A method to construct a points system to predict cardiovascular disease considering repeated measures of risk factors

Area of Science:

• Cardiology
• Biostatistics
• Health Informatics

Background:

• Current cardiovascular disease (CVD) risk scores rely on static, baseline risk factors.
• These models overlook the dynamic nature of risk factor variability over time.
• Existing dynamic models are too complex for routine clinical practice.

Purpose of the Study:

• To develop a new methodological alternative for constructing cardiovascular risk scores.
• To enable predictions of CVD using repeated measures of risk factors.
• To retain the simplicity of traditional points systems for clinical utility.

Main Methods:

• Development of a novel risk score methodology.
• Statistical validation through simulation.
• Clinical application using simulated data for procedural understanding.

Main Results:

• Demonstrated the properties and viability of the new risk score construction method.
• Showcased a method that incorporates temporal risk factor variability.
• Validated the approach's potential for clinical utilization.

Conclusions:

• The proposed method offers a viable alternative for dynamic cardiovascular risk assessment.
• This approach balances predictive accuracy with clinical simplicity.
• It facilitates improved cardiovascular disease prediction by considering longitudinal risk factor data.