Comparison of atherosclerotic cardiovascular disease (ASCVD) and Framingham risk scores (FRS) in an Iranian population

Background

Framingham risk score (FRS) and Atherosclerotic Cardiovascular Disease risk score (ASCVDrs) are widely used tools developed based on the American population. This study aimed to compare the ASCVDrs and FRS in an Iranian population.

Method

The participants of the Fasa Adult Cohort Study and the patients of the cardiovascular database of Vali-Asr Hospital of Fasa, aged 40-80 years, were involved in the present cross-sectional study. After excluding non-eligible participants, the individuals with a history of myocardial infarction or admission to the cardiology ward due to heart failure were considered high-risk, and the others were considered low-risk. The discriminative ability of FRS and ASCVDrs was evaluated and compared using receiver operating characteristic curve analysis. The correlation and agreement of ASCVDrs and FRS were tested using Cohen Kappa and Spearman.

Results

Finally, 8983 individuals (mean age:53.9 ± 9.5 y, 49.2 % male), including 1827 high-risk participants, entered the study. ASCVDrs detected a greater portion of participants as high-risk in comparison with FRS (28.7 % vs. 15.7 %). ASVD (AUC:0.794) had a higher discriminative ability than FRS (AUC:0.746), and both showed better discrimination in women. Optimal cut-off points for both ASCVDrs (4.36 %) and FRS (9.05 %) were lower than the original ones and in men. Compared to FRS, ASCVDrs had a higher sensitivity (79.3 % vs. 71.6 %) and lower specificity (64.5 % vs. 65.1 %). FRS and ASCVDrs had a moderate agreement (kappa:0.593,p-value<0.001) and were significantly correlated (Spearman:0.772,p-value<0.001).

Conclusions

ASCVDrs had a more accurate prediction of cardiovascular events and identified a larger number of people as high-risk in the Iranian population.

Major Limitations of Cardiovascular Risk Scores

Background. Epidemiological studies conducted in extensive population cohorts have led to the creation of numerous cardiovascular risk predictor models. However, these tools have certain limitations that restrict its applicability. The aim behind the following work is to summarize today's best-known limitations of cardiovascular risk assessment models through presenting the critical analyses conducted in this area, with the intention of offering practitioners a comprehensive understanding of these restrictions. Critical analyses revealed that these scales exhibit numerous limitations that could impact their performance. Most of these models evaluate cardiovascular risk based on classic risk factors and other restrictions, thereby negatively affecting their sensitivity. Scientists have made significant advancements in improving cardiovascular risk models, tailoring them to accommodate a wide range of populations and devising scales for estimating cardiovascular risks that can account for all prevailing restrictions. Better understanding these limitations could improve the cardiovascular risk stratification.

Comparing dietary strategies to manage cardiovascular risk in primary care: a narrative review of systematic reviews

BACKGROUND

Nutrition care in general practice is crucial for cardiovascular disease (CVD) prevention and management, although comparison between dietary strategies is lacking.

AIM

To compare the best available (most recent, relevant, and high-quality) evidence for six dietary strategies that are effective for primary prevention/absolute risk reduction of CVD.

DESIGN AND SETTING

A pragmatic narrative review of systematic reviews of randomised trials focused on primary prevention of cardiovascular events.

METHOD

Studies about: 1) adults without a history of cardiovascular events; 2) target dietary strategies postulated to reduce CVD risk; and 3) direct cardiovascular or all-cause mortality outcomes were included. Six dietary strategies were examined: energy deficit, Mediterranean-like diet, sodium reduction (salt reduction and substitution), the Dietary Approaches to Stop Hypertension (DASH) diet, alcohol reduction, and fish/fish oil consumption. Reviews were selected based on quality, recency, and relevance. Quality and certainty of evidence was assessed using GRADE.

RESULTS

Twenty-five reviews met inclusion criteria; eight were selected as the highest quality, recent, and relevant. Three dietary strategies showed modest, significant reductions in cardiovascular events: energy deficit (relative risk reduction [RRR] 30%, 95% confidence interval [CI] = 13 to 43), Mediterranean-like diet (RRR 40%, 95% CI = 20 to 55), and salt substitution (RRR 30%, 95% CI = 7 to 48). Still, some caveats remain on the effectiveness of these dietary strategies. Salt reduction, DASH diet, and alcohol reduction showed small, significant reductions in blood pressure, but no reduction in cardiovascular events. Fish/fish oil consumption showed little or no effect; supplementation of fish oil alone showed small reductions in CVD events.

CONCLUSION

For primary prevention, energy deficit, Mediterranean-like diets, and sodium substitution have modest evidence for risk reduction of CVD events. Strategies incorporated into clinical nutrition care should ensure guidance is person centred and tailored to clinical circumstances.

Societal health and economic burden of cardiovascular diseases in the population with type 2 diabetes in Qatar. A 10-year forecasting model

AIMS

To predict the future health and economic burden of cardiovascular disease (CVD) in type 2 diabetes (T2D) in Qatar.

