A simple risk score using routine data for predicting cardiovascular disease in primary care.

Area of Science:

• Cardiovascular epidemiology
• Preventive medicine
• Health informatics

Background:

• Population-based screening for cardiovascular disease (CVD) risk is increasingly proposed.
• Current screening often incorporates blood tests, which can be resource-intensive.

Purpose of the Study:

• To evaluate the effectiveness of a simple CVD risk identification method using routine data.
• To determine if electronic health records can identify high-risk individuals efficiently.

Main Methods:

• Prospective cohort study (EPIC-Norfolk) of 21,867 adults aged 40-74, free from CVD and diabetes.
• Evaluation of Framingham risk equations and the Cambridge diabetes risk score (using routine GP data).
• Assessed discrimination (aROC), calibration, sensitivity, specificity, and predictive values.

Main Results:

• During follow-up, 2,213 participants developed a first CVD event.
• The Cambridge diabetes risk score showed reasonable predictive ability (aROC 0.72).
• The Framingham risk score demonstrated better prediction (aROC 0.77) but overestimated CVD risk by 60% in this population.

Conclusions:

• Risk scores using routinely available general practice data perform well in predicting CVD events.
• Routine data screening is a potentially more efficient initial step for population CVD risk assessment.
• This approach may streamline identification of high-risk individuals before employing more resource-intensive tests.