Evidence map›Paper›PMID 32944572›Full record

ArticleBulletin of emergency and trauma2020

Real-Time Visual Feedback Device Improves Quality Of Chest Compressions: A Manikin Study.

João B Augusto, Miguel B Santos, Daniel Faria, Paulo Alves, David Roque, José Morais, Victor Gil, Carlos Morais

Abstract read
In one paragraph

Article in Bulletin of emergency and trauma, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. Article
  5. Article
  6. 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

8 authors.

João B AugustoDepartment of Cardiology, Hospital Professor Doutor Fernando Fonseca, Lisbon, Portugal.
Miguel B SantosDepartment of Cardiology, Hospital Professor Doutor Fernando Fonseca, Lisbon, Portugal.
Daniel FariaDepartment of Cardiology, Hospital Professor Doutor Fernando Fonseca, Lisbon, Portugal.
Paulo AlvesDepartment of Cardiology, Hospital Professor Doutor Fernando Fonseca, Lisbon, Portugal.
David RoqueDepartment of Cardiology, Hospital Professor Doutor Fernando Fonseca, Lisbon, Portugal.
José MoraisDepartment of Cardiology, Hospital Professor Doutor Fernando Fonseca, Lisbon, Portugal.
Victor GilDepartment of Cardiology, Hospital dos Lusíadas, Lisbon, Portugal.
Carlos MoraisDepartment of Cardiology, Hospital Professor Doutor Fernando Fonseca, Lisbon, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the impact of a real-time visual feedback device on Chest comparison (CCs) rate and depth delivered by healthcare professionals.

methodsIn a simulated scenario a sensor was placed on a manikin's chest and connected to a defibrillator which provided real-time visual feedback on the rate and depth of CCs. Thirty-two healthcare professionals performed sequentially 5 cycles of 30 CCs without (FeedOFF) and with (FeedON) feedback. CCs with a depth between 50 and 60mm and a rate between 100 and 120cpm were considered optimal.

resultsVisual feedback resulted in a significant increase in the proportion of CCs with optimal depth (median 8.7 [interquartile range 0.7-55.5]% FeedOFF vs 63.3 [17.6-88.1]% FeedON, p=0.002) and optimal rate (median 51.3 [1.3 - 81.3]% FeedOFF vs 68.3 [45.3 - 86.1]% FeedON,

conclusionsIn the absence of visual feedback, there is a tendency towards lower depth and higher rate of CCs. The use of feedback technology significantly improves the quality of CCs.

Indexed as

Basic life supportResuscitationSimulationTraining

Identifiers

PMID32944572
PMCPMC7468227

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.