Developing Non-Laboratory Cardiovascular Risk Assessment Charts and Validating Laboratory and Non-Laboratory-Based Models

Prediction of cardiovascular risk: validation of a non-laboratory and a laboratory-based score in a Brazilian community-based cohort of the PURE study

Background

Risk scores are essential tools for implementing cardiovascular disease (CVD) prevention. Validating risk scores considering regional diversities and disparities is critical for reducing the burden of CVD on global morbidity and mortality. We aimed to validate two cardiovascular risk scores (laboratory and non-laboratory-based) to predict major adverse cardiovascular events in the Brazilian cohort of the PURE study.

Methods

We validated two risk scores derived from the INTERHEART study, the non-laboratory INTERHEART risk score (NL-IHRS) and the laboratory fasting cholesterol INTERHEART risk score (FC-IHRS) using data from 4623 (urban areas) and 1415 (rural areas) participants without CVD in the Brazilian cohort of the PURE study enrolled in 2004 and 2005 and followed up to September 2021. The endpoint was major cardiovascular events (MACE), defined as the composite of myocardial infarction, stroke, heart failure, or death from cardiovascular causes. We evaluated the model performance of IHRS through c-statistic and calibration methods.

Findings

After a mean follow-up of 8.8 years (range, 0.28-15.1 years), there were 312 cardiovascular events, corresponding to an incidence rate of 0.58% per year (0.56% per year in urban versus 0.64% per year in rural areas). For the NL-IHRS, the c-statistic was 0.69 (95% confidence interval, CI, 0.66-0.72) in the overall cohort, 0.68 (95% CI, 0.64-0.72) in the urban cohort, and 0.72 (95% CI, 0.66-0.78) in the rural cohort. C-statistic values for the recalibrated FC-IHRS were 0.71 (95% CI, 0.67-0.74), 0.71 (95% CI, 0.67-0.75), and 0.70 (95% CI, 0.64-0.76) in the overall, urban, and rural cohorts, respectively.

Interpretation

In this Brazilian community-based prospective cohort, both NL-IHRS and FC-IHRS-based models performed with reasonable discriminative accuracy on the risk estimation of long-term risk of major CVD. A non-laboratory-based CVD risk score may be instrumental in Brazilian communities with limited access to medical resources.

Funding

Population Health Research Institute, Novartis Biociências S.A.

Discrimination and calibration performances of non-laboratory-based and laboratory-based cardiovascular risk predictions: a systematic review

BACKGROUND AND OBJECTIVE

This review compares non-laboratory-based and laboratory-based cardiovascular disease (CVD) risk prediction equations in populations targeted for primary prevention.

DESIGN

Systematic review.

METHODS

We searched five databases until 12 March 2024 and used prediction study risk of bias assessment tool to assess bias. Data on hazard ratios (HRs), discrimination (paired c-statistics) and calibration were extracted. Differences in c-statistics and HRs were analysed.

PROTOCOL

PROSPERO (CRD42021291936).

RESULTS

Nine studies (1 238 562 participants, 46 cohorts) identified six unique CVD risk equations. Laboratory predictors (eg, cholesterol and diabetes) had strong HRs, while body mass index in non-laboratory models showed limited effect. Median c-statistics were 0.74 for both models (IQR: lab 0.77-0.72; non-lab 0.76-0.70), with a median absolute difference of 0.01. Calibration measures between laboratory-based and non-laboratory-based equations were similar, although non-calibrated equations often overestimated risk.

CONCLUSION

The discrimination and calibration measures between laboratory-based and non-laboratory-based models show minimal differences, demonstrating the insensitivity of c-statistics and calibration metrics to the inclusion of additional predictors. However, in most reviewed studies, the HRs for these additional predictors were substantial, significantly altering predicted risk, particularly for individuals with higher or lower levels of these predictors compared with the average.

Patterns among factors associated with myocardial infarction: chi-squared automatic interaction detection tree and binary logit model

BACKGROUND

Although mortality from myocardial infarction (MI) has declined worldwide due to advancements in emergency medical care and evidence-based pharmacological treatments, MI remains a significant contributor to global cardiovascular morbidity. This study aims to examine the risk factors associated with individuals who have experienced an MI in Türkiye.

METHODS

Microdata obtained from the Türkiye Health Survey conducted by Turkish Statistical Institute in 2019 were used in this study. Binary logistic regression, Chi-Square, and CHAID analyses were conducted to identify the risk factors affecting MI.

