Cost-effectiveness of the non-laboratory based Framingham algorithm in primary prevention of cardiovascular disease: A simulated analysis of a cohort of African American adults

Health Economic Research Assessing the Value of Early Detection of Cardiovascular Disease: A Systematic Review

INTRODUCTION

Cardiovascular disease (CVD) is the most prominent cause of death worldwide and has a major impact on healthcare budgets. While early detection strategies may reduce the overall CVD burden through earlier treatment, it is unclear which strategies are (most) efficient.

AIM

This systematic review reports on the cost effectiveness of recent early detection strategies for CVD in adult populations at risk.

METHODS

PubMed and Scopus were searched to identify scientific articles published between January 2016 and May 2022. The first reviewer screened all articles, a second reviewer independently assessed a random 10% sample of the articles for validation. Discrepancies were solved through discussion, involving a third reviewer if necessary. All costs were converted to 2021 euros. Reporting quality of all studies was assessed using the CHEERS 2022 checklist.

RESULTS

In total, 49 out of 5552 articles were included for data extraction and assessment of reporting quality, reporting on 48 unique early detection strategies. Early detection of atrial fibrillation in asymptomatic patients was most frequently studied (n = 15) followed by abdominal aortic aneurysm (n = 8), hypertension (n = 7) and predicted 10-year CVD risk (n = 5). Overall, 43 strategies (87.8%) were reported as cost effective and 11 (22.5%) CVD-related strategies reported cost reductions. Reporting quality ranged between 25 and 86%.

CONCLUSIONS

Current evidence suggests that early CVD detection strategies are predominantly cost effective and may reduce CVD-related costs compared with no early detection. However, the lack of standardisation complicates the comparison of cost-effectiveness outcomes between studies. Real-world cost effectiveness of early CVD detection strategies will depend on the target country and local context.

REGISTRATION OF SYSTEMATIC REVIEW

CRD42022321585 in International Prospective Registry of Ongoing Systematic Reviews (PROSPERO) submitted at 10 May 2022.

A comparative assessment between Globorisk and WHO cardiovascular disease risk scores: a population-based study

The Globorisk and WHO cardiovascular risk prediction models are country-specific and region-specific, respectively. The goal of this study was to assess the agreement and correlation between the WHO and Globorisk 10-year cardiovascular disease risk prediction models. The baseline data of 6796 individuals aged 40-74 years who participated in the Fasa cohort study without a history of cardiovascular disease or stroke at baseline were included. In the WHO and Globorisk models scores were calculated using age, sex, systolic blood pressure (SBP), current smoking, diabetes, and total cholesterol for laboratory-based risk and age, sex, SBP, current smoking, and body mass index (BMI) for non-laboratory-based risk (office-based or BMI-based). In Globorisk and WHO risk agreement across risk categories (low, moderate, and high) was examined using the kappa statistic. Also, Pearson correlation coefficients and scatter plots were used to assess the correlation between Globorisk and WHO models. Bland-Altman plots were presented for determination agreement between Globorisk and WHO risk scores in individual's level. In laboratory-based models, agreement across categories was substantial in the overall population (kappa values: 0.75) and also for females (kappa values: 0.74) and males (kappa values: 0.76), when evaluated separately. In non-laboratory-based models, agreement across categories was substantial for the whole population (kappa values: 0.78), and almost perfect for among males (kappa values: 0.82) and substantial for females (kappa values: 0.73). The results showed a very strong positive correlation (r ≥ 0.95) between WHO and Globorisk laboratory-based scores for the whole population, males, and females and also a very strong positive correlation (r > 0.95) between WHO and Globorisk non-laboratory-based scores for the whole population, males, and females. In the laboratory-based models, the limit of agreements was better in males (95%CI 2.1 to - 4.2%) than females (95%CI 4.3 to - 7.3%). Also, in the non-laboratory-based models, the limit of agreements was better in males (95%CI 2.9 to - 4.0%) than females (95%CI 3.2 to - 6.1%). There was a good agreement between both the laboratory-based and the non-laboratory-based WHO models and the Globorisk models. The correlation between two models was very strongly positive. However, in the Globorisk models, more people were in high-risk group than in the WHO models. The scatter plots and Bland-Altman plots showed systematic differences between the two scores that vary according to the level of risk. So, for these models may be necessary to modify the cut points of risk groups. The validity of these models must be determined for this population.

Comparison of laboratory-based and non-laboratory-based WHO cardiovascular disease risk charts: a population-based study

Background

Determining the risk of Cardiovascular Disease (CVD) is a necessity for timely preventive interventions in high-risk groups. However, laboratory testing may be impractical in countries with limited resources. This study aimed at comparison and assessment of the agreement between laboratory-based and non-laboratory-based WHO risk charts models.

Methods

This study was performed using the baseline data of 8138 participants in the pars cohort study who had no history of CVD and stroke. The updated 2019 WHO model was used to determine the 10-year fatal and non-fatal CVD risks. In general, there are two types of new WHO risk prediction models for CVD. The scores were determined based on age, sex, smoking status, diabetes, Systolic Blood Pressure (SBP), and total cholesterol for the laboratory-based model and age, sex, smoking status, SBP, and Body Mass Index (BMI) for the non-laboratory-based model. The agreement of these two models was determined via kappa statistics for the classified risk (low: < 10%, moderate: 10–< 20%, high: ≥ 20%). Correlation coefficients (r) and scatter plots was used for correlation between scores.

