New Lifetime Risk Equation More Accurately Predicts CV Risk and Treatment Benefits

September 1, 2020

The new equation model has shown to be more accurate than the Framingham Lifetime Risk equation and Pooled Cohort Equation.

A new lifetime risk equation based on cumulative exposure to low-density lipoprotein (LDL) and systolic blood pressure (SBP) is able to more accurately predict patient risk for cardiovascular (CV) disease and determine how much benefit they can gain from LDL and SBP reduction, according to new data presented at the European Society of Cardiology (ESC 2020) Congress.

Brian Ference, MD, MPhil, MSc, professor and Director of Research in Translational Therapeutics at the University of Cambridge, led a study that assessed the impact of cumulative exposure to high lipid levels and blood pressure on the risk of CV disease. Beyond risk assessment, the investigators were also interested in understanding if and how benefit can be attained by lowering LDL and SBP.

They conducted their Mendelian randomization study using 184,305 (60, 601 cases) participants enrolled in the CARDIoGRAMplusC4D consortium.

Then they estimated the causal effect of each mmol-year of increasing exposure to LDL and each mmHg-year on SBP on the risk of CV disease.

According to their results, they found that each mmol-year of LDL was associated with a 1.87% proportional increase in CV risk (95% CI; 1.69-1.97; P = 6.8E-143).

Furthermore, each mmHg-year of SBP was linked to a 0.17% increase in risk.

To confirm these findings, they pulled data on 445,675 (23,032 cases) participants between 45-75 years from the UK Biobank. Within this population, they measured the effect of naturally random allocation to 1 mmol/L change in LDL or 10 mmHg change SBP on the risk of major coronary events during each year of life.

The investigators noted that the effect observed was almost identical to that in the CARDIoGRAMplusC4D consortium population.

From this, they were able to construct a new Lifetime Risk equation, which they reported had greater accuracy than the Framingham Lifetime equation (P<.0001) and 10-year prediction models like the Pooled Cohort Equation (P<.0001).

Additionally, this new equation was able to accurately predict meta-analyses of trials involving statin use (193,514 participants) and BP lowering (613,815 participants), as well as the benefits of lipid lowering observed in long-term follow-up of 3 trials (21, 355 participants).

The investigators observed that the increment of increasing benefit from LDL or SBP reduction was exactly similar to the pattern of increasing risk observed in the Mendelian studies.

They also noted that the optimal age to begin lipid or SPB lowering depended on various factors, including the individual’s prior cumulative exposure to LDL and SBP, the preferred method of lipid or BP lowering, and the target lifetime risk goal.

“Using this information, we can begin to reconfigure risk estimation towards health management,” Ference said in an interview with HCPLive®. “Which is similar to the way we conceive of wealth management, where we recommend that somebody invest a certain amount of money each month in order to achieve whatever their goal is at retirement. We can now frame health in the same way.”

He proceeded to discuss the importance of informing patients how much to invest in lowering their LDL and/or BP levels in order to achieve their target lifetime risk benefits.

“We think that focusing on benefit rather than risk is a much stronger motivation to keep people engaged and wanting to invest in their health to achieve those benefits over time,” he concluded.