Related Experiment Video
Updated: Feb 1, 2026
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Brazilian Analytical Decision Model for Cardiovascular Disease: An Adaptation of the Scottish Cardiovascular Disease
Bruno Salgado Riveros1, Walleri Christini Torelli Reis2, Rosa Camila Lucchetta2
1Laboratory of Clinical Services and Health Evidences, Pharmaceutical Sciences, Federal University of Parana, Curitiba, Parana, Brazil; Health Economics and Health Technology Assessment, Institute of Health and Technology Assessment, University of Glasgow, Glasgow, UK.
Insights
A new model assesses cardiovascular disease (CVD) prevention intervention efficiency in Brazil. This calibrated tool accurately predicts risks for individuals aged 35-80, aiding public health strategies.
Area of Science:
- Public Health
- Epidemiology
- Health Economics
Background:
- Cardiovascular disease (CVD) poses a significant public health challenge in Brazil.
- Existing analytical tools for assessing CVD prevention intervention efficiency are lacking.
- A Scottish CVD Policy Model was adapted for the Brazilian population.
Purpose of the Study:
- To adapt and validate a decision-analytic model for assessing the efficiency of primary cardiovascular disease (CVD) prevention interventions in Brazil.
- To ensure the model's predictions align with Brazilian epidemiological data.
Main Methods:
- Calibration of a CVD Policy Model using Brazilian life-table and cohort data.
- Adjustment of linear predictors with multiplicative factors to match observed data.
- Root-Mean-Square-Error (RMSE) used to evaluate model fit for life expectancy (LE), coronary heart disease (CHD), cerebrovascular disease (CBVD), and fatal CVD.
Main Results:
- Initial model underestimated LE and cumulative incidence of CHD/CBVD for women.
- Calibration successfully adjusted predictions to meet acceptance criteria (RMSE < 1 year for LE, < 1% for incidence).
- The calibrated model accurately predicts outcomes for individuals aged 35-80 with various risk factors.
Conclusions:
- This study presents the first decision-analytic model for evaluating primary CVD prevention intervention efficiency in Brazil.
- The calibrated model demonstrates reliability for predicting CVD risks in the target population.
- External validation is recommended for future research to further confirm model robustness.
Introduction:
Despite the significant impact of cardiovascular disease (CVD), there is not yet an analytical decision tool for assessing efficiency of interventions to prevent primary CVD events in Brazil. Therefore, we sought to adapt a Scottish CVD Policy Model to be used in the proposed population.
Methods:
Calibration consisted of identifying multiplicative factors for linear predictors of existing survival analysis models to produce predictions that closely match observed data (Life-table and Brazilian cohort study). Target data were life expectancy (LE) and cumulative incidence of coronary heart disease (CHD), cerebrovascular disease (CBVD), fatal CVD and fatal non-CVD. Root-Mean-Square-Error (RMSE) was used to estimate differences between predictions and observations. Acceptance criteria were defined as a fit of less than one year for LE and 1% for cumulative incidence. Male and female models were built separately.
Results:
The original model underestimated LE (RMSE=2.85 for men and 1.91 for women), CHD and CBVD for women (RMSE=0.044 and 0.041, respectively). The calibration process identified multiplicative factors to reach acceptance criteria for the four target data mentioned above (RMSE=0.61, 0.21, 0.016 and 0.017, respectively). Over prediction was identified only for CHD events in men (RMSE=0.031) being further calibrated (RMSE=0.008). All other target data met the acceptance criteria. Overall, the calibrated model predicts properly to individuals aging 35-80 years old, diabetics or not, smokers or not, with or without family history of CVD, and presenting at least one of the risk factors uncontrolled: Systolic Blood Pressure, Total Cholesterol or HDL-Cholesterol.
Discussion:
This is the first decision analytic model capable of assessing efficiency of interventions that prevent primary CVD events in Brazil. In future research, independent external validation should be carried out to corroborate the reliability of the model outputs.
More Related Videos
Related Concept Videos
01:27Psychoneuroimmunology: Cardiovascular Disease
A key area of focus in PNI is the relationship between stress and coronary...
01:14Overview of the Cardiovascular System
Heart
The heart is the central pump of the cardiovascular system that circulates blood throughout the body. It comprises two atria receiving the blood and two ventricles pumping blood out of the heart. Their rhythmic contractions, called heartbeats, ensure that blood flow remains continuous.
Blood Vessels
Blood...
01:27Regulation of the Cardiovascular System
The regulation of the cardiovascular system involves the autonomic nervous system (ANS), baroreceptors, and chemoreceptors, ensuring that heart rate and blood pressure are appropriately modulated in response to varying physiological demands.
The ANS comprises two main divisions: the sympathetic and parasympathetic nervous systems. The sympathetic nervous system enhances...
01:20Exercise and Cardiovascular Response
Light to moderate physical activity initiates a series of interconnected responses in the body. The heart rate modestly increases in anticipation of the workout, followed by widespread vasodilation as oxygen consumption by skeletal muscles increases. This results in decreased peripheral resistance, increased capillary blood flow, and accelerated...
01:29Assessment of the Cardiovascular System II: Inspection
Head and Neck
01:25Assessment of the Cardiovascular System IV: Auscultation
Normal Heart Sounds
S1 (First Heart Sound)-
S1 is made by the closure of the mitral and tricuspid valves (atrioventricular valves), marking the beginning of systole.
S2 (Second Heart Sound)-
S2 is made by the closure of the aortic and pulmonic valves (semilunar valves), marking the end of the systole.