Observational versus randomized controlled trials to inform antibiotic treatment durations: a narrative review

Abstract

BackgroundStudies comparing shorter and longer antibiotic treatment durations are increasingly common. Randomized controlled trials (RCTs) are an ideal methodological approach to study antibiotic treatment durations; however, these trials can be logistically and financially challenging to conduct. ObjectivesIn this narrative review, we sought to compare the strengths and limitations of observational study data with those of RCT data in evaluating antibiotic treatment durations. We used uncomplicated Gram-negative bacteraemia as an illustrative case example because several published RCTs and observational studies have been conducted in similar patient populations. SourcesWe searched MEDLINE for articles comparing treatment durations for gram-negative bacteremia from inception to June 9th, 2022. We included studies reporting on all-cause mortality and/or relapse at day 28-30. Data comparing short- versus long-course therapy were pooled by Bayesian random effects meta-analyses to assess the odds ratios (OR) of all-cause mortality and relapse at 30 days, stratified by study design. Parameters were summarized with median and 95% highest-density credible intervals (CrI). Posterior probabilities of OR > 1.0 were estimated. Observational studies were further examined to determine if and how they addressed potential sources of bias. ContentWe identified 1671 unique records and included 10 studies (seven observational and three RCTs). With respect to 30-day mortality, the Bayesian posterior probability that a longer course of therapy was better (i.e. OR >1.0) was 42% in RCTs (OR, 0.94; 95% CrI, 0.51–1.68) and 91% in observational studies (OR, 1.25; 95% CrI, 0.88–1.73). No observational study fully addressed all potential sources of bias. ImplicationsOn the basis of our findings, we discuss future directions for antibiotic treatment duration trials, including approaches to limit sources of bias in observation data and novel trial designs.