How to present treatment effects of implantable defibrillators? Statistics, baseline risk, and meaningful benefit

Abstract

This editorial refers to ‘Absolute risk reduction in total mortality with implantable cardioverter defibrillators: analysis of primary and secondary prevention trial data to aid risk/benefit analysis’ by T.R. Betts et al. ,o n page 813. Patients with left ventricular dysfunction are prone to sudden cardiac death, presumably from ventricular tachyarrhythmias. Randomized controlled trials (RCTs) demonstrated that prophylactic implantable cardioverter-defibrillators (ICDs) improve survival among selected patients with left ventricular dysfunction considered at high risk of sudden cardiac death. Based on these clinical trial data, the indications for ICD therapy rapidly expanded from restricted therapy of ‘last resort’ (secondary prevention) to a broad-reaching pre-emptive therapy (primary prevention). However, the survival benefit of prophylactic ICD therapy is not uniform across the population with implants. For evidence to be of value, healthcare professionals, patients, and policy makers are faced with the challenge to interpret and apply the data of RCTs in daily practice. Simplified, they need to understand whether one treatment is better than another or better than no treatment at all. Clinicians have the task to communicate risk of a particular treatment to their patients. So, do we understand the concept of risk of a particular treatment, and how do we communicate this to our patients? In recent years, the amount of medical literature has increased rapidly, and with the Internet era information on medical research has become more easily accessible. The problem is how to interpret the results of several studies and decide whether it justifies changing the current treatment. The poor presentation of medical statistics of risk associated with a particular treatment can lead to poor decision-making. There are several statistical formats to present risk and risk reductions. Formats for presenting risk include frequency, percentage, and probability. Formats for presenting risk reduction include relative risk reduction (RRR), absolute risk reduction (ARR), and numbers needed to treat (NTT). Example illustrating the statistical formats The SCD-HeFT (Sudden Cardiac Death in Heart Failure Trial) study found that the ICD reduces the risk of all-cause mortality by 23% over the 5-year follow-up. 1 Specifically, 36.1% of patients without an ICD died at 5 years, compared with 28.9% of patients who received an ICD. Thus, 7.2% (36.1 2 28.9%) fewer patients would die if they received an ICD during the 5-year follow-up. In other words, 14 patients need to receive an ICD to prevent one death over 5 years. ‘Reduces the risk of all-cause mortality by 23%’ represents a RRR. ‘Seven percent less would die’ represents the ARR. ‘Fourteen patients need to receive an ICD in order to prevent one death’ represents the NTT. The question faced by the clinicians is then ‘Which one will help me in choosing the best treatment for my patient?’ In terms of risk reduction, clinicians might be more willing to recommend, and patients are more willing to accept, an intervention when its benefits are presented in relative compared with absolute terms. In this respect, the RRR can be regarded as a more persuasive summary statistic. But what really counts in decision-making is