An automated external defibrillator in the home did not reduce all-cause mortality in patients at risk of cardiac arrest

Introduction: Survival after out-of-hospital cardiac arrest (OHCA) is dependent on early defibrillation. To increase bystander defibrillation in OHCAs, a first-responder program dispatching lay rescuers (Heart Runners) through a smartphone application (Heart Runner-app) was implemented in the Capital Region of Denmark. We investigated the proportion of Heart Runners arriving prior to the Emergency Medical Services (EMS) and rates of bystander defibrillation. Methods: The Capital Region of Denmark comprises 1.8 mil. inhabitants and 19,048 Heart Runners were registered. In cases of suspected OHCA, the Heart Runner-app was activated by the Emergency Medical Dispatch Center. Up to 20 Heart Runners < 1.8 km from the OHCA were dispatched to either start cardiopulmonary resuscitation (CPR) or to retrieve and use a publicly accessible automated external defibrillator (AED). Through an electronic survey, Heart Runners reported if they arrived before EMS and if they applied an AED. OHCAs where at least one Heart Runner arrived before EMS were compared with OHCAs where EMS arrived first. All OHCAs from September 2017 to May 2018, where Heart Runners had been dispatched, were included. Results: Of 399 EMS treated OHCAs, 78% (n=313/399) had a matching survey. A Heart Runner arrived before EMS in 47% (n=147/313) of the cases, and applied an AED in 41% (n=61/147) of these cases. Rate of bystander defibrillation was 2.5-fold higher compared to cases where the EMS arrived first (Table 1). Conclusions: By activation of the Heart Runner-app, Heart Runners arrived prior to EMS in nearly half of all the OHCA cases. Bystander defibrillation rate was significantly higher when Heart Runners arrived prior to EMS.

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TEMPERATURE ALTERATIONS IN PATIENTS AT RISK FOR BRAIN INJURY ASSOCIATED WITH CARDIAC ARREST, SUBARACHNOID HEMORRHAGE, OR CLOSED HEAD INJURY

Journal of Neurosurgical Anesthesiology ◽

10.1097/00008506-199610000-00034 ◽

1996 ◽

Vol 8 (4) ◽

pp. 324

Author(s):

R F Albrecht ◽

C T Wass ◽

W L Lanier

Keyword(s):

At Risk ◽

Cardiac Arrest ◽

Subarachnoid Hemorrhage ◽

Brain Injury ◽

Head Injury ◽

Closed Head Injury ◽

Closed Head ◽

Patients At Risk

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Changes in automated external defibrillator use and survival after out-of-hospital cardiac arrest in the Nijmegen area

Netherlands Heart Journal ◽

10.1007/s12471-018-1162-9 ◽

2018 ◽

Vol 26 (12) ◽

pp. 600-605 ◽

Cited By ~ 9

Author(s):

J. Nas ◽

J. Thannhauser ◽

J. J. Herrmann ◽

K. van der Wulp ◽

P. M. van Grunsven ◽

...

Keyword(s):

Cardiac Arrest ◽

Automated External Defibrillator ◽

Hospital Cardiac Arrest ◽

External Defibrillator

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Artificial Intelligence in Predicting Cardiac Arrest: A Scoping Review (Preprint)

10.2196/preprints.30798 ◽

2021 ◽

Author(s):

Asma Alamgir ◽

Osama Mousa 2nd ◽

Zubair Shah 3rd

Keyword(s):

