In spite of this, life expectancy continues to increase, and in Australia for people reaching the age of 65 years, it is now 84 years in men and 87 years in women. The number of people for whom lipid management is potentially indicated therefore increases with aging. This is especially the case for secondary prevention and for people aged 65-75 years for whom there is also evidence of benefit from primary prevention. Many people in this age group are not treated with lipid-lowering drugs, however. Even those with CVD may be suboptimally treated, with one study showing treatment rates to fall from ~60% in those aged <50 years to <15% for those aged 85+ years. Treatment of the most elderly patient groups remains controversial partly from the lack of randomized trial intervention data and partly from the potential for adverse effects of lipid therapy. There are many complex issues involved in the decision to introduce effective lipid-lowering therapy and, unfortunately, in many instances there is not adequate data to make evidence-based decisions regarding management. This review summarizes the current state of knowledge of the management of lipid disorders in the elderly and proposes guidelines for management.

Prediction of cardiovascular and all-cause mortality at 10 years in the hypertensive aged population

BACKGROUND

We have previously developed a score for predicting cardiovascular events in the intermediate term in an elderly hypertensive population. In this study, we aimed to extend this work to predict 10-year cardiovascular and all-cause mortality in the hypertensive aged population.

METHODS

Ten-year follow-up data of 5,378 hypertensive participants in the Second Australian National Blood Pressure study who were aged 65-84 years at baseline (1995-2001) and without prior cardiovascular events were analyzed. By using bootstrap resampling variable selection methods and comparing the Akaike and Bayesian information criterion and C-indices of the potential models, optimal and parsimonious multivariable Cox proportional hazards models were developed to predict 10-year cardiovascular and all-cause mortality. The models were validated using bootstrap validation method internally and using the Dubbo Study dataset externally.

RESULTS

The final model for cardiovascular mortality included detrimental (age, smoking, diabetes, waist-hip ratio, and disadvantaged socioeconomic status) and protective factors (female sex, alcohol consumption, and physical activity). The final model for all-cause mortality also included detrimental (age, smoking, random blood glucose, and disadvantaged socioeconomic status) and protective factors (female sex, alcohol consumption, body mass index, and statin use). Blood pressure did not appear in either model in this patient group. The C-statistics for internal validation were 0.707 (cardiovascular mortality) and 0.678 (all-cause mortality), and for external validation were 0.729 (cardiovascular mortality) and 0.772 (all-cause mortality).

CONCLUSIONS

These algorithms allow reliable estimation of 10-year risk of cardiovascular and all-cause mortality for hypertensive aged individuals.

Independent external validation of cardiovascular disease mortality in women utilising Framingham and SCORE risk models: a mortality follow-up study

BACKGROUND

We conducted an independent external validation of three cardiovascular risk score models (Framingham risk score model and SCORE risk charts developed for low-risk regions and high-risk regions in Europe) on a prospective cohort of 4487 Australian women with no previous history of heart disease, diabetes or stroke. External validation is an important step to evaluate the performance of risk score models using discrimination and calibration measures to ensure their applicability beyond the settings in which they were developed.

METHODS

Ten year mortality follow-up of 4487 Australian adult women from the National Heart Foundation third Risk Factor Prevalence Study with no baseline history of heart disease, diabetes or stroke. The 10-year risk of cardiovascular mortality was calculated using the Framingham and SCORE models and the predictive accuracy of the three risk score models were assessed using both discrimination and calibration.

RESULTS

The discriminative ability of the Framingham and SCORE models were good (area under the curve > 0.85). Although all models overestimated the number of cardiovascular deaths by greater than 15%, the Hosmer-Lemeshow test indicated that the Framingham and SCORE-Low models were calibrated and hence suitable for predicting the 10-year cardiovascular mortality risk in this Australian population. An assessment of the treatment thresholds for each of the three models in identifying participants recommended for treatment were found to be inadequate, with low sensitivity and high specificity resulting from the high recommended thresholds. Lower treatment thresholds of 8.7% for the Framingham model, 0.8% for the SCORE-Low model and 1.3% for the SCORE-High model were identified for each model using the Youden index, at greater than 78% sensitivity and 80% specificity.

CONCLUSIONS

Framingham risk score model and SCORE risk chart for low-risk regions are recommended for use in the Australian women population for predicting the 10-year cardiovascular mortality risk. These models demonstrate good discrimination and calibration performance. Lower treatment thresholds are proposed for better identification of individuals for treatment.

