Risk factor control or drug therapy in lowering cardiovascular deaths

I'm not sure exactly how this intriguing graph was constructed, but it demonstrates that nearly half of the reduction in CHD deaths over the past 50 years is due to new therapies, whilst most of the remainder stems from better risk factor control.

From the ESC Guidelines

Another approach to understanding the changes in CVD mortality and incidence rates is by applying models such as the IMPACT mortality model. Based on information on changes in coronary risk factors and in treatment as obtained from the results of RCTs regarding the effectiveness of different treatment modalities, it estimates the expected influence on CHD mortality by age and gender. This model has been applied in different countries; the results from these studies are rather consistent and similar to what has been observed in other studies of the same subject, as summarised in Figure 1. Beneficial reductions in major risk factors—in particular smoking, BP, and cholesterol—accounted for more than half of the decrease in CHD deaths, although they were counteracted by an increase in the prevalence of obesity and type 2 diabetes; 40% of the decline in CHD death rates is attributed to better treatments of acute myocardial infarction, heart failure, and other cardiac conditions. Results from clinical trials and natural experiments also show that a decline in CHD mortality can happen rapidly after individual or population-wide changes in diet or smoking.

Percentage of the decrease in deaths from coronary heart disease attributed to treatments and risk factor changes in different populations (adapted from Di Chiara et al. Does surveillance impact on cardiovascular prevention? Eur Heart J 2009;30:1027–1029.)

Predicting the outcome of clinical trials by computer?

Clinical trials are very expensive, time consuming and frequently yield inconclusive results.

An article in Wired magazine described a computer simulation model that can predict the results of drug trials in humans, without actually giving a single patient a pill.

The model is called Archimedes, and is based at the San Francisco company of the same name. Its creator, David Eddy, spent two decades programming information about anatomy, physiology, disease, risk factors and their response to different drugs. The article explains how Archimedes was able to almost exactly predict the true results of the CARDS trial (examining the effects of statin therapy on cardiovascular outcomes in diabetic patients) ahead of unblinding of that study.

Whilst the underlying algorithms and assumptions of Archimedes are a trade secret, do you think it gives us a glimpse into the future of clinical trials? Where studies of drug efficacy will be simulated on hundreds of thousands of patients? There’s also evidence that the adverse effects of drugs (for example hepatotoxicity) can also be predicted with reasonable accuracy. This is achieved by comparing the molecular structure of the drug in question with millions of others that have a known side effect profile.

Personally, I think these developments are fascinating, but I think the era of the large-scale clinical trial will be here for a while yet. Whether big pharma can leverage these types of simulations to screen for likely efficacious molecules with few adverse effects on human physiology remains to be seen.

What do you think? Let me know in the comments below.

HDL research must continue say experts at the EAS meeting

HDL research must continue (via the heart.org)

> After a series of negative trial results, the concept of raising high-density lipoprotein (HDL) as a therapeutic approach to reducing cardiovascular risk looks to be in a sorry state. But lipid experts at the recent European Atherosclerosis Society (EAS) 2012 Congress were adamant that the HDL hypothesis was not yet dead and that it is imperative that research in this direction continue.

What do you think? Do you hold out any hope for this method of lowering CV risk? Should we wait for the results of the phase 3 anacetrapib study? What about the HPS2-thrive study with Niacin? Do we have to wait until 2013 or should we withdraw Niacin today?