MATERIALS AND METHODS

A dynamic multistate model was designed to simulate the progression of fatal and non-fatal CVD events among people with T2D in Qatar aged 40-79 years. First CVD events [i.e. myocardial infarction (MI) and stroke] were calculated via the 2013 Pooled Cohort Equation, while recurrent CVD events were sourced from the REACH registry. Key model outcomes were fatal and non-fatal MI and stroke, years of life lived, quality-adjusted life years, total direct medical costs and total productivity loss costs. Utility and cost model inputs were drawn from published sources. The model adopted a Qatari societal perspective. Sensitivity analyses were performed to test the robustness of estimates.

RESULTS

Over 10 years among people with T2D, model estimates 108 195 [95% uncertainty interval (UI) 104 249-112 172] non-fatal MIs, 62 366 (95% UI 60 283-65 520) non-fatal strokes and 14 612 (95% UI 14 472-14 744) CVD deaths. The T2D population accrued 4 786 605 (95% UI 4 743 454, 4 858 705) total years of life lived and 3 781 833 (95% UI 3 724 718-3 830 669) total quality-adjusted life years. Direct costs accounted for 57.85% of the total costs, with a projection of QAR41.60 billion (US$11.40 billion) [95% UI 7.53-147.40 billion (US$2.06-40.38 billion)], while the total indirect costs were expected to exceed QAR30.31 billion (US$8.30 billion) [95% UI 1.07-162.60 billion (US$292.05 million-44.55 billion)].

CONCLUSIONS

The findings suggest a significant economic and health burden of CVD among people with T2D in Qatar and highlight the need for more enhanced preventive strategies targeting this population group.

Evidence supporting the choice of a new cardiovascular risk equation for Australia

This article reviews the risk equations recommended for use in international cardiovascular disease (CVD) primary prevention guidelines and assesses their suitability for use in Australia against a set of a priori defined selection criteria. The review and assessment were commissioned by the National Heart Foundation of Australia on behalf of the Australian Chronic Disease Prevention Alliance to inform recommendations on CVD risk estimation as part of the 2023 update of the Australian CVD risk assessment and management guidelines. Selected international risk equations were assessed against eight selection criteria: development using contemporary data; inclusion of established cardiovascular risk factors; inclusion of ethnicity and deprivation measures; prediction of a broad selection of fatal and non-fatal CVD outcomes; population representativeness; model performance; external validation in an Australian dataset; and the ability to be recalibrated or modified. Of the ten risk prediction equations reviewed, the New Zealand PREDICT equation met seven of the eight selection criteria, and met additional usability criteria aimed at assessing the ability to apply the risk equation in practice in Australia.

Effectiveness of China-PAR and Framingham risk score in assessment of 10-year cardiovascular disease risk in Chinese hypertensive patients

OBJECTIVES

Estimating the total risk of cardiovascular disease (CVD) using risk prediction models represents a huge improvement in identifying and treating each of the risk factors. The objective of this study was to evaluate the effectiveness of the China-PAR (Prediction of atherosclerotic CVD risk in China) and Framingham risk score (FRS) in predicting the 10-year risk of CVD in Chinese hypertensive patients. The results of the study can be used to design health promotion strategies.

STUDY DESIGN

A large cohort study was used to assess the validity of models by comparing model predictions with actual incidence rates.

METHODS

In total, 10,498 hypertensive patients aged 30-70 years in Jiangsu Province, China, participated in the baseline survey that took place between January and December 2010 and were followed up to May 2020. China-PAR and FRS were used to calculate the predicted 10-year risk of CVD. The 10-year observed incidence of new cardiovascular events was adjusted by the Kaplan-Meier method. The ratio of the predicted risk to the actual incidence was calculated to evaluate the effectiveness of the model. The discrimination Harrell's C statistics and calibration Chi-square value were used to evaluate the predictive reliability of the models.

RESULTS

Of the 10,498 participants, 4411 (42.02%) were male. During the mean follow-up of 8.30 ± 1.45 years, a total of 693 new cardiovascular events occurred. Both models overestimated the risk of morbidity to varying degrees, and the FRS overestimated to a greater extent. After adjustment for covariates, the results of Cox proportional hazards regression showed that the risk of CVD in the high-risk group was higher than in low-risk group. The degree of discrimination in both models was approximately 0.6, which showed that discrimination was not ideal in the models. In addition, Chi-square calibrations of the two models were <20 in males, which showed that calibration of the models was better for men than women.

CONCLUSIONS

The China-PAR and FRS models overestimated the risk of CVD for participants in this study. In addition, the degree of discrimination was not ideal, and both models performed better in males than in females in terms of calibration. The results of this study suggest that a more suitable risk prediction model should be established according to the characteristics of the hypertensive population in Jiangsu Province.

Projecting the Health and Economic Burden of Cardiovascular Disease Among People with Type 2 Diabetes, 2022-2031

OBJECTIVE

The aim was to project the health and economic outcomes of cardiovascular disease (CVD) among people with type 2 diabetes from Australian public healthcare and societal perspectives over the next decade.