Assessment of cardiovascular disease risk in South asian populations

Although South Asian populations have high cardiovascular disease (CVD) burden in the world, their patterns of individual CVD risk factors have not been fully studied. None of the available algorithms/scores to assess CVD risk have originated from these populations. To explore the relevance of CVD risk scores for these populations, literature search and qualitative synthesis of available evidence were performed. South Asians usually have higher levels of both “classical” and nontraditional CVD risk factors and experience these at a younger age. There are marked variations in risk profiles between South Asian populations. More than 100 risk algorithms are currently available, with varying risk factors. However, no available algorithm has included all important risk factors that underlie CVD in these populations. The future challenge is either to appropriately calibrate current risk algorithms or ideally to develop new risk algorithms that include variables that provide an accurate estimate of CVD risk.

Birth cohort differences in cardiovascular risk factors in a Brazilian population of older elderly: the Bambuí Cohort Study of Aging (1997 and 2008)

The aim of this study was to investigate whether cohort differences exist in the prevalence of cardiovascular risk factors among older elderly from the Bambuí Cohort Study of Aging. Participants were those aged 71-81 years at two points in time a decade apart: 457 in 1997 (earlier cohort) and 553 in 2008 (recent cohort). The prevalence of hypertension (PR = 1.27; 95%CI: 1.19-1.36) and of diabetes mellitus (PR = 1.39; 95%CI: 1.06-1.83) was higher in the recent cohort compared to the earlier one, regardless of sex. The recent cohort had a lower prevalence of smoking (PR = 0.58; 95%CI: 0.42-0.80), and lower total cholesterol/HDL cholesterol ratio level (PR = 0.85; 95%CI: 0.80-0.89). There was a 136% increase in the pharmacologic treatment of diabetes and a 56% increase in pharmacologic management of hypertension in 2008 in comparison with 1997. Overall, the number of cardiovascular risk factors in the recent cohort remained similar to that of the early cohort.

A score for the prediction of cardiovascular events in the hypertensive aged

BACKGROUND

With few exceptions, tools used to estimate cardiovascular disease (CVD) risk in those without prior events are based mainly on data from middle-aged subjects. Given the ever increasing number of older people, many with hypertension, a risk score relevant to this group is warranted. Our aim was to develop a cardiovascular risk equation suitable for risk prediction in elderly, hypertensive populations.

METHODS

We utilized cardiovascular end point data from 4.1 years median follow-up in 5,426 hypertensive subjects without previous CVD from the Second Australian National Blood Pressure Study (ANBP2). Our risk model, based on Cox regression, was developed using 75% of subjects without evident CVD (n = 4,072), randomly selected and stratified by age and gender, and internally validated using the remaining 25%. The model was also externally validated against the Dubbo Study dataset.

RESULTS

The final model included sex, age, physical activity in the 2 weeks prior to entry into study, family history, use of anticoagulants, centrally acting antihypertensive agents or diabetes medication, and an interaction term for sex and diabetes medication. The C-statistic was 0.65 (0.62-0.67) for our predictive model on the model development dataset and 0.62 (0.57-0.67) on the internal validation dataset. The Dubbo Data C-statistic for CVD was 0.68 (95% CI 0.65-0.71).

CONCLUSIONS

All models performed similarly. Because of greater ease of implementation, we recommend that existing algorithms be extended into older age groups.

The new Sheffield risk and benefit tables for the elderly

Several charts or tables are used to guide treatment in primary prevention of cardiovascular disease (CVD). These usually relate to patients up to 75 years of age, leaving older patients without guidance. Most also present this information as risk, leaving patients to estimate the benefit of treatment and decide whether it is worthwhile. We present tables to display both CVD risk and benefit from treatment in the elderly. A systematic review identified CVD risk functions for the elderly. The Dubbo study of older patients' 5-year CVD risk equation was deemed most appropriate, due to the population studied, endpoints observed and risk factors recorded. By dichotomizing most risk factors, we produced a new risk table in the form of the original 'Sheffield table'. Risk is calculated by selecting the appropriate table for gender and the appropriate cell from the rows and columns, representing age and risk factor contributors, respectively. Total cholesterol above a cell value corresponds to a 20 or 40% 10-year CVD risk. A simple risk scoring system was then derived from the Dubbo equation. Calculation of risk score requires knowledge of a patient's simple demographics, systolic blood pressure and total and high-density lipoprotein cholesterol. Positive integers corresponding to level of risk for each contributing factor are then added together to give a final risk score. A Markov chain model was produced based on the Dubbo derived risk and relative risk reductions from published meta-analyses of 3-hydroxy-3-methyl-glutaryl-CoA reductase inhibitors (statins) and anti-hypertensive treatment. Using this model, individual scores were mapped to likely benefit from treatment in terms of disease free years. Our risk table provides a simple means for calculating risk in the elderly, to two major thresholds, while the benefit table explores the concept of presenting benefit of taking CVD-preventing medication.

