Evidence map›Paper›PMID 39376834›Full record

ArticleCureus2024

Improving Cardiopulmonary Resuscitation Quality and Resuscitation Training by Using Real-Time Audiovisual-Assisted Devices.

Jerin Varghese, Abin Thomas, Bharath S Prasad, Sreekrishnan Trikkur, Sabarish Nair, Gireesh Kumar, Naveen Mohan, Manna M Theresa

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Jerin VargheseDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Kochi, IND.
Abin ThomasDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Kochi, IND.
Bharath S PrasadDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Kochi, IND.
Sreekrishnan TrikkurDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Kochi, IND.
Sabarish NairDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Kochi, IND.
Gireesh KumarDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Kochi, IND.
Naveen MohanDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Kochi, IND.
Manna M TheresaDepartment of Emergency Medicine, Amrita Institute of Medical Sciences, Kochi, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Context Cardiac arrest occurring outside of a medical facility is a significant cause of death and disability worldwide. In developing nations, it accounts for a notable portion of total mortality, with only a small percentage of those affected surviving beyond the initial emergency department intervention. Despite the critical importance of high-quality cardiopulmonary resuscitation (CPR) in these situations, there has been limited research on the effectiveness of audiovisual feedback devices in improving CPR performance among laypersons or non-medical caregivers. These caregivers, often untrained in advanced medical procedures, play a crucial role in the immediate response to cardiac arrest before professional help arrives. This study aims to address this gap by evaluating the impact of such devices on CPR quality delivered by non-medical individuals. Aim This study aimed to determine whether the use of audiovisual devices would improve cardiopulmonary resuscitation performance among non-medical care providers. Materials and methods Using a multiple-choice questionnaire, an audiovisual aid-based prospective observational study (non-interventional observation study) was conducted at a medical college hospital in Kochi from June 2022 to February 2024. A minimum sample size of 66 was derived from pilot studies, with 95% confidence and 90% power. A total of 146 participants met the inclusion criteria (non-medical personnel of 18-50 years of age). After the exclusion of pregnant women and non-interested participants, the study participants were analyzed for the quality of cardiopulmonary resuscitation in a mannequin with the help of audiovisual devices. Statistical analysis was conducted using IBM SPSS Statistics for Windows, Version 20 (IBM Corp., Armonk, New York, released in 2011). Categorical variables were expressed as frequency and percentage. McNemar's Chi-square test was applied to compare the CPR compression rates with and without the visual feedback device, and the kappa statistic was used to assess how consistently participants performed within the same compression rate category (less than 100, 100-120, or more than 120 compressions per minute) with and without the feedback device. Results The improvement in CPR quality, which was visualized as a green color in the CPR feedback device, was significant, with 109 participants (74.7%) showing good outcomes. The chest compression rate also significantly improved from 95 to 117 with the use of feedback devices (p -0.011, Kappa - 0.167) among non-medical care providers. While the kappa value suggests that some variability exists in compression rates when switching between with and without feedback, the overall improvement is still noteworthy. Conclusion The majority of participants (74.7%) were able to consistently maintain green light in the visual feedback watch, which means their chest compression rate was within optimal range (100-120/min) when using the audiovisual feedback device. This indicated that use of audiovisual devices significantly improved compression rates among non-medical care providers and effectively helped them perform high-quality CPR.

Indexed as

audiovisual devicescardiac arrestcprfeedback devicesnon medical care providers

Identifiers

PMID39376834
PMCPMC11456418

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Registered trials

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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.