METHODS

A dynamic multistate model with yearly cycles was developed to project cardiovascular events among Australians with type 2 diabetes aged 40-89 years from 2022 to 2031. CVD risk (myocardial infarction [MI] and stroke) in the type 2 diabetes population was estimated using the 2013 pooled cohort equation, and recurrent cardiovascular event rates in the type 2 diabetes with established CVD population were obtained from the global Reduction of Atherothrombosis for Continued Health (REACH) registry. Costs and utilities were derived from published sources. Outcomes included fatal and non-fatal MI and stroke, years of life lived, quality-adjusted life years (QALYs), total healthcare costs, and total productivity losses. The annual discount rate was 5%, applied to outcomes and costs.

RESULTS

Between 2022 and 2031, a total of 83,618 non-fatal MIs (95% uncertainty interval [UI] 83,170-84,053) and 58,774 non-fatal strokes (95% UI 58,458-59,013) were projected. Total years of life lived and QALYs (discounted) were projected to be 9,549,487 (95% UI 9,416,423-9,654,043) and 6,632,897 (95% UI 5,065,606-7,591,679), respectively. Total healthcare costs and total lost productivity costs (discounted) were projected to be 9.59 billion Australian dollars (AU$) (95% UI 1.90-30.45 billion) and AU$9.07 billion (95% UI 663.53 million-33.19 billion), respectively.

CONCLUSIONS

CVD in people with type 2 diabetes will substantially impact the Australian healthcare system and society over the next decade. Future work to investigate different strategies to optimize the control of risk factors for the prevention and treatment of CVD in type 2 diabetes in Australia is warranted.

Lost Therapeutic Benefit of Delayed Low-Density Lipoprotein Cholesterol Control in Statin-Treated Patients and Cost-Effectiveness Analysis of Lipid-Lowering Intensification

OBJECTIVES

Attainment of low-density lipoprotein cholesterol (LDL-C) therapeutic goals in statin-treated patients remains suboptimal. We quantified the health economic impact of delayed lipid-lowering intensification from an Australian healthcare and societal perspective.

METHODS

A lifetime Markov cohort model (n = 1000) estimating the impact on coronary heart disease (CHD) of intensifying lipid-lowering treatment in statin-treated patients with uncontrolled LDL-C, at moderate to high risk of CHD with no delay or after a 5-year delay, compared with standard of care (no intensification), starting at age 40 years. Intensification was tested with high-intensity statins or statins + ezetimibe. LDL-C levels were extracted from a primary care cohort. CHD risk was estimated using the pooled cohort equation. The effect of cumulative exposure to LDL-C on CHD risk was derived from Mendelian randomization data. Outcomes included CHD events, quality-adjusted life-years (QALYs), healthcare and productivity costs, and incremental cost-effectiveness ratios (ICERs). All outcomes were discounted annually by 5%.

RESULTS

Over the lifetime horizon, compared with standard of care, achieving LDL-C control with no delay with high-intensity statins prevented 29 CHD events and yielded 30 extra QALYs (ICERs AU$13 205/QALY) versus 22 CHD events and 16 QALYs (ICER AU$20 270/QALY) with a 5-year delay. For statins + ezetimibe, no delay prevented 53 CHD events and gave 45 extra QALYs (ICER AU$37 271/QALY) versus 40 CHD events and 29 QALYs (ICER of AU$44 218/QALY) after a 5-year delay.

CONCLUSIONS

Delaying attainment of LDL-C goals translates into lost therapeutic benefit and a waste of resources. Urgent policies are needed to improve LDL-C goal attainment in statin-treated patients.

Sex-Specific Survival Bias and Interaction Modeling in Coronary Artery Disease Risk Prediction

BACKGROUND

The 10-year Atherosclerotic Cardiovascular Disease risk score is the standard approach to predict risk of incident cardiovascular events, and recently, addition of coronary artery disease (CAD) polygenic scores has been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined. This study performed an extensive evaluation of age and sex effects in genetic CAD risk prediction.

METHODS

The population-based Norwegian HUNT2 (Trøndelag Health Study 2) cohort of 51 036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372 410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards, and Harrell concordance index, sensitivity, and specificity were compared.

RESULTS

Inclusion of age and sex interactions of CAD polygenic score to the prediction models increased the C-index and sensitivity by accounting for nonadditive effects of CAD polygenic score and likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. We identified a total of 82.6% of incident CAD cases by using a 2-step approach: (1) Atherosclerotic Cardiovascular Disease risk score (74.1%) and (2) the CAD polygenic score interaction model for those in low clinical risk (additional 8.5%).

CONCLUSIONS

These findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age- and sex-interaction terms with polygenic scores to optimize detection of individuals at high risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information.

An Assessment of the Effect of Bariatric Surgery on Cardiovascular Disease Risk in the Chinese Population Using Multiple Cardiovascular Risk Models

BACKGROUND

Many studies have reported that bariatric surgery may reduce postoperative cardiovascular risk in patient with obesity, but few have addressed this risk in the Chinese population.

OBJECTIVE

To assess the impact of bariatric surgery on cardiovascular disease (CVD) risk in the Chinese population using the World Health Organization (WHO) risk model, the Global risk model, and the Framingham Risk Score.

