Risk predictive modelling for diabetes and cardiovascular disease.

Area of Science:

• Medical Statistics
• Epidemiology
• Health Informatics

Background:

• Clinical prediction models are increasingly used for risk stratification in common conditions like cardiovascular disease (CVD) and diabetes mellitus.
• These models aim to guide prevention and treatment decisions in routine healthcare practice.

Purpose of the Study:

• To review the historical development and principles of prediction research, focusing on statistical underpinnings and implications for routine practice.
• To examine predictive modeling specifically for CVD and diabetes, highlighting the need for validation and impact assessment.

Main Methods:

• Review of historical development and statistical principles of prediction research.
• Focus on predictive modeling for CVD and diabetes, including factor identification, model derivation, internal and external validation, and updating procedures.
• Emphasis on the necessity of impact studies to assess the effect of validated models in routine practice.

Main Results:

• Predictive modeling for CVD risk has a longer history (5 decades) than for diabetes (∼20 years).
• Many prediction models exist, but few have undergone external validation or impact studies.
• Comparative performance of existing models remains largely unevaluated.

Conclusions:

• A shift in focus from developing new models to validating existing ones is needed for better adoption in routine practice.
• External validation and impact studies are critical steps before widespread dissemination of prediction models.
• Improving the validation and assessment of prediction models will enhance their utility in clinical decision-making for CVD and diabetes.