Evidence map›Paper›PMID 38129358›Full record

Trial reportCanadian journal of anaesthesia = Journal canadien d'anesthesie2024

Modelling the potential increase in eligible participants in clinical trials with inclusion of community intensive care unit patients in Alberta, Canada: a decision tree analysis.

Nicholas Quigley, Alexandra Binnie, Nadia Baig, Dawn Opgenorth, Janek Senaratne, Wendy I Sligl, Danny J Zuege, Oleksa Rewa, Sean M Bagshaw, Jennifer Tsang and 1 more

Abstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Canadian journal of anaesthesia = Journal canadien d'anesthesie, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Nicholas QuigleyDepartment of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, AB, Canada. quigley1@ualberta.ca.ORCID 0000-0001-9177-7947
Alexandra BinnieDepartment of Critical Care, William Osler Health System, Brampton, ON, Canada.
Nadia BaigDepartment of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, AB, Canada.
Dawn OpgenorthDepartment of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, AB, Canada.
Janek SenaratneDepartment of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, AB, Canada.
Wendy I SliglDepartment of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, AB, Canada.
Danny J ZuegeDepartment of Critical Care Medicine, University of Calgary, Calgary, AB, Canada.
Oleksa RewaDepartment of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, AB, Canada.
Sean M BagshawDepartment of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, AB, Canada.
Jennifer TsangDivision of Critical Care Medicine, Niagara Health, St. Catharines, ON, Canada.
Vincent I LauDepartment of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, AB, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeCritical care research in Canada is conducted primarily in academically affiliated intensive care units (ICUs) with established research infrastructure. Efforts are made to engage community hospital ICUs in research, although the impacts of their inclusion in clinical research have never been explicitly quantified. We therefore sought to determine the number of additional eligible patients that could be recruited into critical care trials and the change in time to study completion if community ICUs were included in clinical research.

methodsWe conducted a decision tree analysis using 2018 Alberta Health Services data. Patient demographics and clinical characteristics for all ICU patients were compared against eligibility criteria from ten landmark, randomized, multicentre critical care trials. Individual patients from academic and community ICUs were assessed for eligibility in each of the ten studies, and decision tree analysis models were built based on prior inclusion and exclusion criteria from those trials.

resultsThe number of potentially eligible patients for the ten trials ranged from 2,082 to 10,157. Potentially eligible participants from community ICUs accounted for 40.0% of total potentially eligible participants. The recruitment of community ICU patients in trials would have increased potential enrolment by an average of 64.0%. The inclusion of community ICU patients was predicted to decrease time to trial completion by a mean of 14 months (43% reduction).

conclusionInclusion of community ICU patients in critical care research trials has the potential to substantially increase enrolment and decrease time to trial completion.

Indexed as

Critical CareIntensive Care UnitsAlbertaDecision TreesHumanscommunity sitescritical care researchinclusion criteriaparticipants

Identifiers

PMID38129358

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.