Recalibration and validation of the SCORE risk chart in the Australian population: the AusSCORE chart

BACKGROUND

Development of a validated risk prediction model for future cardiovascular disease (CVD) in Australians is a high priority for cardiovascular health strategies.

DESIGN

Recalibration of the SCORE (Systematic COronary Risk Evaluation) risk chart based on Australian national mortality data and average major CVD risk factor levels.

METHODS

Australian national mortality data (2003-2005) were used to estimate 10-year cumulative CVD mortality rates for people aged 40-74 years. Average age-specific and sex-specific levels of systolic blood pressure, total cholesterol and prevalence of current smoking were generated from data obtained in eight Australian large-scale population-based surveys undertaken from the late 1980s. The SCORE risk chart was then recalibrated by applying hazard ratios for 10-year CVD mortality obtained in the SCORE project. Discrimination and calibration of the recalibrated model was evaluated and compared with that of the original SCORE and Framingham equations in the Blue Mountains Eye Study in Australia using Harrell's c and Hosmer-Lemeshow chi statistics, respectively.

RESULTS

An Australian risk prediction chart for CVD mortality was derived. Among 1998 Blue Mountains Eye Study participants aged 49-74 years with neither CVD nor diabetes at baseline, the Harrell's c statistics for the Australian risk prediction chart for CVD mortality were 0.76 (95% confidence interval: 0.69-0.84) and 0.71 (confidence interval: 0.62-0.80) in men and women, respectively. The corresponding Hosmer-Lemeshow chi statistics, the measure of calibration, were 2.32 (P = 0.68) and 7.43 (P = 0.11), which were superior to both the SCORE and Framingham equations.

CONCLUSION

This new tool provides a valid and reliable method to predict risk of CVD mortality in the general Australian population.

The Role of Traditional Cardiovascular Risk Factors Among Patients with Rheumatoid Arthritis

Objective

People with rheumatoid arthritis (RA) have an increased risk of cardiovascular disease (CVD) compared with the general population. We investigated the relative contribution of traditional cardiovascular risk factors to this elevated risk.

Methods

Fifty RA subjects and 150 age and sex matched controls attended a cardiovascular risk assessment clinic betweenMarch and July 2006. Traditional cardiovascular risk factors and the absolute risks of CVD (calculated from application of a Framingham risk equation) were compared between the 2 groups.

Results

Compared with the controls, RA subjects were more likely to smoke (p < 0.001), be physically inactive (p = 0.006), and have higher mean measurements of body mass index (p = 0.040) and waist circumference (p = 0.049). No significant differences were found in mean levels of plasma lipid or glucose, or in the prevalences of diabetes and hypertension. Overall, the mean absolute risk of CVD was higher in the RA group, even after excluding smokers (p = 0.036).

Conclusion

Smoking and physical inactivity are important risk factors in the management of cardiovascular risk among patients with RA. Subjects with RA seem to have higher absolute risks of CVD compared with controls, even independently of smoking. This highlights the importance of treating all modifiable risk factors in those with RA although, individually, few may be conspicuous.

How do the Australian guidelines for lipid-lowering drugs perform in practice? Cardiovascular disease risk in the AusDiab Study, 1999-2000

OBJECTIVE

To determine how well the current Pharmaceutical Benefits Scheme (PBS) eligibility criteria for subsidy of lipid-lowering drugs compare with current national guidelines for determining the population at high risk of developing cardiovascular disease (CVD).

DESIGN AND PARTICIPANTS

Analyses of the population-based, cross-sectional Australian Diabetes, Obesity and Lifestyle (AusDiab) study, conducted in 1999-2000. The 1991 Framingham risk prediction equation was used to compute 5-year risk of developing first-time CVD in 8286 participants aged 30-74 years with neither CVD nor diabetes. Based on the National Heart Foundation of Australia and Cardiac Society of Australia and New Zealand guidelines, people with either 5-year CVD risk > or = 15% or with 5-year CVD risk of 10%-< 15% and the metabolic syndrome were defined as having estimated high absolute CVD risk.

