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AIM OF THE STUDY: We evaluated whether an artificial intelligence (AI)-driven robot cardiopulmonary resuscitation (CPR) could improve hemodynamic parameters and clinical outcomes. METHODS: We developed an AI-driven CPR robot which utilizes an integrated feedback system with an AI model predicting carotid blood flow (CBF). Twelve pigs were assigned to the AI robot group (n = 6) and the LUCAS 3 group (n = 6). They underwent 6 min of CPR after 7 min of ventricular fibrillation. In the AI robot group, the robot explored for the optimal compression position, depth and rate during the first 270-second period, and continued CPR with the optimal setup during the next 90-second period and beyond. The primary outcome was CBF during the last 90-second period. The secondary outcomes were coronary perfusion pressure (CPP), end-tidal carbon dioxide level (ETCO2) and return of spontaneous circulation (ROSC). RESULTS: The AI model's prediction performance was excellent (Pearson correlation coefficient = 0.98). CBF did not differ between the two groups [estimate and standard error (SE), -23.210 ± 20.193, P = 0.250]. CPP, ETCO2 level and rate of ROSC also did not show difference [estimate and SE, -0.214 ± 7.245, P = 0.976 for CPP; estimate and SE, 1.745 ± 3.199, P = 0.585 for ETCO2; 5/6 (83.3%) vs. 4/6 (66.7%), P = 1.000 for ROSC). CONCLUSION: This study provides proof of concept that an AI-driven CPR robot in porcine cardiac arrest is feasible. Compared to a LUCAS 3, an AI-driven CPR robot produced comparable hemodynamic and clinical outcomes.

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Background and Objectives: As the first three links of the chain of survival of victims of cardiac arrest depend on prompt action by bystanders, it is important to educate as much of the population as possible about basic life support and use of an automatic external defibrillator (BLS and AED). Schoolchildren are an accessible population that can be easily taught and numerous BLS and AED courses are available. The aim of this study was to assess the effectiveness of two different practical approaches to teaching BLS and AED. Material and Methods: We compared two different BLS and AED courses (course A and B) offered to 280 eighth- and ninth-grade students in primary schools. Knowledge about and the intention to perform BLS and AED were evaluated using validated questionnaires before and after the courses. Descriptive methods were used to describe the results. To compare courses, we used the Mann-Whitney U test. A p value of <0.05 was considered statistically significant. Results: Differences in knowledge and intention to perform BLS and AED after the courses were significant between courses (p < 0.001 and p = 0.037, respectively). After course A, students demonstrated significantly better knowledge and numerically greater intention to perform BLS and AED (intention score 6.55 ± 0.61 out of 7). Conclusions: Courses in which students have the opportunity to individually practice BLS skills show a greater increase in knowledge and in intention to perform BLS and AED.

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The objective of this study was to develop a sound recognition-based cardiopulmonary resuscitation (CPR) training system that is accessible, cost-effective, easy-to-maintain and provides accurate CPR feedback. Beep-CPR, a novel device with accordion squeakers that emit high-pitched sounds during compression, was developed. The sounds emitted by Beep-CPR were recorded using a smartphone, segmented into 2-second audio fragments, and then transformed into spectrograms. A total of 6,065 spectrograms were generated from approximately 40 minutes of audio data, which were then randomly split into training, validation, and test datasets. Each spectrogram was matched with the depth, rate, and release velocity of the compression measured at the same time interval by the ZOLL X Series monitor/defibrillator. Deep learning models utilizing spectrograms as input were trained using transfer learning based on EfficientNet to predict the depth (Depth model), rate (Rate model), and release velocity (Recoil model) of compressions. Results: The mean absolute error (MAE) for the Depth model was 0.30 cm (95% confidence interval [CI]: 0.27-0.33). The MAE of the Rate model was 3.6/min (95% CI: 3.2-3.9). For the Recoil model, the MAE was 2.3 cm/s (95% CI: 2.1-2.5). External validation of the models demonstrated acceptable performance across multiple conditions, including the utilization of a newly-manufactured device, a fatigued device, and evaluation in an environment with altered spatial dimensions. We have developed a novel sound recognition-based CPR training system, that accurately measures compression quality during training. Significance: Beep-CPR is a cost-effective and easy-to-maintain solution that can improve the efficacy of CPR training by facilitating decentralized at-home training with performance feedback.

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Use of onboard commercial airline defibrillators began in 1997. At first, it was met with resistance but is now present on all planes. The first in-flight resuscitation of a passenger occurred in 1998 and is described here.

