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Modeling a novel hypothetical use of postal collection boxes as automated external defibrillator access points.

ABSTRACT: INTRODUCTION:Optimizing placement of Automated External Defibrillators (AED) can increase survival after an out-of-hospital cardiac arrest (OHCA). Using postal collection boxes (PCB) as locations for AEDs could potentially enhance accessibility and streamline maintenance. In this study, we modeled the hypothetical effects of deploying AEDs at PCB locations. HYPOTHESIS:We hypothesized that PCB-AEDs would increase AED coverage overall and in residential areas, and reduce the distance from OHCA to an AED. METHODS:AEDs in Pittsburgh, PA were identified by the University of Pittsburgh Resuscitation Logistics and Informatics Venture (n=747). PCB locations were obtained from the United States Postal Service (n=479). OHCA locations from 2009 to 2014 were obtained from the Pittsburgh site of the Resuscitation Outcomes Consortium. AED coverage assuming a ¼ mile radius around each AED was estimated for known AEDs, PCB-AEDs (hypothetical AED locations), and known AEDs augmented by PCB-AEDs, both overall and for residential and non-residential zones. Linear distance from each OHCA to the nearest AED was calculated and compared between the sets. RESULTS:The set of known AEDs augmented with PCB-AEDs covered more of the city overall (55% vs 30%), as well as greater proportions of residential (62% vs 27%) and non-residential areas (45% vs 30%). The median distance from OHCA to AED was significantly shorter when known AEDs were augmented with PCB-AEDs (0.12mi vs 0.32mi; p=0.001). CONCLUSION:Augmenting existing publicly accessible AEDs with AEDs deployed at PCBs can increase AED spatial coverage in both residential and non-residential areas, and reduce the distance from AED to OHCA.

SUBMITTER: Srinivasan S

PROVIDER: S-EPMC5730075 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Modeling a novel hypothetical use of postal collection boxes as automated external defibrillator access points.

Srinivasan Sanjana S Salerno Jessica J Hajari Hadi H Weiss Lenny S LS Salcido David D DD

Resuscitation 20170825

<h4>Introduction</h4>Optimizing placement of Automated External Defibrillators (AED) can increase survival after an out-of-hospital cardiac arrest (OHCA). Using postal collection boxes (PCB) as locations for AEDs could potentially enhance accessibility and streamline maintenance. In this study, we modeled the hypothetical effects of deploying AEDs at PCB locations.<h4>Hypothesis</h4>We hypothesized that PCB-AEDs would increase AED coverage overall and in residential areas, and reduce the distanc ...[more]

PMID: 28847755

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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.

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.

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Modeling a novel hypothetical use of postal collection boxes as automated external defibrillator access points.

Publications

Modeling a novel hypothetical use of postal collection boxes as automated external defibrillator access points.

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