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Enregistrement W3185798185 · doi:10.29173/cjen149

The clinical effects of CPR meter on chest compression quality: a QI project

2021· article· en· W3185798185 sur OpenAlexaffvenue
Christopher Picard, Richard H. Drew, Domhnall O’Dochartaigh, Matthew J. Douma, Candice Keddie, Colleen M. Norris

Notice bibliographique

RevueCanadian Journal of Emergency Nursing · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueCardiac Arrest and Resuscitation
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMedicineMetreQuality (philosophy)Compression (physics)Internal medicineEnvironmental scienceMaterials science

Résumé

récupéré en direct d'OpenAlex

The clinical effects of CPR meter on chest compression quality: a QI project. Christopher Picard, Richard Drew, Domhnall O’Dochartaigh, Matthew J Douma, Candice Keddie, Colleen Norris. Background: High-quality chest compressions are the cornerstone of resuscitation. Training guidelines require CPR feedback, and pre-clinical data shows that feedback devices improve chest compression quality; but devices are not being used in many emergency departments, and their impact on clinical care is less well understood. Some services use defibrillator generated reports for quality improvement, but these measurements may be limited in scope and have not been rigorously compared to other tools. Methods: Laerdal CPRMeter 2 chest compression feedback devices were purchased using funds made available by a zone QI initiative. Initial training for implementation consisted of staff performing one minute of blinded chest compression using the feedback device, followed by one minute of chest compression unblinded. Staff were shown the raw percentage of chest compressions meeting target depth, release, and rate under both conditions as well as overall improvement. Following initial orientation, devices were incorporated into clinical care and all subsequent staff simulation and training. Clinically, use of the feedback device and completion or QI tracking forms was not mandated but was encouraged by drawing code participant names from completed forms for a free ACLS or PALS course. Data from all codes were automatically collected by the LifePak 20, data from any resuscitation using the Laerdal CPRmeter 2 were also automatically recorded when the device was used: these data were downloaded weekly. Completed questionnaire forms were submitted to the Clinical Educators and extracted as received. Evaluation Methods: Chest compression quality data was collected in two ways: first, using a Laerdal CPRMeter2, second, by downloading and analyzing cardiac arrest data from a LifePak20 defibrillator using CodeStatTM software. Device data were matched and synthesized by an emergency department CNE using Microsoft excel and IBM SPSS 26. Descriptive statistics (mean and standard deviations) are used to describe the data. Differences in chest compression quality and duration of resuscitations between resuscitation that did or did not use a feedback device or a backboard were compared using independent t-testing. Differences in chest compressions at the target depth, release, and rate between the numbers of staff involved were assessed using ANOVA. Agreement between devices (CPRMeter2 and LifePak) used during the resuscitations were evaluated using paired t-testing, Pearson correlations, and Bland-Altman plots. All tests were two-tailed with predetermined significance levels set at a=0.05. Results: Data collection occurred between August 2019 and December 2020. There were a total of 50 cardiac arrests included, 36 had questionnaire data returned, 36 had data collected from the CPR meter 2, 24 had data collected from the LifePak, and 10 had data collected using all three methods. The average duration of resuscitation (number of chest compressions) was 1079.56 (SD=858.25); there was no difference in the duration of resuscitation (number of chest compressions) between resuscitations using versus not using CPR feedback devices (p=0.673). Resuscitations utilizing chest compression feedback had a higher percentage of chest compressions at the target rate compared to resuscitations not using feedback (74.08% vs 42.18%, p=0.007). Resuscitations that utilized a backboard had a higher percentage of chest compressions at target depth (72.92% vs 48.73%, p=0.048). There were no differences noted in the duration of resuscitation attempt (p=0.167) or percentages of chest compressions at the target depth (p=0.181), release (p=0.538), or rate (p=0.656) between resuscitations with different sized teams (4-5, 6-7, 8-9, >10 staff involved). There was a strong positive correlation (r=0.771, p=0.005, n=11) between the two measurement methods and chest compression rates, and no statistically significant difference in measured scores (p=0.999), with 100% of values falling within the Bland-Altman confidence intervals of 36.72 and -36.72, n=11. Interpretation of the levels of agreement between these two device measures methods should be done cautiously however, given the small sample size and wide confidence intervals. Implications 1) Incorporation of visual chest compression feedback and use of a backboard are fast andaffordable and significantly improved the percentage of chest compression at the target rateand depth. 2) There was no correlation between the size of the resuscitation team and the percentage ofchest compressions at the target depth, release or rate; nor was the feedback device useassociated with the duration of the resuscitation attempt. 3) The implications of improvement with the CPR meter suggests that areas or service not usingfeedback should consider implementing its use to achieve the target compression rate. 4) Compared to LifePak feedback alone the CPRMeter2 will also allow services to target depthand release targets as well as rate.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,678
Score d'incertitude au seuil0,237

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,085
Tête enseignante GPT0,433
Écart entre enseignants0,348 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2021
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueCanadian Journal of Emergency NursingMême sujetCardiac Arrest and ResuscitationTravaux en français237 207