Statins for primary prevention: at what coronary risk is safety assured?

Abstract

Aims: Increasingly HMG CoA reductase inhibitors (statins) are being used for primary prevention of vascular disease in patients with a raised cholesterol but at low absolute risk of coronary heart disease (CHD). This study uses clinical trial results to explore the limits of absolute safety for statin use in such patients.

Methods: The major placebo controlled statin outcome trials were identified by automated and manual literature searches. Principal results including all cause mortality in placebo and intervention groups and baseline values of standard coronary risk factors were abstracted for each trial. For the trials identified the reduction in overall mortality with statin treatment for each study was regressed against the underlying CHD risk of the population recruited into that trial using a statistically robust method.

Results: The regression line describing the relationship between mortality benefit and risk suggests that statin use could be associated with an increase in mortality of 1% in 10 years. This would be sufficiently large to negate statin's beneficial effect on CHD mortality in patients with a CHD event risk less than 13% over 10 years.

Conclusions: Absolute safety of statins has not been demonstrated for patients at low risk of CHD. Patients absolute risk of CHD should be calculated before starting statin treatment for primary prevention. Extensions of such treatment to low risk patients should await further evidence of safety.

Figure 1

Theoretical L'Abbe plot. Treatment causing neither harm nor benefit follows the line of identity (—). Panel a represents treatment (–) producing 20% relative endpoint reduction, and panel b treatment (…) producing identical benefit but increased mortality independent of underlying risk.

Figure 2

Illustration of regression dilution. The line of best fit (—) to data where the x-value (•) is measured without error is steeper than the line (–) fitted to the same data with random ‘error’ added to the x-values (○).

Figure 3

Illustrates automatic correlation. Values for a and b have been taken from a random distribution. Correlation of the difference between the two variables (a−b) with the value of one of the variables (a) is highly significant (P < 0.01) despite only 20 points.

Figure 4

The L'Abbe plot for the statin outcome trials. The line of best fit intersects with the line of identity at a 10 years death risk of 4.5%. This estimate may be biased by regression dilution (see text and [12]).

Figure 5

Statin net mortality benefit plotted against placebo group mortality for the outcome trials. The line of best fit intersects the x-axis at a 10 years death risk of 4.5%. This estimate may be biased by regression dilution (see text and [13]).

Figure 6

Bland-Altman plot of difference between observed placebo group CHD death rates and that predicted using the Grover equation against their mean. The model tends to underestimate risk especially when the risk of CHD death is high.

Figure 7

Plot of statin net mortality benefit against predicted CHD event risk. The line of best fit intersects the x-axis at a 10 years CHD event risk of 13%. People at lower risk may not benefit, and might even suffer harm.