METHODS

We retrospectively analyzed data collected on patient with obesity who underwent bariatric surgery at our institution between March 2009 and January 2021. Their demographic characteristics, anthropometric variables, and glucolipid metabolic parameters were assessed preoperatively and at their 1-year postoperative follow-up. Subgroup analysis compared body mass index (BMI) < 35 kg/m² and BMI ≥ 35 kg/m², as well as gender. We used the 3 models to calculate their CVD risk.

RESULTS

We evaluated 61 patients, of whom 26 (42.62%) had undergone sleeve gastrectomy (SG) surgery and 35 (57.38%) Roux-en-Y gastric bypass (RYGB) surgery. Of the patients with BMI ≥ 35 kg/m², 66.67% underwent SG, while 72.97% with BMI < 35 kg/m² underwent RYGB. HDL levels were significantly higher at 12 months postoperatively relative to baseline. When the models were applied to calculate CVD risk in Chinese patients with obesity, the 1-year CVD risk after surgery were reduced lot compared with the preoperative period.

CONCLUSION

Patient with obesity had significantly lower CVD risks after bariatric surgery. This study also demonstrates that the models are reliable clinical tools for assessing the impact of bariatric surgery on CVD risk in the Chinese population.

Development and internal validation of the Edmonton Obesity Staging System-2 Risk screening Tool (EOSS-2 Risk Tool) for weight-related health complications: a case-control study in a representative sample of Australian adults with overweight and obesity

OBJECTIVE

Excess weight and related health complications remain under diagnosed and poorly treated in general practice. We aimed to develop and validate a brief screening tool for determining the presence of unknown clinically significant weight-related health complications for potential application in general practice.

DESIGN

We considered 14 self-reported candidate predictors of clinically significant weight-related health complications according to the Edmonton Obesity Staging System (EOSS score of ≥2) and developed models using multivariate logistic regression across training and test data sets. The final model was chosen based on the area under the receiver operating characteristic curve and the Hosmer-Lemeshow statistic; and validated using sensitivity, specificity and positive predictive value.

SETTING AND PARTICIPANTS

We analysed cross-sectional data from the Australian Health Survey 2011-2013 sample aged between 18 and 65 years (n=7518) with at least overweight and obesity.

RESULTS

An EOSS≥2 classification was present in 78% of the sample. Of 14 candidate risk factors, 6 (family history of diabetes, hypertension, high sugar in blood/urine, high cholesterol and self-reported bodily pain and disability) were automatically included based on definitional or obvious correlational criteria. Three variables were retained in the final multivariate model (age, self-assessed health and history of depression/anxiety). The EOSS-2 Risk Tool (index test) classified 89% of those at 'extremely high risk' (≥25 points), 67% of those at 'very high risk' (7-24 points) and 42% of those at 'high risk' (<7 points) of meeting diagnostic criteria for EOSS≥2 (reference).

CONCLUSION

The EOSS-2 Risk Tool is a simple, safe and accurate screening tool for diagnostic criteria for clinically significant weight-related complications for potential application in general practice. Research to determine the feasibility and applicability of the EOSS-2 Risk Tool for improving weight management approaches in general practice is warranted.

Development of machine learning prediction models to explore nutrients predictive of cardiovascular disease using Canadian linked population-based data

Machine learning may improve use of observational data to understand the nutritional epidemiology of cardiovascular disease (CVD) through better modelling of non-linearity, non-additivity, and dietary complexity. Our objective was to develop machine learning prediction models for exploring how nutrients are related to CVD risk and to evaluate their predictive performance. We established a population-based cohort from the Canadian Community Health Survey and measured CVD incidence and mortality from 2004 to 2018 using administrative databases of national hospital discharges and deaths. Predictors included 61 nutrition variables and fourteen socioeconomic, demographic, psychological, and behavioural variables. Conditional inference forest models were interpreted and evaluated by permutation feature importance, accumulated local effects, and predictive discrimination and calibration. A total of 12 130 individuals were included in the study. Use of supplements, caffeine, and alcohol were the most important nutrition variables for prediction of CVD. Supplement use was associated with decreased risk, caffeine was associated with increasing risk, and alcohol had a u-shaped association with risk. The model had an out-of-sample c-statistic of 0.821 (95% confidence interval = 0.801-0.842). Exploratory findings included both known and novel associations and predictive performance was competitive, suggesting that further application of machine learning to nutritional epidemiology may help elucidate risks and improve predictive models. Novelty: Machine learning prediction models were developed for CVD using dietary data. Models were interpreted with interpretable machine learning techniques, revealing diverse associations between diet and CVD. Models achieved comparable or superior predictive performance to existing CVD risk prediction models.

Absolute cardiovascular risk scores and medication use in rural India: a cross-sectional study

OBJECTIVES

We compared the performance of laboratory-based cardiovascular risk prediction tools in a low-income and middle-income country setting, and estimated the use of antihypertensive and lipid-lowering medications in those deemed at high risk of a cardiovascular event.

DESIGN

A cross-sectional study.

