Different risk calculation tools showed only moderate agreement (κ=0.33-0.65) with the Framingham reference standard for estimating coronary heart disease risk, often underestimating risk.
Cross-Sectional (n=36)
Yes
Does the use of different risk calculation tools improve the accuracy of coronary heart disease risk estimation by general practitioners and practice nurses compared to a reference standard?
Routine calculation of coronary heart disease risk in primary care is hampered by missing data, and clinicians evaluate risk with only moderate accuracy.
Effect estimate: κ=0.33-0.65
Abstract Objective: To assess the effect of using different risk calculation tools on how general practitioners and practice nurses evaluate the risk of coronary heart disease with clinical data routinely available in patients9 records. Design: Subjective estimates of the risk of coronary heart disease and results of four different methods of calculation of risk were compared with each other and a reference standard that had been calculated with the Framingham equation; calculations were based on a sample of patients9 records, randomly selected from groups at risk of coronary heart disease. Setting: General practices in central England. Participants: 18 general practitioners and 18 practice nurses. Main outcome measures: Agreement of results of risk estimation and risk calculation with reference calculation; agreement of general practitioners with practice nurses; sensitivity and specificity of the different methods of risk calculation to detect patients at high or low risk of coronary heart disease. Results: Only a minority of patients9 records contained all of the risk factors required for the formal calculation of the risk of coronary heart disease (concentrations of high density lipoprotein (HDL) cholesterol were present in only 21%). Agreement of risk calculations with the reference standard was moderate (κ=0.33-0.65 for practice nurses and 0.33 to 0.65 for general practitioners, depending on calculation tool), showing a trend for underestimation of risk. Moderate agreement was seen between the risks calculated by general practitioners and practice nurses for the same patients (κ=0.47 to 0.58). The British charts gave the most sensitive results for risk of coronary heart disease (practice nurses 79%, general practitioners 80%), and it also gave the most specific results for practice nurses (100%), whereas the Sheffield table was the most specific method for general practitioners (89%). Conclusions: Routine calculation of the risk of coronary heart disease in primary care is hampered by poor availability of data on risk factors. General practitioners and practice nurses are able to evaluate the risk of coronary heart disease with only moderate accuracy. Data about risk factors need to be collected systematically, to allow the use of the most appropriate calculation tools. What is already known on this topic Recent guidelines have recommended determining the risk of coronary heart disease for targeting patients at high risk for primary prevention Estimates of risk have been shown to be inaccurate General practitioners and practice nurses can use risk calculation tools accurately when given patient data in the form of scenarios What this study adds Many patients do not have adequate information in their records to allow the risk of coronary heart disease to be calculated When data about risk factors were available, risk calculations made by general practitioners and practice nurses were moderately accurate compared to a reference calculation When adequate information about risk factors is not available, subjective estimates are a reasonable alternative to calculating risk
Richard J. McManus (Sat,) conducted a cross-sectional in Coronary heart disease risk (n=36). Different risk calculation tools vs. Framingham equation reference standard was evaluated on Agreement of risk calculations with reference standard (κ=0.33-0.65). Different risk calculation tools showed only moderate agreement (κ=0.33-0.65) with the Framingham reference standard for estimating coronary heart disease risk, often underestimating risk.
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