Heart failure is a major cause of death all over the world, but also causes a lot of disability as a chronic condition, especially with ageing populations. 2-3% of the population suffer with heart failure. Heart failure patients are often prescribed a whole range of medicines to treat their blood pressure, kidney disease and many other conditions. One more tablet called IVABRADINE (or Procoralan) looks set to join the list. I just saw the results of the SHIFT trial presented at the European Congress of Cardiology in Stockholm today and they were simultaneously published in the Lancet online.

50% of heart failure patients have a high heart rate (defined as greater than 70 beats per minute). Beta-blockers reduce the heart rate and have been shown to reduce mortality in heart failure. However, they are not always tolerated well, partly because they also cause a drop in blood pressure. Ivabradine is a new drug which reduced heart rate without much effect on blood pressure, and so may be a new option to treat heart failure.

The SHIFT trial was a double-blinded, placebo-controlled, randomised controlled trial of ivabradine in 6500 patients with moderate to severe heart failure and a regular heart rhythm. This trial specifically looked at the heart rate of patients at the start of the trial (“baseline”) and throughout the trial. The main or primary outcome of the trial was death or hospitalisation due to heart failure. Double blinding means that neither the patients nor the researchers knew which treatment the patients received. Controlling with a placebo allows the researchers to estimate the effect of the drug beyond no treatment. Randomisation means that patients randomly received placebo or the drug (in this case, ivabradine), and removed bias in the selection of patients. The trial lasted for just under 2 years.

Basically, ivabradine reduced death and hospitalisations by 18%, and the drug was very well tolerated, with few side effects of unduly low heart rate (bradycardia) or low blood pressure. The authors concluded that for every 1 beat per minute increase in heart rate, there was a 3% increase in mortality in a continuous relationship. They also found that baseline heart rate predicted the degree of risk of death, and interestingly, patients with the highest heart rate at baseline had the greatest reduction in heart rate with the drug, ivabradine. High heart rate has been shown to be a “risk marker” for outcome of patients with heart failure. This trial seems to suggest that a high heart rate may also be a “risk factor” for heart failure, i.e. it may have a role in causing the disease. Either way, “the slower, the better” seems to be the motto for the heart when it is failing.

Evidence-based guidelines recommend that after a heart attack, the blocked coronary artery needs to be reopened quickly by either fibrinolysis (or “clot-busting” drugs) or primary percutaneous coronary intervention (primary PCI), which aims to open the artery using balloons and stents. However, there are several reasons for delay in these treatments.

Firstly, “patient delay” is the delay from the onset of chest pain or symptoms to when a call is made to emergency medical services, and can only really be reduced by better public education about heart attacks. Secondly, “system delay” is a combination of “transportation delay” (the time taken for the patient to get to the hospital) and “door-to-balloon delay” (the time taken for the patient to receive the artery-opening therapy once they are in the hospital). In terms of training of doctors and measurement of outcomes within hospitals and across health systems, there has been a huge focus on the “door-to-balloon” delay. However, to know the effect of delaying therapy on outcome, we need to look at “system delay”, which is what a Danish study does in this week’s JAMA.

Due to excellent public medical databases in Denmark, the authors were able to study over 6000 patients with the particular form of heart attack (“STEMI”) which is best treated by primary PCI, and obtain estimates for the various types of delay outlined above. The authors excluded patients with a treatment delay greater than 12 hours or a system delay greater than 6 hours.

Interestingly, treatment delay and patient delay were not associated with mortality, but the authors are quick to assert that “should not deter encouraging patients to seek medical help as soon as possible after the onset of symptoms”. On the other hand, system delay predicted mortality, with a hazard ratio of 1.10 per 1-hour delay. In other words, for every one hour of system delay, there is a 10% increase in mortality. When the authors analysed further, they found that a 1-hour transportation delay led to 10% increase in mortality, whereas a 1-hour door-to-balloon delay led to a 14% increase in mortality. In other words, time does really mean muscle (and life) when it comes to the heart.

A brain scan that detects autism in adults could mean much more straightforward diagnosis of the condition, scientists say. Reported the BBC, Sky the Guardian and many more.

I had great difficulty getting hold of this paper, it wasn’t published online at the time of the press release. I managed to get a copy via Ben Goldacre at Bad Science and Evidence Matters who sent me the full text. Given this problem in getting the paper, it is highly likely no one who released the story has actually read the paper.

The news all report the headline ‘The researchers detected autism with over 90% accuracy, the Journal of Neuroscience reports.’

Sounds impressive, but this is one of the most obvious mistakes to make in interpreting a diagnostic test result. Never mind this is not the correct study type.

What has happened is the sensitivity has been taken for the positive predictive value, which is what you want to know: if I have a positive test do I have the disease?

Sensitivity: The proportion of people with disease who have a positive test.
Positive predictive value (+PV): The proportion of people with a positive test who have disease.

So, for a prevalence of 1% the actual positive predictive value is 4.5%. That is about 5 in every 100 with a positive test would have autism. Even at a prevalence of 2%, only 8.5% would be correctly identified.

Suddenly, not that great a test. This has to be one of the worst examples of misinterpreting diagnostic test results in the media I’ve ever seen.