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Identification of patients for clinical risk assessment by prediction of cardiovascular risk using default risk
1Department of Public Health & Epidemiology, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK. T.P.Marshall@bham.ac.uk
Insights
Primary care can identify high-risk cardiovascular disease patients using routine electronic health record data. This strategy is more effective than using age or treatment status alone for risk assessment.
Area of Science:
- Cardiovascular disease risk assessment
- Primary care informatics
- Health services research
Background:
- Identifying patients without cardiovascular disease (CVD) at high risk necessitates risk factor assessment.
- Primary care providers need efficient methods to prioritize CVD risk assessment for at-risk individuals.
- Utilizing routinely available electronic medical record (EMR) data with imputed values for missing blood pressure and cholesterol can estimate prior CVD risk.
Purpose of the Study:
- To analyze the test characteristics of using routinely collected EMR data to identify patients at high risk of CVD.
- To compare the effectiveness of a CVD risk estimation strategy using EMR data against strategies based on age or treatment status.
Main Methods:
- Framingham cardiovascular risk estimates were calculated using data from the Health Survey for England 2003.
- Receiver operating characteristic (ROC) curves were constructed to evaluate the ability of prior CVD risk estimates to identify patients with a >20% ten-year CVD risk.
- Performance was compared against strategies using age, or diabetic and antihypertensive treatment status.
Main Results:
- The area under the ROC curve (AUC) for prior CVD risk estimation using minimum EMR data was 0.933 (95% CI: 0.925–0.941).
- This AUC was significantly higher than strategies based on age (AUC: 0.892, 95% CI: 0.882–0.902) or diabetic and hypertensive status (AUC: 0.608, 95% CI: 0.584–0.632).
Conclusions:
- Routinely available primary care data can effectively identify populations at high risk for cardiovascular disease.
- Leveraging information technology for patient prioritization in CVD prevention can enhance the efficiency of risk assessment in primary care settings.
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.
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