Trial reportBMJ open2019
Perioperative haemodynamic therapy for major gastrointestinal surgery: the effect of a Bayesian approach to interpreting the findings of a randomised controlled trial.
Trial report in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Target temperature management following cardiac arrest: a systematic review and Bayesian meta-analysis.Critical care (London, England) · 2022Pooled it
- Lower versus higher oxygenation targets in critically ill patients with severe hypoxaemia: secondary Bayesian analysis to explore heterogeneous treatment effects in the Handling Oxygenation Targets in the Intensive Care Unit (HOT-ICU) trial.British journal of anaesthesia · 2022Trial
- A tutorial on the what, why, and how of Bayesian analysis: Estimating mood and anxiety disorder prevalence using a Canadian data linkage study.PLOS mental health · 2025Article
- Improving clinical trial interpretation with ACCEPT analyses.NEJM evidence · 2022Article
- Randomised clinical trials in critical care: past, present and future.Intensive care medicine · 2022Review
- Higher vs Lower Doses of Dexamethasone in Patients with COVID-19 and Severe Hypoxia (COVID STEROID 2) trial: Protocol for a secondary Bayesian analysis.Acta anaesthesiologica Scandinavica · 2021Article
- Clinical trials in critical care: can a Bayesian approach enhance clinical and scientific decision making?The Lancet. Respiratory medicine · 2021Review
- Application of the Reverse Fragility Index to Statistically Nonsignificant Randomized Clinical Trial Results.JAMA network open · 2020Article
- Shedding light on perioperative hemodynamic monitoring.Journal of clinical monitoring and computing · 2020Article
- The Effect of βFrontiers in immunology · 2020Article
- Designing and Evaluating Bayesian Advanced Adaptive Randomised Clinical Trials: A Practical Guide.Pharmaceutical statisticsArticle
- Effects of duration of follow-up and lag in data collection on the performance of adaptive clinical trials.Pharmaceutical statisticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveThe traditional approach of null hypothesis testing dominates the design and analysis of randomised controlled trials. This study aimed to demonstrate how a simple Bayesian analysis could have been used to analyse the Optimisation of Perioperative Cardiovascular Management to Improve Surgical Outcome (OPTIMISE) trial to obtain more clinically interpretable results. DESIGN, SETTING, PARTICIPANTS AND
interventionsThe OPTIMISE trial was a pragmatic, multicentre, observer-blinded, randomised controlled trial of 734 high-risk patients undergoing major gastrointestinal surgery in 17 acute care hospitals in the UK. Patients were randomly allocated to a cardiac output-guided haemodynamic therapy algorithm for intravenous fluid and inotropic drug administration during and in the 6 hours following surgery (n=368) or to standard care (n=366). The primary outcome was a binary outcome consisting of a composite of predefined 30-day moderate or major complications and mortality.
methodsWe repeated the primary outcome analysis of the OPTIMISE trial using Bayesian statistical methods to calculate the probability that the intervention was superior, and the probability that a clinically relevant difference existed. We explored the impact of a flat prior and an evidence-based prior on our analyses.
resultsAlthough OPTIMISE was not powered to detect a statistically significant difference between the treatment arms for the observed effect size (relative risk=0.84, 95% CI 0.70 to 1.01; p=0.07), by using Bayesian analyses we were able to demonstrate that there was a 96.9% (flat prior) to 99.5% (evidence-based prior) probability that the intervention was superior to the control.
conclusionsThe use of a Bayesian analytical approach provided a different interpretation of the findings of the OPTIMISE trial (compared with the original frequentist analysis), and suggested patient benefit from the intervention. Incorporation of information from previous studies provided further evidence of a benefit from the intervention. Bayesian analyses can produce results that are more easily interpretable and relevant to clinicians and policy-makers. TRIAL REGISTRATION NUMBER: ISRCTN04386758; Post-results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.