MAIN OUTCOME MEASURES

5-year CVD risk; estimated population with high CVD risk.

RESULTS

Among participants without prevalent CVD or diabetes, 7.9% of men and 1.5% of women had a 5-year CVD risk > or = 15%. Of the estimated residential Australian population in 2000 aged 30-74 years without CVD or diabetes, 717 000 people were considered to be at high absolute CVD risk. Among the high-risk AusDiab participants without CVD or diabetes, only 16.9% of men and 15.4% of women were being treated with lipid-lowering drugs. Of the 9.6% of participants free of CVD and diabetes who were untreated but eligible for subsidy under PBS criteria, only 27.4% had an estimated high absolute CVD risk.

CONCLUSION

Strategies for CVD prevention using lipid-lowering medications can be improved by adoption of the absolute-risk approach.

Cardiovascular risks of calcium supplements in women

Increased risk of myocardial infarction outweighs the reduction in fractures

Prediction of first coronary events with the Framingham score: a systematic review

BACKGROUND

Uncertainty exists about the performance of the Framingham risk score when applied in different populations.

OBJECTIVE

We assessed calibration of the Framingham risk score (ie, relationship between predicted and observed coronary event rates) in US and non-US populations free of cardiovascular disease.

METHODS

We reviewed studies that evaluated the performance of the Framingham risk score to predict first coronary events in a validation cohort, as identified by Medline, EMBASE, BIOSIS, and Cochrane library searches (through August 2005). Two reviewers independently assessed 1496 studies for eligibility, extracted data, and performed quality assessment using predefined forms.

RESULTS

We included 25 validation cohorts of different population groups (n = 128,000) in our main analysis. Calibration varied over a wide range from under- to overprediction of absolute risk by factors of 0.57 to 2.7. Risk prediction for 7 cohorts (n = 18658) from the United States, Australia, and New Zealand was well calibrated (corresponding figures: 0.87-1.08; for the 5 biggest cohorts). The estimated population risks for first coronary events were strongly associated (goodness of fit: R2 = 0.84) and in good agreement with observed risks (coefficient for predicted risk: beta = 0.84; 95% CI 0.41-1.26). In 18 European cohorts (n = 109499), the corresponding figures indicated close association (R2 = 0.72) but substantial overprediction (beta = 0.58, 95% CI 0.39-0.77). The risk score was well calibrated on the intercept for both population clusters.

CONCLUSION

The Framingham score is well calibrated to predict first coronary events in populations from the United States, Australia, and New Zealand. Overestimation of absolute risk in European cohorts requires recalibration procedures.

Cardiovascular risk prediction tools for populations in Asia

BACKGROUND

Cardiovascular risk equations are traditionally derived from the Framingham Study. The accuracy of this approach in Asian populations, where resources for risk factor measurement may be limited, is unclear.

OBJECTIVE

To compare "low-information" equations (derived using only age, systolic blood pressure, total cholesterol and smoking status) derived from the Framingham Study with those derived from the Asian cohorts, on the accuracy of cardiovascular risk prediction.

DESIGN

Separate equations to predict the 8-year risk of a cardiovascular event were derived from Asian and Framingham cohorts. The performance of these equations, and a subsequently "recalibrated" Framingham equation, were evaluated among participants from independent Chinese cohorts.

SETTING

Six cohort studies from Japan, Korea and Singapore (Asian cohorts); six cohort studies from China; the Framingham Study from the US.

PARTICIPANTS

172,077 participants from the Asian cohorts; 25,682 participants from Chinese cohorts and 6053 participants from the Framingham Study.

MAIN RESULTS

In the Chinese cohorts, 542 cardiovascular events occurred during 8 years of follow-up. Both the Asian cohorts and the Framingham equations discriminated cardiovascular risk well in the Chinese cohorts; the area under the receiver-operator characteristic curve was at least 0.75 for men and women. However, the Framingham risk equation systematically overestimated risk in the Chinese cohorts by an average of 276% among men and 102% among women. The corresponding average overestimation using the Asian cohorts equation was 11% and 10%, respectively. Recalibrating the Framingham risk equation using cardiovascular disease incidence from the non-Chinese Asian cohorts led to an overestimation of risk by an average of 4% in women and underestimation of risk by an average of 2% in men.

INTERPRETATION

A low-information Framingham cardiovascular risk prediction tool, which, when recalibrated with contemporary data, is likely to estimate future cardiovascular risk with similar accuracy in Asian populations as tools developed from data on local cohorts.