SETTING

The study population comprised adult residents (aged ≥18 years) of the Rishi Valley region located in Chittoor District, south-western Andhra Pradesh, India.

PARTICIPANTS

7935 participants were surveyed between 2012 and 2015. We computed the 10-year cardiovascular risk and undertook pair-to-pair analyses between various risk tools used to predict a fatal or non-fatal cardiovascular event (Framingham Risk Score (FRS), World Health Organization Risk Score (WHO-RS) and Australian Risk Score (ARS)), or a fatal cardiovascular event (Systematic COronary Risk Evaluation (SCORE-high and SCORE-low)). Concordance was assessed by ordinary least-products (OLP) regression (for risk score) and quadratic weighted kappa (κ_w, for risk category).

RESULTS

Of participants aged 35-74 years, 3.5% had prior cardiovascular disease. The relationships between risk scores were quasi-linear with good agreement between the FRS and ARS (OLP slope=0.96, κ_w=0.89). However, the WHO-RS underestimated cardiovascular risk compared with all other tools. Twenty per cent of participants had ≥20% risk of an event using the ARS; 5% greater than the FRS and nearly threefold greater than the WHO-RS. Similarly, 16% of participants had a risk score ≥5% using SCORE-high which was 6% greater than for SCORE-low. Overall, absolute cardiovascular risk increased with age and was greater in men than women. Only 9%-12% of those deemed 'high risk' were taking lipid-lowering or antihypertensive medication.

CONCLUSIONS

Cardiovascular risk prediction tools perform disparately in this setting of disadvantage. Few deemed at high risk were receiving the recommended treatment.

Projected New-Onset Cardiovascular Disease by Socioeconomic Group in Australia

BACKGROUND

Socioeconomic status has an important effect on cardiovascular disease (CVD). Data on the economic implications of CVD by socioeconomic status are needed to inform healthcare planning.

OBJECTIVES

The aim of this study was to project new-onset CVD and related health economic outcomes in Australia by socioeconomic status from 2021 to 2030.

METHODS

A dynamic population model was built to project annual new-onset CVD by socioeconomic quintile in Australians aged 40-79 years from 2021 to 2030. Cardiovascular risk was estimated using the Pooled Cohort Equation (PCE) from Australian-specific data, stratified for each socioeconomic quintile. The model projected years of life lived, quality- adjusted life-years (QALYs), acute healthcare medical costs, and productivity losses due to new-onset CVD. All outcomes were discounted by 5% annually.

RESULTS

PCE estimates showed that 8.4% of people in the most disadvantaged quintile were at high risk of CVD, compared with 3.7% in the least disadvantaged quintile (p < 0.001). From 2021 to 2030, the model projected 32% more cardiovascular events in the most disadvantaged quintile compared with the least disadvantaged (127,070 in SE 1 vs. 96,222 in SE 5). Acute healthcare costs in the most disadvantaged quintile were Australian dollars (AU$) 183 million higher than the least disadvantaged, and the difference in productivity costs was AU$959 million. Removing the equity gap (by applying the cardiovascular risk from the least disadvantaged quintile to the whole population) would prevent 114,822 cardiovascular events and save AU$704 million of healthcare costs and AU$3844 million of lost earnings over the next 10 years.

CONCLUSION

Our results highlight the pressing need to implement primary prevention interventions to reduce cardiovascular health inequity. This model provides a platform to incorporate socioeconomic status into health economic models by estimating which interventions are likely to yield more benefits in each socioeconomic quintile.

Social isolation, social support, loneliness and cardiovascular disease risk factors: A cross-sectional study among older adults

BACKGROUND

Social health reflects one's ability to form interpersonal relationships. Poor social health is a risk factor for cardiovascular disease (CVD), however an in-depth exploration of the link through CVD risk factors is lacking.

AIM

To examine the relationship between social health (social isolation, social support, loneliness) and CVD risk factors among healthy older women and men.

METHODS

Data were from 11,498 healthy community-dwelling Australians aged ≥70 years from the ASPirin in Reducing Events in the Elderly (ASPREE) trial and the ASPREE Longitudinal Study of Older Persons sub-study. Ten-year CVD risk was estimated using the Atherosclerotic CVD Risk Scale (ASCVDRS) and the Framingham Risk Score (FRS).

RESULTS

Physical inactivity and experiencing depressive symptoms were the only CVD risk factors that consistently differed by all three social health constructs. Loneliness was associated with greater ASCVDRS (women: β = 0.01, p < 0.05; men: β = 0.03, p < 0.001), social isolation with greater FRS (women: β = 0.02, p < 0.01; men: β = 0.03, p < 0.01) and the social health composite of being lonely (regardless of social isolation and/or social support status) with greater ASCVDRS (women: β = 0.01, p = 0.02; men: β = 0.03, p < 0.001). Among men, loneliness was also associated with greater FRS (β = 0.03, p < 0.001) and social support with greater ASCVDRS (β = 0.02, p = 0.01). Men were more socially isolated, less socially supported and less lonely than women.