Artificial Intelligence ◽

At Risk ◽

Cardiac Arrest ◽

Scoping Review ◽

Pediatric Population ◽

Data Extraction ◽

Warning System ◽

Reference List ◽

Evaluation Tool ◽

Patients At Risk

BACKGROUND Cardiac arrest is a life-threatening cessation of heart activity. Early prediction of cardiac arrest is important as it provides an opportunity to take the necessary measures to prevent or intervene during the onset. Artificial intelligence technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. OBJECTIVE This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. METHODS Scoping review was conducted in line with guidelines of PRISMA Extension for Scoping Review (PRISMA-ScR). Scopus, Science Direct, Embase, IEEE, and Google Scholar were searched to identify relevant studies. Backward reference list checking of included studies was also conducted. The study selection and data extraction were conducted independently by two reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. We were able to classify the approach taken by the studies in three different categories - 26 studies predicted cardiac arrest by analyzing specific parameters or variables of the patients while 16 studies developed an AI-based warning system. The rest of the 5 studies focused on distinguishing high-risk cardiac arrest patients from patients, not at risk. 2 studies focused on the pediatric population, and the rest focused on adults (n=45). The majority of the studies used datasets with a size of less than 10,000 (n=32). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (n=38) and the most used algorithm belonged to the neural network (n=23). K-Fold cross-validation was the most used algorithm evaluation tool reported in the studies (n=24). CONCLUSIONS : AI is extensively being used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in changing cardiac medicine for the better. There is a need for more reviews to learn the obstacles of implementing AI technologies in the clinical setting. Moreover, research focusing on how to best provide clinicians support to understand, adapt and implement the technology in their practice is also required.

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Abstract 147: Rapid Responder Operating Times for Out-of-hospital Cardiac Arrest Using Three Automated External Defibrillator Models

Circulation ◽

10.1161/circ.142.suppl_4.147 ◽

2020 ◽

Vol 142 (Suppl_4) ◽

Author(s):

Mengqi Gao ◽

Chenguang Liu ◽

Dawn Jorgenson

Keyword(s):

Cardiac Arrest ◽

Life Support ◽

Operating Time ◽

Sudden Cardiac Arrest ◽

The United States ◽

Automated External Defibrillator ◽

Simulation Studies ◽

Time Intervals ◽

Shock Delivery ◽

External Defibrillator

Background: Early defibrillation with an automated external defibrillator (AED) is crucial for improving the survival rate in out-of-hospital resuscitation from sudden cardiac arrest (SCA). Chance of survival decreases by 7% to 10% for every minute that defibrillation is delayed. While simulation studies have been used to assess AED usability factors, our objective was to report the actual operating time for three Philips AED models used in SCA responses. Methods: A convenience dataset recorded by Philips AEDs (HS1, FRx, or FR3) was obtained from Europe and the United States from 2007 - 2018. The HS1 is intended for minimally trained or untrained individuals, the FRx is for Basic Life Support (BLS), and the FR3 is for both BLS and Advanced Life Support (ALS) responders. A retrospective analysis was conducted to report the operating time intervals for cases where a shock was delivered after initial rhythm analysis. The study analyzed 90 HS1, 46 FRx and 32 FR3 cases. Results: Compared with HS1, both FRx (p < 0.001) and FR3 (p = 0.001) responders spent less time in placing pads on the patient after powering on the AED (Figure 1) as expected. Similarly, time intervals from the start of shock advised prompt to first shock delivery for FRx (p = 0.02) and FR3 (p < 0.01) are shorter than for HS1. Time from AED power-on to first shock was within 90 seconds in 74.4% (67 of 90) HS1 cases, 97.8% (45 of 46) FRx cases, and 100% (32 of 32) FR3 cases. On average, the FR3 and FRx responders were able to deliver the first shock within 48 seconds. Conclusions: The analysis shows that responders were able to quickly apply the AEDs and respond to the shock advisory prompt for all three AED models despite different training levels. This real-world performance is better than most reported simulation studies, however, this analysis cannot convey variety of activities that account for the differences in timing (e.g. pads applied before power-on, or compressions began before applying pads, etc.).