Risk scoring in the assessment of cardiovascular risk

PURPOSE OF REVIEW

Cardiovascular risk scoring is incorporated in guidelines and recommended for targeting preventive treatment. Evidence is required on the most appropriate method, its accuracy in a given population, and its effectiveness in favourably influencing clinical behaviour and health outcomes.

RECENT FINDINGS

Recent risk scores address inaccuracies that arise when methods are transferred between populations, and specific methods and recalibrations are described for use in low-risk populations. Ethnic and social differences in risk are also recognized in the context of cardiovascular risk scoring. More sensitive measures of known risk factors and numerous emerging risk factors are reported and new statistical methods and sources of data suggested. Little emphasis has been placed on evaluation of the clinical effectiveness of cardiovascular risk scores. Education in cardiovascular risk assessment may help improve uptake of methods by healthcare professionals.

SUMMARY

Numerous risk scoring methods are available to the healthcare professional but use is patchy. Accuracy varies between populations and methods have been developed to compensate for some of this variability. If risk scoring methods are to be widely used in general practice, evidence is required on both the accuracy of methods in appropriate populations and their effectiveness in improving health outcomes.

Quality of diabetes care and coronary heart disease absolute risk in patients with type 2 diabetes mellitus in Australian general practice

OBJECTIVE

To examine the quality of diabetes care and prevention of cardiovascular disease (CVD) in Australian general practice patients with type 2 diabetes and to investigate its relationship with coronary heart disease absolute risk (CHDAR).

METHODS

A total of 3286 patient records were extracted from registers of patients with type 2 diabetes held by 16 divisions of general practice (250 practices) across Australia for the year 2002. CHDAR was estimated using the United Kingdom Prospective Diabetes Study algorithm with higher CHDAR set at a 10 year risk of >15%. Multivariate multilevel logistic regression investigated the association between CHDAR and diabetes care.

RESULTS

47.9% of diabetic patient records had glycosylated haemoglobin (HbA1c) >7%, 87.6% had total cholesterol >or=4.0 mmol/l, and 73.8% had blood pressure (BP) >or=130/85 mm Hg. 57.6% of patients were at a higher CHDAR, 76.8% of whom were not on lipid modifying medication and 66.2% were not on antihypertensive medication. After adjusting for clustering at the general practice level and age, lipid modifying medication was negatively related to CHDAR (odds ratio (OR) 0.84) and total cholesterol. Antihypertensive medication was positively related to systolic BP but negatively related to CHDAR (OR 0.88). Referral to ophthalmologists/optometrists and attendance at other health professionals were not related to CHDAR.

CONCLUSIONS

At the time of the study the diabetes and CVD preventive care in Australian general practice was suboptimal, even after a number of national initiatives. The Australian Pharmaceutical Benefits Scheme (PBS) guidelines need to be modified to improve CVD preventive care in patients with type 2 diabetes.

Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review

OBJECTIVE

To determine the accuracy of assessing cardiovascular disease (CVD) risk in the primary prevention of CVD and its impact on clinical outcomes.

DESIGN

Systematic review.

DATA SOURCES

Published studies retrieved from Medline and other databases. Reference lists of identified articles were inspected for further relevant articles.

SELECTION OF STUDIES

Any study that compared the predicted risk of coronary heart disease (CHD) or CVD, with observed 10-year risk based on the widely recommended Framingham methods (review A). Randomised controlled trials examining the effect on clinical outcomes of a healthcare professional assigning a cardiovascular risk score to people predominantly without CVD (review B).

REVIEW METHODS

Data were extracted on the ratio of the predicted to the observed 10-year risk of CVD and CHD (review A), and on cardiovascular or coronary fatal or non-fatal events, risk factor levels, absolute cardiovascular or coronary risk, prescription of risk-reducing drugs and changes in health-related behaviour (review B).

RESULTS

27 studies with data from 71,727 participants on predicted and observed risk for either CHD or CVD were identified. For CHD, the predicted to observed ratios ranged from an underprediction of 0.43 (95% CI 0.27 to 0.67) in a high-risk population to an overprediction of 2.87 (95% CI 1.91 to 4.31) in a lower-risk population. In review B, four randomised controlled trials confined to people with hypertension or diabetes found no strong evidence that a cardiovascular risk assessment performed by a clinician improves health outcomes.

CONCLUSION

The performance of the Framingham risk scores varies considerably between populations and evidence supporting the use of cardiovascular risk scores for primary prevention is scarce.