Identification of patients for clinical risk assessment by prediction of cardiovascular risk using default risk factor values

Background: To identify high risk patients without cardiovascular disease requires assessment of risk factors. Primary care providers must therefore determine which patients without cardiovascular disease should be highest priority for cardiovascular risk assessment. One approach is to prioritise patients for assessment using a prior estimate of their cardiovascular risk. This prior estimate of cardiovascular risk is derived from risk factor data that are routinely held in electronic medical records, with unknown blood pressure and cholesterol levels replaced by default values derived from national survey data. This paper analyses the test characteristics of using such a strategy for identification of high risk patients. Methods: Prior estimates of Framingham cardiovascular risk were derived in a population obtained from the Health Survey for England 2003. Receiver operating characteristics curves were constructed for using a prior estimate of cardiovascular risk to identify patients at greater than 20% ten-year cardiovascular risk. This was compared to strategies using age, or diabetic and antihypertensive treatment status to identify high risk patients. Results: The area under the curve for a prior estimate of cardiovascular risk calculated using minimum data (0.933, 95% CI: 0.925 to 0.941) is significantly greater than for a selection strategy based on age (0.892, 95% CI: 0.882 to 0.902), or diabetic and hypertensive status (0.608, 95% CI: 0.584 to 0.632). Conclusion: Using routine data held on primary care databases it is possible to identify a population at high risk of cardiovascular disease. Information technology to help primary care prioritise patients for cardiovascular disease prevention may improve the efficiency of cardiovascular risk assessment.