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

Abstract

This simulation study investigates the effects of withinindividual variability in estimated cardiovascular risk on categorization of patients as high risk. Published estimates of within-individual blood pressure and cholesterol variability were used to generate blood pressure and cholesterol levels for hypothetical subjects at a range of ages. These were used to calculate the estimated cardiovascular risk of each individual. The relationship between an individual's mean cardiovascular risk and within-individual coefficient of variation for cardiovascular risk was determined. Using the derived relationship, mean cardiovascular risk and withinindividual variation in risk was calculated for 5018 adults from a population health survey. From this, was determined their probability of being classified as high risk (420% 10-year cardiovascular risk) and the test characteristics of risk estimation at a range of ages. Within-individual variability in cardiovascular risk and potential for misclassification are both greater in lowerrisk populations. At age 35-44 years, the positive predictive value of a diagnosis of high risk is 0.61 (95% confidence interval (CI): 0.59-0.64), and at age 65-74 years, it is 0.94 (95% CI: 0.91-0.96). About 39% of adults under 45 years diagnosed as high risk are not at high risk. Cardiovascular risk assessment should be targeted at high-risk populations.

Visit-to-visit lipid variability: Clinical significance, effects of lipid-lowering treatment, and (pharmaco) genetics

“…Researchers working with large-scale data from the Korean NHIS have recently shown that incorporating variability of different cardiovascular disease risk factors (including intra-individual variability of total cholesterol) substantially improved cardiovascular risk predictability compared with single measurement values or taking the average of repeated measurements 30 , though this was not examined separately for lipid variability. These findings are in line with a previous simulation study showing that blood pressure and cholesterol variability may lead to substantial misclassification when cardiovascular risk assessment is based on single measurements 31 , and with increasing evidence that incorporating repeated measurements can improve cardiovascular risk prediction. 32 Based on the current literature it is however not yet possible to make recommendations on the necessity of repeated lipid measurements in clinical practise either before or after starting lipid lowering treatment, beyond which is already viewed as necessary to overcome short-term fluctuations in lipid levels.…”

Secondary tests for stratification of risk for atherosclerosis

“…This leads to significant misclassification at the 30% risk threshold with 30.3% of 'true' positives being given a false negative result and 20.4% of those given a positive result which should have received a negative result. Similar error rates applied at other risk threshold and have been validated in other analyses 7 . Absolutely correct classification for the Framingham risk equation would have required an infinite number of tests, but in this scenario it was necessary to repeat testing nine times before the incremental improvement on classification was acceptable.…”

“…Intermediate risk is subject to a variety of cut-offs ranging from 5-10% at the lower bound to 15-20% at the upper boundary though 25% might be statistically more plausible given the errors involved. It is recognised that wherever a cut-off is set there will be misclassification on either side of the threshold 6,7 . By using two separate thresholds, the opportunity for misclassification is increased.…”

“…They also ignore intra-individual variation in risk factor levels which leads to considerable inaccuracy in risk estimation as the confidence intervals on any estimate can be substantial, i.e. 25% of the calculated value 6,7 . Given these limitations it is surprising that these calculation methods give a quite respectable C-statistic (receiver-operator characteristic area-under-curve) of 0.70 in most validation studies 2 .…”

The NHS Health Check programme: a comparison against established standards for screening

“…Finally, when derived, in-person variability in risk factors is not accounted for: this causes misclassification when applied, which is larger in those at lower risk. 3 Health checks not only test for risk, but also for occult disease. Early diagnosis, not direct management of risk, may hold the greatest potential programme gains, but there is evidence of limitations in health check procedures.…”