N-of-1 trials to facilitate evidence-based deprescribing: Rationale and case study

Abstract

Deprescribing has emerged as an important aspect of patient-centred medication management but is vastly underutilized in clinical practice. The current narrative review will describe an innovative patient-centred approach to deprescribing-N-of-1 trials. N-of-1 trials involve multiple-period crossover design experiments conducted within individual patients. They enable patients to compare the effects of two or more treatments or, in the case of deprescribing N-of-1 trials, continuation with a current treatment versus no treatment or placebo. N-of-1 trials are distinct from traditional between-patient studies such as parallel-group or crossover designs which provide an average effect across a group of patients and obscure differences between individuals. By generating data on the effect of an intervention for the individual rather than the population, N-of-1 trials can promote therapeutic precision. N-of-1 trials are a particularly appealing strategy to inform deprescribing because they can generate individual-level evidence for deprescribing when evidence is uncertain, and can thus allay patient and physician concerns about discontinuing medications. To illustrate the use of deprescribing N-of-1 trials, we share a case example of an ongoing series of N-of-1 trials that compare maintenance versus deprescribing of beta-blockers in patients with heart failure with preserved ejection fraction. By providing quantifiable data on patient-reported outcomes, promoting personalized pharmacotherapy, and facilitating shared decision making, N-of-1 trials represent a potentially transformative strategy to address polypharmacy.

Keywords: N-of-1; beta-blockers; deprescribing; personalized medicine.

Figure 1:

Comparison of N-of-1 trial design to conventional randomized clinical trials

Figure 2:

Potential treatment schemes for deprescribing N-of-1 trials

Figure 3:

Data visualization scheme using gas gauges *Adapted from a prototype developed by Lisa Grossman Liu.