RESULTS

The analysis identified several factors associated with an increased likelihood of MI, including hyperlipidemia, hypertension, diabetes, chronic disease status, male gender, older age, single marital status, lower education level, and unemployment. Marginal effects revealed that elevated hyperlipidemia levels increased the probability of MI by 4.6%, while the presence of hypertension, diabetes, or depression further heightened this risk. Additionally, individuals with chronic diseases lasting longer than six months were found to have a higher risk of MI. In contrast, factors such as being female, having higher education, being married, being employed, engaging in moderate physical activity, and moderate alcohol consumption were associated with a reduced risk of MI.

CONCLUSION

To prevent MI, emphasis should be placed on enhancing general education and health literacy. There should be a focus on increasing preventive public health education and practices to improve variables related to healthy lifestyle behaviours, such as diabetes, hypertension, and hyperlipidemia.

The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review

BACKGROUND

Supporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in providing feedback to patients, and visualization plays an important role in facilitating the interpretation and understanding of data and, thus, influencing patients' behavior. Visual analytics enable efficient analysis and understanding of large datasets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating CVD risk.

OBJECTIVE

This review aims to present the most-used visualization techniques to estimate CVD risk.

METHODS

In this scoping review, we followed the Joanna Briggs Institute PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search strategy involved searching databases, including PubMed, CINAHL Ultimate, MEDLINE, and Web of Science, and gray literature from Google Scholar. This review included English-language articles on digital health, mobile health, mobile apps, images, charts, and decision support systems for estimating CVD risk, as well as empirical studies, excluding irrelevant studies and commentaries, editorials, and systematic reviews.

RESULTS

We found 774 articles and screened them against the inclusion and exclusion criteria. The final scoping review included 17 studies that used different methodologies, including descriptive, quantitative, and population-based studies. Some prognostic models, such as the Framingham Risk Profile, World Health Organization and International Society of Hypertension risk prediction charts, Cardiovascular Risk Score, and a simplified Persian atherosclerotic CVD risk stratification, were simpler and did not require laboratory tests, whereas others, including the Joint British Societies recommendations on the prevention of CVD, Systematic Coronary Risk Evaluation, and Framingham-Registre Gironí del COR, were more complex and required laboratory testing-related results. The most frequently used prognostic risk factors were age, sex, and blood pressure (16/17, 94% of the studies); smoking status (14/17, 82%); diabetes status (11/17, 65%); family history (10/17, 59%); high-density lipoprotein and total cholesterol (9/17, 53%); and triglycerides and low-density lipoprotein cholesterol (6/17, 35%). The most frequently used visualization techniques in the studies were visual cues (10/17, 59%), followed by bar charts (5/17, 29%) and graphs (4/17, 24%).

CONCLUSIONS

On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk. Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health's effectiveness in improving CVD outcomes is limited.

First Iranian guidelines for the diagnosis, management, and treatment of hyperlipidemia in adults

This guideline is the first Iranian guideline developed for the diagnosis, management, and treatment of hyperlipidemia in adults. The members of the guideline developing group (GDG) selected 9 relevant clinical questions and provided recommendations or suggestions to answer them based on the latest scientific evidence. Recommendations include the low-density lipoprotein cholesterol (LDL-C) threshold for starting drug treatment in adults lacking comorbidities was determined to be over 190 mg/dL and the triglyceride (TG) threshold had to be >500 mg/dl. In addition to perform fasting lipid profile tests at the beginning and continuation of treatment, while it was suggested to perform cardiovascular diseases (CVDs) risk assessment using valid Iranian models. Some recommendations were also provided on lifestyle modification as the first therapeutic intervention. Statins were recommended as the first line of drug treatment to reduce LDL-C, and if its level was high despite the maximum allowed or maximum tolerated drug treatment, combined treatment with ezetimibe, proprotein convertase subtilisin/kexin type 9 inhibitors, or bile acid sequestrants was suggested. In adults with hypertriglyceridemia, pharmacotherapy with statin or fibrate was recommended. The target of drug therapy in adults with increased LDL-C without comorbidities and risk factors was considered an LDL-C level of <130 mg/dl, and in adults with increased TG without comorbidities and risk factors, TG levels of <200 mg/dl. In this guideline, specific recommendations and suggestions were provided for the subgroups of the general population, such as those with CVD, stroke, diabetes, chronic kidney disease, elderly, and women.

Ethnicity affects the risk factors of acute myocardial infarction and should be considered in educational programs

Acute Myocardial infarction is a non-communicable disease representing the leading cause of death in Saudi Arabia. Studying the ethnicity in its risk factors has been poorly investigated.