CONCLUSION

Social isolation, social support and loneliness displayed diverse relationships with CVD risk factors and risk scores, emphasising the importance of distinguishing between these constructs. These findings inform on potential avenues to manage poor social health and CVD risk among older adults.

Examining the Potential for Coronary Artery Calcium (CAC) Scoring for Individuals at Low Cardiovascular Risk

Atherosclerosis is the commonest cause of death in Australia. Cardiovascular (CV) risk calculators have an important role in preventative cardiology, although they are are strongly age-dependent and designed to identify individuals at high risk of an imminent event. The imprecision around "intermediate" or "low" risk generates therapeutic uncertainty, and a significant proportion of patients presenting with myocardial infarction come from these groups, often with no warning. This highlights a conundrum: "Low" risk does not mean "no" risk. A fresh approach may be required to address the clinical conundrum around CV preventative approaches in non-high-risk individuals. While probabilistic calculators do not measure atherosclerosis, calculation of Coronary Artery Calcium (CAC) scores by low-dose computed tomography (CT) can provide a snapshot of atherosclerotic burden. In intermediate-risk individuals, CAC is well-established as an aid to CV risk prediction. Although CAC scoring in low-risk asymptomatic people may be considered controversial, CAC has emerged as the single best predictor of CV events in asymptomatic individuals, independent of traditional risk factor calculators. Therefore, apart from the contribution of age and sex, the somewhat arbitrary distinction between "intermediate" and "low" CV risk using probabilistic calculators may need to be reconsidered. A zero CAC score has a very low future event rate and non-zero CAC scores are associated with a progressive, graded increase in risk as the CAC score rises. In this review, we examine the evidence for CAC screening in low-risk individuals, and propose more widespread use of CAC using simple new model intended to enhance established CV risk prediction equations.

Risk Models and Scores of Cardiovascular Disease in Patients with Diabetes Mellitus

Diabetes mellitus (DM) is an established risk factor for atherosclerotic cardiovascular disease (CVD), and patients with DM are at a two to four-fold higher cardiovascular risk, including myocardial infraction, unstable angina, stroke, and heart failure. All of the above have arisen interest in CVD preventive strategies by the use of non-invasive methods, such as risk scores. The most common approach is to consider DM as a CVD equivalent and, therefore, to treat patients with DM in a similar way to those who required secondary CVD prevention. However, this approach has been disputed as all patients with DM do not have the same risk for CVD, and since other potentially important factors within the context of DM, such as DM duration, presence of albuminuria, and comorbidities, should be taken into consideration. Thus, the second and third approach is the application of risk models that were either developed initially for the general population or designed specifically for patients with DM, respectively. This review summarizes the evidence and implications for clinical practice regarding these scores. Up to date, several models that can be applied to the diabetic population have been proposed. However, only a few meet the minimum requirement of adequate external validation. In addition, moderate discrimination and poor calibration, which might lead to inaccurate risk estimations in populations with different characteristics, have been reported. Therefore, future research is needed before recommending a specific risk model for universal clinical practice in the management of diabetes.

Trends in the Utilization of Lipid-Lowering Medications in Australia: An Analysis of National Pharmacy Claims Data

Lipid-lowering medications comprise standard of care in the prevention of cardiovascular disease. This study examined the trends in the utilization of statin and non-statin medications in the Australian general population between 2013 and 2019. Pharmacoepidemiological analyses were performed using pharmacy dispensing data from Australian Pharmaceutical Benefits Scheme. One-year prevalence and incidence of statin and non-statin prescribing patterns were reported, and relative variations in prescribing examined via Poisson regression modelling. The one-year prevalence of statins' prescriptions decreased between 2013-2019 by 5.5% (from 25.0%-19.5%). Females were less likely than males to be prescribed statins (rate ratio [RR]=0.90, 95% confidence interval [CI] 0.89-0.91). The one-year prevalence of ezetimibe alone, and in combination with statins, increased consistently from 2013-2019 from 1.5%-3.6% (P<0.01) and 0.1%-1.1% (P<0.01), respectively. The prevalence was higher among those aged 61-80 years (RR=1.20, 95%CI 1.10-1.21) and those aged older than 80 years (RR=1.34, 95%CI 1.22-1.47), when compared to people aged <60 years. The incidence of ezetimibe prescriptions was highest in people aged 61-80 years (RR=1.36, 95%CI 1.31-1.41) compared to those aged <60 years. The one-year prevalence of proprotein convertase subtilisin/kexin type 9 inhibitor prescriptions was highest among those aged 46-60 years (RR=1.24, 95%CI 0.97-4.97) compared to people aged <46 and >60 years. Females were less likely than males to be prescribed a proprotein convertase subtilisin/kexin type 9 inhibitor (RR=0.87, 95%CI 0.75-0.98). Statins remain the most prevalent lipid-lowering medication prescribed in Australia. The prescribing of non-statin medications remains low, but is increasing.

Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning

Effective cardiovascular disease (CVD) prevention relies on timely identification and intervention for individuals at risk. Conventional formula-based techniques have been demonstrated to over- or under-predict the risk of CVD in the Australian population. This study assessed the ability of machine learning models to predict CVD mortality risk in the Australian population and compare performance with the well-established Framingham model. Data is drawn from three Australian cohort studies: the North West Adelaide Health Study (NWAHS), the Australian Diabetes, Obesity, and Lifestyle study, and the Melbourne Collaborative Cohort Study (MCCS). Four machine learning models for predicting 15-year CVD mortality risk were developed and compared to the 2008 Framingham model. Machine learning models performed significantly better compared to the Framingham model when applied to the three Australian cohorts. Machine learning based models improved prediction by 2.7% to 5.2% across three Australian cohorts. In an aggregated cohort, machine learning models improved prediction by up to 5.1% (area-under-curve (AUC) 0.852, 95% CI 0.837–0.867). Net reclassification improvement (NRI) was up to 26% with machine learning models. Machine learning based models also showed improved performance when stratified by sex and diabetes status. Results suggest a potential for improving CVD risk prediction in the Australian population using machine learning models.

Future burden of cardiovascular disease in Australia: impact on health and economic outcomes between 2020 and 2029

AIMS

To estimate the health and economic burden of new and established cardiovascular disease from 2020 to 2029 in Australia.

METHODS AND RESULTS

A two-stage multistate dynamic model was developed to predict the burden of the incident and prevalent cardiovascular disease, for Australians 40-90 years old from 2020 to 2029. The model captured morbidity, mortality, years of life lived, quality-adjusted life years, healthcare costs, and productivity losses. Cardiovascular risk for the primary prevention population was derived using Australian demographic data and the Pooled Cohort Equation. Risk for the secondary prevention population was derived from the REACH registry. Input data for costs and utilities were extracted from published sources. All outcomes were annually discounted by 5%. A number of sensitivity analyses were undertaken to test the robustness of the study. Between 2020 and 2029, the model estimates 377 754 fatal and 991 375 non-fatal cardiovascular events. By 2029, 1 061 756 Australians will have prevalent cardiovascular disease (CVD). The population accrued 8 815 271 [95% uncertainty interval (UI) 8 805 083-8 841 432] years of life lived with CVD and 5 876 975 (5 551 484-6 226 045) QALYs. The total healthcare costs of CVD were projected to exceed Australian dollars (AUD) 61.89 (61.79-88.66) billion, and productivity losses will account for AUD 78.75 (49.40-295.25) billion, driving the total cost to surpass AUD 140.65 (123.13-370.23) billion.

CONCLUSION

Cardiovascular disease in Australia has substantial impacts in terms of morbidity, mortality, and lost revenue to the healthcare system and the society. Our modelling provides important information for decision making in relation to the future burden of cardiovascular disease.

External validation and clinical usefulness of three commonly used cardiovascular risk prediction scores in an Emirati population: a retrospective longitudinal cohort study

OBJECTIVES

Cardiovascular disease (CVD) risk prediction models are useful tools for identifying those at high risk of cardiovascular events in a population. No studies have evaluated the performance of such risk models in an Arab population. Therefore, in this study, the accuracy and clinical usefulness of two commonly used Framingham-based risk models and the 2013 Pooled Cohort Risk Equation (PCE) were assessed in a United Arab Emirates (UAE) national population.

DESIGN

A 10-year retrospective cohort study.

SETTING

Outpatient clinics at a tertiary care hospital, Al-Ain, UAE.

PARTICIPANTS

The study cohort included 1041 UAE nationals aged 30-79 who had no history of CVD at baseline. Patients were followed until 31 December 2019. Eligible patients were grouped into the PCE and the Framingham validation cohorts.

EXPOSURE

The 10-year predicted risk for CVD for each patient was calculated using the 2008 Framingham risk model, the 2008 office-based Framingham risk model, and the 2013 PCE model.

PRIMARY OUTCOME MEASURE

The discrimination, calibration and clinical usefulness of the three models for predicting 10-year cardiovascular risk were assessed.

RESULTS

In women, the 2013 PCE model showed marginally better discrimination (C-statistic: 0.77) than the 2008 Framingham models (C-statistic: 0.74-0.75), whereas all three models showed moderate discrimination in men (C-statistic: 0.69‒0.70). All three models overestimated CVD risk in both men and women, with higher levels of predicted risk. The 2008 Framingham risk model (high-risk threshold of 20%) classified only 46% of women who subsequently developed incident CVD within 10 years as high risk. The 2013 PCE risk model (high-risk threshold of 7.5%) classified 74% of men who did not develop a cardiovascular event as high risk.

CONCLUSIONS

None of the three models is accurate for predicting cardiovascular risk in UAE nationals. The performance of the models could potentially be improved by recalibration.

Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound

RECENT FINDINGS

Cardiovascular disease (CVD) is the leading cause of mortality and poses challenges for healthcare providers globally. Risk-based approaches for the management of CVD are becoming popular for recommending treatment plans for asymptomatic individuals. Several conventional predictive CVD risk models based do not provide an accurate CVD risk assessment for patients with different baseline risk profiles. Artificial intelligence (AI) algorithms have changed the landscape of CVD risk assessment and demonstrated a better performance when compared against conventional models, mainly due to its ability to handle the input nonlinear variations. Further, it has the flexibility to add risk factors derived from medical imaging modalities that image the morphology of the plaque. The integration of noninvasive carotid ultrasound image-based phenotypes with conventional risk factors in the AI framework has further provided stronger power for CVD risk prediction, so-called "integrated predictive CVD risk models."