Results

The results revealed very strong correlation coefficients for all sex and age groups (r = 0.84 for males < 60 years old, 0.93 for males ≥ 60 years old, 0.85 for females < 60 years old, and 0.88 for females ≥ 60 years old). In the laboratory-based model, low, moderate, and high risks were 76.10%, 18.17%, and 5.73%, respectively. These measures were respectively obtained as 77.00%, 18.08%, and 4.92% in the non-laboratory-based model. Based on risk classification, the agreement was substantial for males < 60 years old and for both males and females aged ≥ 60 years (kappa values: 0.79 for males < 60 years old, 0.65 for males ≥ 60 years old, and 0.66 for females ≥ 60 years old) and moderate for females < 60 years old (kappa = 0.46).

Conclusions

The non-laboratory-based risk prediction model, which is simple, inexpensive, and non-invasive, classifies individuals almost identically to the laboratory-based model. Therefore, in countries with limited resources, these two models can be used interchangeably.

Agreement between laboratory-based and non-laboratory-based Framingham risk score in Southern Iran

The Framingham 10-year cardiovascular disease risk is measured by laboratory-based and non-laboratory-based models. This study aimed to determine the agreement between these two models in a large population in Southern Iran. In this study, the baseline data of 8138 individuals participated in the Pars cohort study were used. The participants had no history of cardiovascular disease or stroke. For the laboratory-based risk model, scores were determined based on age, sex, current smoking, diabetes, systolic blood pressure (SBP) and treatment status, total cholesterol, and High-Density Lipoprotein. For the non-laboratory-based risk model, scores were determined based on age, sex, current smoking, diabetes, SBP and treatment status, and Body Mass Index. The agreement between these two models was determined by Bland Altman plots for agreement between the scores and kappa statistic for agreement across the risk groups. Bland Altman plots showed that the limits of agreement were reasonable for females < 60 years old (95% CI: −2.27–4.61%), but of concern for those ≥ 60 years old (95% CI: −3.45–9.67%), males < 60 years old (95% CI: −2.05–8.91%), and males ≥ 60 years old (95% CI: −3.01–15.23%). The limits of agreement were wider for males ≥ 60 years old in comparison to other age groups. According to the risk groups, the agreement was better in females than in males, which was moderate for females < 60 years old (kappa = 0.57) and those ≥ 60 years old (kappa = 0.51). The agreement was fair for the males < 60 years old (kappa = 0.39) and slight for those ≥ 60 years old (Kappa = 0.14). The results showed that in overall participants, the agreement between the two risk scores was moderate according to risk grouping. Therefore, our results suggest that the non-laboratory-based risk model can be used in resource-limited settings where individuals cannot afford laboratory tests and extensive laboratories are not available.

Health determinants and risk factors for coronary artery disease among older Filipinos in rural communities

AIMS

Lifestyle modifications and healthy behavioural regimens are critical in preventing coronary artery disease (CAD) and other important health conditions. Little is known about the risk for CAD and health behaviour among older adults (>60 years) living in rural areas in the Philippines. Compare risk profiles and health behaviours of Filipinos at low- vs. moderate-to-high-risk for CAD and examine the association between demographic variables, risk profiles, and health behaviours.

METHODS AND RESULTS

A comparative, cross-sectional study was conducted using a convenient sample of 427 Filipinos (≥60 years old). Data on sociodemographic characteristics, risk profiles, and health behaviours (e.g. diet, physical activity, smoking status, and alcohol use) were collected. Ten-year CAD risk was estimated using the non-laboratory-based Framingham algorithm. Of the 427 participants [mean age was 69.2 ± 6.7 years, primarily women (65%), married (52.8%)], 319 (75%) were at low risk, and 108 (25%) were at moderate-to-high-risk for CAD. Filipinos at moderate to high risk were more likely to have cardiometabolic diseases (e.g. hypertension, hyperlipidaemia, diabetes, and obesity, all P's < 0.001). Health behaviours did not differ between the two groups except for the consumption of ≥5 servings of fruit, higher in the low-risk group.

CONCLUSION

Data showed highly consistent and convergent evidence among older Filipinos living in rural areas at high risk for CAD and other health conditions. These findings underscore the need for culturally sensitive guidance to improve CAD outcomes for moderate to high-risk older adults living in rural areas, including education and counselling on risk and risk-reducing strategies.

Do the 2017 blood pressure cut-offs improve 10-year cardiovascular disease mortality risk prediction?

BACKGROUND AND AIMS

High blood pressure (BP) is a significant predictor for cardiovascular disease (CVD) mortality. The 2017 American College of Cardiology/American Heart Association guideline reclassified the BP categories; however, its impact on CVD mortality prediction is still unclear. Our study aimed to examine whether the application of new BP cut-offs could improve 10-year CVD mortality prediction among US adults.

METHODS AND RESULTS

This population-based cohort study linked data from the US National Health and Nutrition Examination Survey (1988-1994 and 1999-2004) and National Death Index (up to December 31, 2015). We constructed original and modified, using new BP cut-offs, Systematic COronary Risk Evaluation models to predict 10-year CVD mortality. We measured model discrimination and calibration using the Harrell's C statistic and calibration plots, respectively. We calculated the net reclassification index to evaluate the reclassification. In addition, we compared the sensitivity, specificity, predictive values (PVs), and likelihood ratios (LRs). Among 28,964 adults (aged ≥ 20 years), 1493 have died of CVD within ten years of follow-up. The modified models had improvements in calibration and reclassification instead of discrimination compared to the original models. The modified models have higher sensitivity and negative PV; however, they have lower specificity, positive PV, positive LR, and negative LR.

CONCLUSIONS

The modified models failed to improve the discrimination of 10-year CVD mortality. However, they could increase the calibration and reclassification and capture more participants with high CVD risk. More studies are needed on the potential use of the new BP cut-offs in the CVD primary prevention.