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What Do Adolescents Learn from a 50 Minute Cardiopulmonary Resuscitation/Automated External Defibrillator Education in a Rural Area: A Pre-Post Design

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16061053 ◽

2019 ◽

Vol 16 (6) ◽

pp. 1053

Author(s):

Ming-Fen Tsai ◽

Li-Hsiang Wang ◽

Ming-Shyan Lin ◽

Mei-Yen Chen

Keyword(s):

Cardiac Arrest ◽

Cardiopulmonary Resuscitation ◽

Education Program ◽

Individual Performance ◽

Automated External Defibrillator ◽

Quasi Experimental ◽

Rescue Time ◽

The Impact ◽

Hospital Cardiac Arrest ◽

External Defibrillator

Background: Literature indicates that patients who receive cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) from bystanders have a greater chance of surviving out-of-hospital cardiac arrest (OHCA). A few evaluative studies involving CPR/AED education programs for rural adolescents have been initiated. This study aimed to examine the impact of a 50 min education program that combined CPR with AED training in two rural campuses. Methods: A quasi-experimental pre-post design was used. The 50 min CPR/AED training and individual performance using a Resusci Anne manikin was implemented with seventh grade students between August and December 2018. Results: A total of 336 participants were included in this study. The findings indicated that the 50 min CPR/AED education program significantly improved participant knowledge of emergency responses (p < 0.001), correct actions at home (p < 0.01) and outside (p < 0.001) during an emergency, and willingness to perform CPR if necessary (p < 0.001). Many participants described that “I felt more confident to perform CPR/AED,” and that “It reduces my anxiety and saves the valuable rescue time.” Conclusions: The brief education program significantly improved the immediate knowledge of cardiac emergency in participants and empowered them to act as first responders when they witnessed someone experiencing a cardiac arrest. Further studies should consider the study design and explore the effectiveness of such brief programs.

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Identifying patients at risk of futile resuscitation: palliative care indicators in out-of-hospital cardiac arrest

BMJ Supportive & Palliative Care ◽

10.1136/bmjspcare-2019-001828 ◽

2019 ◽

pp. bmjspcare-2019-001828

Author(s):

Mia Cokljat ◽

Adam Lloyd ◽

Scott Clarke ◽

Anna Crawford ◽

Gareth Clegg

Keyword(s):

At Risk ◽

Palliative Care ◽

Cardiac Arrest ◽

High Risk ◽

Low Risk ◽

Intermediate Risk ◽

Patients At Risk ◽

Risk Patients ◽

Record Review ◽

Hospital Cardiac Arrest

ObjectivesPatients with indicators for palliative care, such as those with advanced life-limiting conditions, are at risk of futile cardiopulmonary resuscitation (CPR) if they suffer out-of-hospital cardiac arrest (OHCA). Patients at risk of futile CPR could benefit from anticipatory care planning (ACP); however, the proportion of OHCA patients with indicators for palliative care is unknown. This study quantifies the extent of palliative care indicators and risk of CPR futility in OHCA patients.MethodsA retrospective medical record review was performed on all OHCA patients presenting to an emergency department (ED) in Edinburgh, Scotland in 2015. The risk of CPR futility was stratified using the Supportive and Palliative Care Indicators Tool. Patients with 0–2 indicators had a ‘low risk’ of futile CPR; 3–4 indicators had an ‘intermediate risk’; 5+ indicators had a ‘high risk’.ResultsOf the 283 OHCA patients, 12.4% (35) had a high risk of futile CPR, while 16.3% (46) had an intermediate risk and 71.4% (202) had a low risk. 84.0% (68) of intermediate-to-high risk patients were pronounced dead in the ED or ED step-down ward; only 2.5% (2) of these patients survived to discharge.ConclusionsUp to 30% of OHCA patients are being subjected to advanced resuscitation despite having at least three indicators for palliative care. More than 80% of patients with an intermediate-to-high risk of CPR futility are dying soon after conveyance to hospital, suggesting that ACP can benefit some OHCA patients. This study recommends optimising emergency treatment planning to help reduce inappropriate CPR attempts.

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