PURPOSE

of the review: The objective of this review is (i) to understand several aspects in the development of predictive CVD risk models, (ii) to explore current conventional predictive risk models and their successes and challenges, and (iii) to refine the search for predictive CVD risk models using noninvasive carotid ultrasound as an exemplar in the artificial intelligence-based framework.

CONCLUSION

Conventional predictive CVD risk models are suboptimal and could be improved. This review examines the potential to include more noninvasive image-based phenotypes in the CVD risk assessment using powerful AI-based strategies.

Performance of cardiovascular risk prediction equations in Indigenous Australians

OBJECTIVE

To assess the performance of cardiovascular disease (CVD) risk equations in Indigenous Australians.

METHODS

We conducted an individual participant meta-analysis using longitudinal data of 3618 Indigenous Australians (55% women) aged 30-74 years without CVD from population-based cohorts of the Cardiovascular Risk in IndigenouS People(CRISP) consortium. Predicted risk was calculated using: 1991 and 2008 Framingham Heart Study (FHS), the Pooled Cohorts (PC), GloboRisk and the Central Australian Rural Practitioners Association (CARPA) modification of the FHS equation. Calibration, discrimination and diagnostic accuracy were evaluated. Risks were calculated with and without the use of clinical criteria to identify high-risk individuals.

RESULTS

When applied without clinical criteria, all equations, except the CARPA-adjusted FHS, underestimated CVD risk (range of percentage difference between observed and predicted CVD risks: -55% to -14%), with underestimation greater in women (-63% to -13%) than men (-47% to -18%) and in younger age groups. Discrimination ranged from 0.66 to 0.72. The CARPA-adjusted FHS equation showed good calibration but overestimated risk in younger people, those without diabetes and those not at high clinical risk. When clinical criteria were used with risk equations, the CARPA-adjusted FHS algorithm scored 64% of those who had CVD events as high risk; corresponding figures for the 1991-FHS were 58% and were 87% for the PC equation for non-Hispanic whites. However, specificity fell.

CONCLUSION

The CARPA-adjusted FHS CVD risk equation and clinical criteria performed the best, achieving higher combined sensitivity and specificity than other equations. However, future research should investigate whether modifications to this algorithm combination might lead to improved risk prediction.

Estimated Cardiovascular Risk and Guideline-Concordant Primary Prevention With Statins: Retrospective Cross-Sectional Analyses of US Ambulatory Visits Using Competing Algorithms

INTRODUCTION

The 2013 pooled cohort equations (PCE) may misestimate cardiovascular event (CVE) risk, particularly for black patients. Alternatives to the original PCE (O-PCE) to assess potential statin benefit for primary prevention-a revised PCE (R-PCE) and US Preventive Services Task Force (USPSTF) algorithms-have not been compared in contemporary US patients in routine office-based practice.

METHODS

We performed retrospective, cross-sectional analysis of a nationally representative, US sample of office visits made from 2011 to 2014. Sampling criteria matched those used for PCE development: aged 40 to 79 years, black or white race, no cardiovascular disease. Original PCE, R-PCE, and USPSTF algorithms were applied to biometric and demographic data. Outcomes included estimated 10-year CVE risk, percentage exceeding each algorithm's statin-treatment threshold (>7.5% risk for O-PCE and R-PCE, and >10% O-PCE plus >1 risk factor for USPSTF), and percentage prescribed statin therapy.

RESULTS

In 12 556 visits (representing 285 330 123 nationwide), 10.8% of patients were black, 27.1% had diabetes, and 15.7% were current smokers. Replacing O-PCE with R-PCE decreased mean (95% confidence interval [CI]) estimated CVE risk from 12.4% (12.0%-12.7%) to 8.5% (8.2%-8.8%). Significant (P < 0.05) racial disparity in the rate of CVE risk >7.5% was identified using O-PCE (black and white patients [95% CI], respectively: 58.8% [54.6%-62.9%] vs 52.8% [51.1%-54.4%], P = .006) but not R-PCE (41.6% [37.6%-45.7%] vs 39.9% [38.3%-41.5%], P = .448). Revised PCE and USPSTF recommendations were concordant for 90% of patients. Significant racial disparity in guideline-concordant statin prescribing was found using O-PCE (black and white patients, respectively, 35.0% [30.5%-39.9%] vs 41.8% [39.9%-44.4%], P = .013), but not R-PCE (40.6% [35.0%-46.6%] vs 43.0% [40.0%-45.9%], P = .482) or USPSTF recommendations (39.0% [33.8%-44.5%] vs 44.4% [41.5%-47.5%], P = .073).

CONCLUSIONS

Use of an alternative to O-PCE may reduce racial disparity in estimated CVE risk and may facilitate shared decision-making about primary prevention.