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Inverse Association Between Bystander Use of Audiovisual Feedback From an Automated External Defibrillator and Return of Spontaneous Circulation.

ABSTRACT: Background Treatment with an automated external defibrillator (AED) improves outcome in out-of-hospital cardiac arrest (OHCA). Audiovisual feedback from an AED may assist bystanders achieve higher quality cardiopulmonary resuscitation. However, the association between audiovisual feedback and clinical outcomes is not well assessed in real-life OHCA. The aim of this study was to assess the association between audiovisual feedback from an AED used in bystander resuscitation with rates of return of spontaneous circulation (ROSC) and 30-day survival in a real-life cohort of patients with OHCA. Methods and Results We included 325 patients treated with bystander AED use before arrival of emergency medical services during 2016 to 2019 from the Capital Region of Denmark. Patients were divided into a "feedback" and a "nonfeedback" group, depending on presence of audiovisual feedback from the AED. Audiovisual feedback was defined as voice prompts with continuous feedback to ongoing resuscitation. Rates of ROSC upon hospital admission and 30-day survival were assessed, and univariate and multivariable models were applied to decide the association to audiovisual feedback. Multivariable models were adjusted for sex, age, primary heart rhythm, and location of OHCA. A total of 155 (48%) patients had a bystander AED applied with audiovisual feedback and 170 (52%) without audiovisual feedback. A lower rate of ROSC was found in the feedback group compared with the nonfeedback group (33% [n=51] versus 45% [n=76]; P=0.03). No association was observed between AV feedback and 30-day survival (feedback=27% [n=42] and nonfeedback=31% [n=53]; P=0.49). In the unadjusted logistic regression model, audiovisual feedback was associated with a decreased chance of ROSC (odds ratio, 0.61; 95% CI, 0.38-0.95; P=0.03), which remained significant after adjusted analysis (odds ratio, 0.53; 95% CI, 0.29-0.97; P=0.04), whereas we found no significant association between audiovisual feedback and 30-day survival in the unadjusted and adjusted analyses. Conclusions Audiovisual feedback from an AED used by bystanders was associated with a lower chance of ROSC at hospital admission, but we found no significant difference in 30-day survival. Focus on early and correct bystander cardiopulmonary resuscitation and AED use remain key for OHCA survival.

SUBMITTER: Obling L

PROVIDER: S-EPMC9245825 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Survival following out-of-hospital cardiac arrest (OHCA) with shockable rhythms can be improved with early defibrillation. Although shockable OHCA accounts for only ?25% of overall arrests, ?60% of public OHCAs are shockable, offering the possibility of restoring thousands of individuals to full recovery with early defibrillation by bystanders. We sought to determine the association of bystander automated external defibrillator use with survival and functional outcomes in shockable observed public OHCA. METHODS:From 2011 to 2015, the Resuscitation Outcomes Consortium prospectively collected detailed information on all cardiac arrests at 9 regional centers. The exposures were shock administration by a bystander-applied automated external defibrillator in comparison with initial defibrillation by emergency medical services. The primary outcome measure was discharge with normal or near-normal (favorable) functional status defined as a modified Rankin Score ?2. Survival to hospital discharge was the secondary outcome measure. RESULTS:Among 49?555 OHCAs, 4115 (8.3%) observed public OHCAs were analyzed, of which 2500 (60.8%) were shockable. A bystander shock was applied in 18.8% of the shockable arrests. Patients shocked by a bystander were significantly more likely to survive to discharge (66.5% versus 43.0%) and be discharged with favorable functional outcome (57.1% versus 32.7%) than patients initially shocked by emergency medical services. After adjusting for known predictors of outcome, the odds ratio associated with a bystander shock was 2.62 (95% confidence interval, 2.07-3.31) for survival to hospital discharge and 2.73 (95% confidence interval, 2.17-3.44) for discharge with favorable functional outcome. The benefit of bystander shock increased progressively as emergency medical services response time became longer. CONCLUSIONS:Bystander automated external defibrillator use before emergency medical services arrival in shockable observed public OHCA was associated with better survival and functional outcomes. Continued emphasis on public automated external defibrillator utilization programs may further improve outcomes of OHCA.

Project description:Background Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out-of-hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ECGs. In out-of-hospital cardiac arrest, an algorithm within an automated external defibrillator is the major determinant to deliver defibrillation. This study developed and validated the performance of a convolution neural network (CNN) to diagnose shockable arrhythmias within a novel, miniaturized automated external defibrillator. Methods and Results There were 26 464 single-lead ECGs that comprised the study data set. ECGs of 7-s duration were retrospectively adjudicated by 3 physician readers (N=18 total readers). After exclusions (N=1582), ECGs were divided into training (N=23 156), validation (N=721), and test data sets (N=1005). CNN performance to diagnose shockable and nonshockable rhythms was reported with area under the receiver operating characteristic curve analysis, F1, and sensitivity and specificity calculations. The duration for the CNN to output was reported with the algorithm running within the automated external defibrillator. Internal and external validation analyses included CNN performance among arrhythmias, often mistaken for shockable rhythms, and performance among ECGs modified with noise to mimic artifacts. The CNN algorithm achieved an area under the receiver operating characteristic curve of 0.995 (95% CI, 0.990-1.0), sensitivity of 98%, and specificity of 100% to diagnose shockable rhythms. The F1 scores were 0.990 and 0.995 for shockable and nonshockable rhythms, respectively. After input of a 7-s ECG, the CNN generated an output in 383±29 ms (total time of 7.383 s). The CNN outperformed adjudicators in classifying atrial arrhythmias as nonshockable (specificity of 99.3%-98.1%) and was robust against noise artifacts (area under the receiver operating characteristic curve range, 0.871-0.999). Conclusions We demonstrate high diagnostic performance of a CNN algorithm for shockable and nonshockable rhythm arrhythmia classifications within a digitally connected automated external defibrillator. Registration URL: https://clinicaltrials.gov/ct2/show/NCT03662802; Unique identifier: NCT03662802.

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Inverse Association Between Bystander Use of Audiovisual Feedback From an Automated External Defibrillator and Return of Spontaneous Circulation.

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