Mendelian randomisation studies show that lifelong exposure to lower low-density lipoprotein (LDL) cholesterol and systolic blood pressure (SBP) is associated with much larger reductions in the risk of CV events compared to the reductions observed in randomised trials from lowering LDL and SBP starting later in life.1–3 However, these causal effects of LDL and SBP are not incorporated into current risk-estimating algorithms.
In a Hot Line session yesterday, Professor Brian A. Ference (University of Cambridge - Cambridge, UK) presented analyses designed to help improve existing CV risk-prediction strategies. Analyses were performed that firstly evaluated whether current risk scores accurately estimate the baseline risk of CV events caused by LDL cholesterol and SBP and the benefit of lowering LDL and SBP beginning at any age and extending for any duration. The team also studied whether adding the causal effects of LDL and SBP more accurately estimates CV risk and benefit. The Causal AI algorithm was used to estimate the effects of LDL and SBP in discrete time units of exposure among 1.8 million individuals, including 1,320,974 enrolled in Mendelian randomisation studies evaluating 140 variants associated with LDL and 202 variants associated with SBP, and 527,512 participants enrolled in 76 randomised trials evaluating LDL- or SBP-lowering therapies.
The accuracy of the Joint British Societies’ (JBS3) algorithm was evaluated, both alone and after adding Causal AI effects of LDL and SBP in: 1) an independent sample of 445,771 participants in the UK Biobank to assess how well these algorithms estimated lifetime risk and benefit; and 2) in 48,315 participants in LDL- and SBP-lowering trials (HPS, SPRINT and HOPE-3) to assess how well these algorithms estimated the short-term benefit of lowering LDL, SBP or both observed in the trials. The primary outcome was major coronary events (MCE), defined as the first occurrence of a fatal or non-fatal myocardial infarction or coronary revascularisation.
The JBS3 algorithm systematically underestimated the risk of MCE among persons with lifelong higher LDL, SBP or both, and systematically overestimated risk among those with lifelong exposure to lower LDL, SBP or both.
Prof. Ference comments, “This finding explains why current risk algorithms lead to the biologically implausible conclusion that LDL and SBP – the two main modifiable causes of atherosclerotic CV events – do not meaningfully contribute to the risk of CV events. By contrast, including the causal effects of LDL and SBP, derived from the Causal AI algorithm, accurately estimated the risk of MCE at all ages among persons with both higher and lower lifetime exposure to LDL, SBP or both.”
Regarding treatment benefit, the JBS3 algorithm was found to systematically underestimate the benefit of maintaining lifelong lower LDL, SBP or both on MCE. In contrast, including the causal effects accurately estimated the benefit of maintaining lifelong lower LDL and SBP at all ages. Similarly, the JBS3 algorithm systematically underestimated the benefit of lowering LDL, SBP or both starting later in life as compared to randomised trials of LDL- and SBP-lowering therapies. Of note, including causal effects accurately estimated the benefit of lowering LDL, SBP or both starting later in life during every month of follow-up observed in randomised trials.
Prof. Ference explains, “Current risk-estimating algorithms are biased against prevention because they systematically underestimate the benefit of lowering LDL cholesterol and SBP. This may lead to the false conclusion that waiting to lower LDL and SBP until later in life is more effective and costs less than lowering LDL and SBP at a younger age. This study shows for the first time how to embed the causal effects of LDL cholesterol and SBP into AI algorithms. These algorithms could be used to inform decisions for individual patients on the optimal timing, intensity and duration of LDL and SBP lowering to most effectively prevent atherosclerotic CV events.” And he concludes, “Replacing current algorithms with Causal AI has the potential to personalise the prevention of CV disease and illustrate the public health and economic value of investing in CV prevention.”