Recalibrated Framingham Risk Score reduces overestimation, improves statin use

Depending on the model being used, recalibration of risk scores using contemporary population-level data may improve risk stratification for statin therapy, suggests a study.

In particular, recalibrating the Framingham Risk Score (FRS), but not the Pooled Cohort Equations (PCEs), reduces overestimation, leading to improved statin utilization.

The authors recalibrated 5-year FRS and PCEs using a cohort of Ontario, Canada, residents alive 1 January 2011. Participants were 30‒79 years of age without cardiovascular disease. Scores were externally validated in a primary care cohort of routinely collected electronic medical record data from 1 January 2010 to 31 December 2014.

Finally, the authors reported the relative difference in mean predicted and observed risk, number of statins avoided, and number needed to treat with statins to reduce a cardiovascular event at 5 years.

The FRS was recalibrated in a total of 6,938,971 residents (mean age 48 years, 51.6 percent women) and validated in 71,450 individuals (mean age 52 years, 56.1 percent). Recalibration led to a reduction in overestimation from 109 percent to 49 percent for women and from 131 percent to 32 percent for men.

The recalibrated FRS was projected to reduce statin prescription in up to 26 per 1,000 low-risk women and 80 per 1,000 low-risk men. It was also expected to decrease the number needed to treat from 61 to 47 in women and from 53 to 41 in men.

On the other hand, the risk remained overestimated by 217 percent in women and 128 percent in men after recalibrating the PCE.

“Recalibration is a feasible solution to improve risk prediction but is dependent on the model being used,” the authors said. “Recalibration of the FRS but not the PCEs reduced overestimation and may improve utilization of statins.”