Accuracy of Cardiovascular Risk Estimation for Primary Prevention in Patients Without Diabetes

Abstract

Background The burden of atherosclerosis has led to treatment prioritization on high-risk individuals without established cardiovascular disease based on risk estimates. We investigated the effects of biological variation in risk factors on risk estimate accuracy and whether current primary prevention screening (risk assessment) models correctly categorize patients.Methods A population of 10 000 'perfect' individuals with 100 simulants affected by biological and analytical variation for systolic blood pressure, total cholesterol, high-density lipoprotein-cholesterol was mathematically modelled. Coronary heart disease (CHD) risks were calculated using the Framingham study algorithm and the mathematical properties of the screening system were evaluated.Results At internationally recommended 10-year CHD risk treatment threshold levels of 15, 20 and 30%, the 95% confidence intervals were 7 5.1, 7 6.0 and 7 6.9% for single-point (singlicate), 7 3.6, 7 4.2 and 7 4.9% for duplicate and 7 2.8, 7 3.3 and 7 3.9% for triplicate estimates respectively (i.e. for singlicate 15% risk, 95% confidence interval is 9.9-20.1%). Consequently, using the 30% risk threshold from the National Service Framework (NSF) for CHD with singlicate estimation, 30% of patients who should receive treatment would be denied it and 20% would receive treatment unnecessarily. Multiple measurements improve precision but cannot absolutely define risk. Blood pressure should be measured to the greatest accuracy possible and not rounded prior to averaging.Conclusions This study suggests biological variation in cardiovascular risk factors has profound consequences on calculated risk for therapeutic decision-making. Current guidelines recommending multiple measurements are usually ignored. Triplicate measurement is required to allow risk to be identified and clinical judgement has to be exercised in interpretation of the results.

The effect of blood pressure and cholesterol variability on the precision of Framingham cardiovascular risk estimation: a simulation study

“…A previous modelling study found a very similar CI for estimated coronary risk of among diabetic patients to be 15% (95% CI 9.9-20.1%). 18 The findings are likely to be similar with any multivariable risk equation using similar variable risk factors and in any population with a similar distribution of those risk factors. A model is dependent on its underlying assumptions.…”

Agreement Among Cardiovascular Disease Risk Calculators

“…However, it is known that Framingham models require some recalibration for differing populations based on prevalence of risk factors and CHD rates. 16 Therefore, some of these differences may result from adjustments applied to the calculator algorithms to better reflect the population for which the risk calculators was designed.…”

Accuracy of Cardiovascular Risk Estimation

“…This was achieved at nine repetitions, but again, testing each patient on nine separate occasions would be excessive for obviously low-risk cases (2 ). Because the detection limit and specificity of less than three repetitions were poor and because there is always the possibility of laboratory error, it follows that the minimum standard in very low-or very high-risk cases should be three repetitions.…”

“…The baseline data on which risk calculators are based suffer the same problems of variation that affect the interpretation of patient data. The failure of decision thresholds to correctly identify patients and the implications of both of these studies (1,2 ) …”

“…We have recently published a similar analysis scenario in which we mathematically modeled a hypothetical "True" population derived from data from the National Health Survey for England. We investigated the effect of combined biological and analytical variation in total cholesterol and HDL-cholesterol, as well as blood pressure, on calculated risk and likely treatment decisions (2 ). Using the Framingham (1991) risk model (3 ) at the various internationally recommended 10-year coronary-heartdisease-risk treatment threshold concentrations of 15%, 20%, and 30%, the 95% confidence limits at these points were Ϯ5.1%, Ϯ6.0%, and Ϯ6.9% for singlicate estimates; Ϯ3.6%, Ϯ4.2%, and Ϯ4.9% for duplicate estimates; and Ϯ2.8%, Ϯ3.3%, Ϯ3.9% for triplicate estimates, respectively (i.e., for singlicate 15% risk, 95% confidence interval is 9.9 -20.1%).…”