Evidence map›Paper›PMID 32450877›Full record

ArticleScandinavian journal of trauma, resuscitation and emergency medicine2020

Decision support tool and suggestions for the development of guidelines for the helicopter transport of patients with COVID-19.

Per P Bredmose, Monica Diczbalis, Emma Butterfield, Karel Habig, Andrew Pearce, Svein Are Osbakk, Ville Voipio, Marcus Rudolph, Alistair Maddock, John O'Neill

Abstract read
In one paragraph

Article in Scandinavian journal of trauma, resuscitation and emergency medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–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

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. A Cross-Border Biorisk Toolkit for Healthcare Professionals.International journal of environmental research and public health · 2024
    Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Observational
  9. Performance analysis of data mining algorithms for diagnosing COVID-19.Journal of education and health promotion · 2021
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Observational
  19. Review
  20. 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

10 authors.

Per P BredmoseLifeFlight Retrieval Medicine, PO box 15166, City East, QLD, 4002, Australia. bredmose@hotmail.com.
Monica DiczbalisLifeFlight Retrieval Medicine, PO box 15166, City East, QLD, 4002, Australia.
Emma ButterfieldLifeFlight Retrieval Medicine, PO box 15166, City East, QLD, 4002, Australia.
Karel HabigNSW Ambulance Aeromedical Sydney HEMS, Sydney, Australia.
Andrew PearceMedSTAR Emergency Medical Retrieval, Adelaide, Australia.
Svein Are OsbakkAir Ambulance Department, Oslo University Hospital, Oslo, Norway.
Ville VoipioCentre for pre-hospital emergency care, Oulu University Hospital, Oulu, Finland.
Marcus RudolphDRF Stiftung Luftrettung gAG, Filderstadt, Germany.
Alistair MaddockEmergency Medical Retrieval Service, ScotSTAR, Paisley, Scotland.
John O'NeillLifeFlight Retrieval Medicine, PO box 15166, City East, QLD, 4002, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The novel coronavirus SARS-CoV2 emerged in December 2019 and is now pandemic. Initial analysis suggests that 5% of infected patients will require critical care, and that respiratory failure requiring intubation is associated with high mortality.Sick patients are geographically dispersed: most patients will remain in situ until they are in need of critical care. Additionally, there are likely to be patients who require retrieval for other reasons but who are co-incidentally infected with SARS-CoV-2 or shedding virus.The COVID-19 pandemic therefore poses a challenge to critical care retrieval systems, which often depend on small teams of specialists who live and work together closely. The infection or quarantining of a small absolute number of these staff could catastrophically compromise service delivery.Avoiding occupational exposure to COVID-19, and thereby ensuring service continuity, is the primary objective of aeromedical retrieval services during the pandemic. In this discussion paper we collaborated with helicopter emergency medical services(HEMS) worldwide to identify risks in retrieving COVID-19 patients, and develop strategies to mitigate these.Simulation involving the whole aeromedical retrieval team ensures that safety concerns can be addressed during the development of a standard operating procedure. Some services tested personal protective equipment and protocols in the aeromedical environment with simulation. We also incorporated experiences, standard operating procedures and approaches across several HEMS services internationally.As a result of this collaboration, we outline an approach to the safe aeromedical retrieval of a COVID-19 patient, and describe how this framework can be used to develop a local standard operating procedure.

Indexed as

BetacoronavirusCoronavirus InfectionsPandemicsPneumonia, ViralPractice Guidelines as TopicAircraftCOVID-19Decision MakingHumansPersonal Protective EquipmentRespiratory InsufficiencySARS-CoV-2Aeromedical transportCOVID-19Helicopter emergency medical serviceHEMSInfectious diseaseRetrievalTransport

Identifiers

PMID32450877
PMCPMC7247287

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.