Evidence map›Paper›PMID 35580239›Full record

ArticleActa anaesthesiologica Scandinavica2022

Agents intervening against delirium in the intensive care unit trial-Protocol for a secondary Bayesian analysis.

Nina Andersen-Ranberg, Lone M Poulsen, Anders Perner, Johanna Hästbacka, Matthew P G Morgan, Giuseppe Citerio, Marie Oxenbøll-Collet, Sven-Olaf Weber, Anne Sofie Andreasen, Morten H Bestle and 9 more

Abstract read
In one paragraph

Article in Acta anaesthesiologica Scandinavica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Trial
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Nina Andersen-RanbergDepartment of Anaesthesiology and Intensive Care Medicine, Zealand University Hospital, Køge, Denmark.ORCID 0000-0002-0804-1064
Lone M PoulsenDepartment of Anaesthesiology and Intensive Care Medicine, Zealand University Hospital, Køge, Denmark.ORCID 0000-0002-7030-3395
Anders PernerCollaboration for Research in Intensive Care (CRIC), Copenhagen, Denmark.ORCID 0000-0002-4668-0123
Johanna HästbackaDepartment of Anaesthesiology, Helsinki University Hospital, Helsinki, Finland.ORCID 0000-0002-3613-7231
Matthew P G MorganDepartment of Intensive Care, Cardiff University Hospital of Wales, Cardiff, UK.
Giuseppe CiterioUniversita Milano Bicocca, Monza, Italy.
Marie Oxenbøll-ColletCollaboration for Research in Intensive Care (CRIC), Copenhagen, Denmark.ORCID 0000-0002-8387-3960
Sven-Olaf WeberDepartment of Anaesthesia and Intensive Care, Aalborg University Hospital, Aalborg, Denmark.
Anne Sofie AndreasenDepartment of Anaesthesiology and Intensive Care, Herlev Hospital, Herlev, Denmark.
Morten H BestleDepartment of Anaesthesiology and Intensive Care, Copenhagen University Hospital - North Zealand, Hillerød, Denmark.
Bülent UsluDepartment of Anaesthesiology and Intensive Care Medicine, Zealand University Hospital, Roskilde, Denmark.
Helle B S PedersenDepartment of Anaesthesiology and Intensive Care, Nykøbing Falster Sygehus, Nykøbing Falster, Denmark.
Louise G NielsenDepartment of Anaesthesiology and Intensive Care, Odense University Hospital, Odense, Denmark.
Kjeld DamgaardDepartment of Anaesthesiology and Intensive Care, Regionshospital Nordjylland, Hjørring, Denmark.
Troels B JensenDepartment of Anaesthesiology and Intensive Care Medicine, Herning Hospital, Herning, Denmark.
Trine SommerDepartment of Anaesthesiology and Intensive Care Medicine, Hospital Sønderjylland, Aabenraa, Denmark.
Nilanjan DeyDepartment of Anaesthesiology and Intensive Care Medicine, Holstebro Hospital, Holstebro, Denmark.
Ole MathiesenDepartment of Anaesthesiology and Intensive Care Medicine, Zealand University Hospital, Køge, Denmark.ORCID 0000-0003-4544-0619
Anders GranholmCollaboration for Research in Intensive Care (CRIC), Copenhagen, Denmark.ORCID 0000-0001-5799-7655

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDelirium is highly prevalent in the intensive care unit (ICU) and is associated with high morbidity and mortality. The antipsychotic haloperidol is the most frequently used agent to treat delirium although this is not supported by solid evidence. The agents intervening against delirium in the intensive care unit (AID-ICU) trial investigates the effects of haloperidol versus placebo for the treatment of delirium in adult ICU patients.

methodsThis protocol describes the secondary, pre-planned Bayesian analyses of the primary and secondary outcomes up to day 90 of the AID-ICU trial. We will use Bayesian linear regression models for all count outcomes and Bayesian logistic regression models for all dichotomous outcomes. We will adjust for stratification variables (site and delirium subtype) and use weakly informative priors supplemented with sensitivity analyses using sceptical priors. We will present results as absolute differences (mean differences and risk differences) and relative differences (ratios of means and relative risks). Posteriors will be summarised using median values as point estimates and percentile-based 95% credibility intervals. Probabilities of any benefit/harm, clinically important benefit/harm and clinically unimportant differences will be presented for all outcomes. DISCUSSION: The results of this secondary, pre-planned Bayesian analysis will complement the primary frequentist analysis of the AID-ICU trial and facilitate a nuanced and probabilistic interpretation of the trial results.

Indexed as

Antipsychotic AgentsDeliriumAdultBayes TheoremHaloperidolHumansIntensive Care UnitsAntipsychotic AgentsHaloperidolBayesian statisticsDeliriumhaloperidolICU

Identifiers

PMID35580239
PMCPMC9540259

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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.