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INTRODUCTION: Cardiac arrest (CA) is the third leading cause of death, with persistently low survival rates despite medical advancements. This article evaluates the potential of emerging technologies to enhance CA management over the next decade, using predictions from the AI tools ChatGPT-4 and Gemini Advanced. METHODS: We conducted an exploratory literature review to envision the future of cardiopulmonary arrest (CA) management. Utilizing ChatGPT-4 and Gemini Advanced, we predicted implementation timelines for innovations in early recognition, CPR, defibrillation, and post-resuscitation care. We also consulted the AI to assess the consistency and reproducibility of the predictions. RESULTS: We extrapolate that healthcare may embrace new technologies, such as comprehensive monitoring of vital signs to activate the emergency system (wireless detectors, smart speakers, and wearable devices), use new innovative early CPR and early AED devices (robot CPR, wearable AEDs, and immersive reality), and post-resuscitation care monitoring (brain-computer interface). These technologies could enhance timely life-saving interventions for cardiac arrest. However, there are many ethical and practical challenges, particularly in maintaining patient privacy and equity. The two AI tools made different predictions, with a horizon for implementation ranging between three and eight years. CONCLUSION: Integrating advanced monitoring technologies and AI-driven tools offers hope in improving CA management. A balanced approach involving rigorous scientific validation and ethical oversight is necessary. Collaboration among technologists, medical professionals, ethicists, and policymakers is crucial to use these innovations ethically to reduce CA incidence and enhance outcomes. Further research is needed to enhance the reliability of AI predictive capabilities.

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The use of audiovisual feedback devices to guide the quality of chest compressions during cardiopulmonary resuscitation has increased in recent years. Audiovisual feedback devices can be classified as integrated (eg, Zoll AED Plus defibrillator) or standalone (eg, CPRmeter). This study aimed to explore users' needs and factors affecting the acceptability of audiovisual feedback devices. Semistructured interviews were conducted with healthcare professionals involved in lifesaving activities. The Unified Theory of Acceptance and Use of Technology was used as a theoretical framework for the study. The Unified Theory of Acceptance and Use of Technology model has four constructs: performance expectancy, effort expectancy, social influence, and facilitating factors. Ten themes were identified under the four constructs. The performance expectancy constructs include three themes: perceived usefulness, outcome expectation, and applicability in diverse situations. The effort expectancy construct encompasses two themes: user-friendliness and complexity. The social influence construct has two themes: social and organizational factors. Lastly, the facilitating factors construct includes three themes: staff competence, perceived cost, and compatibility of devices. Exploring the needs and factors influencing the acceptability of audiovisual feedback devices used during cardiopulmonary resuscitation will inform healthcare providers, managers, manufacturers, and procurers on how to improve the efficiency and use of these devices.

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BACKGROUND: Out-of-hospital cardiac arrest is a life-threatening condition that requires immediate intervention to increase the prospect of survival. There are various ways to achieve cardiopulmonary resuscitation in such patients, either through manual chest compression or mechanical chest compression. Thus, we performed a systematic review and meta-analysis to investigate the differences between these interventions. METHODS: PubMed, Cochrane Library, and Scopus were explored from inception to May 2023. Additionally, the bibliographies of relevant studies were searched. The Cochrane Risk of Bias Tool for Randomized Controlled Trials, Newcastle-Ottawa Scale, and the Risk of Bias in Non-Randomized Studies-I tools were utilized to perform quality and risk of bias assessments. RESULTS: There were 24 studies included within this quantitative synthesis, featuring a total of 111,681 cardiac arrest patients. Overall, no statistically significant differences were observed between the return of spontaneous circulation, survival to hospital discharge, short-term survival, and long-term survival. However, manual chest compression was associated with a significantly superior favorability of neurological outcomes (OR: 1.41; 95% CI: 1.07, 1.84; P = .01). CONCLUSION: Although there were no major differences between the strategies, the poorer post-resuscitation neurological outcomes observed in mechanical chest compression indicate the need for further innovation and advancements within the current array of mechanical devices. However, future high-quality studies are necessary in order to arrive at a valid conclusion.

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Left ventricular (LV) unloading has been shown to improve survival for patients requiring veno-arterial extracorporeal membrane oxygenation (VA ECMO) support for cardiogenic shock. A mortality benefit has been shown for ECMO and concomitant placement of a transcatheter unloading LV pump such as an Impella device (colloquially referred to as ECPELLA or ECMELLA) for patients resuscitated with VA ECMO after a short period of cardiac arrest. Despite the described benefit of LV unloading with VA ECMO for cardiopulmonary resuscitation, it remains unclear as to what criteria should be used and what other diagnostic and therapeutic adjuncts may be useful. We describe here the successful utilization of concomitant VA ECMO and Impella in a 43 year old male with acute heart failure and cardiac arrest. Distinguishing itself from the currently reported methods, our methodology incorporates transesophageal echocardiography (TEE) in the emergency department for rapid decision-making in addition to an automatic chest compression device, the Lund University Cardiac Assist System (LUCAS) device (Stryker, Portage, MI) as a bridge to LV unloading in a hybrid operating suite.

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BACKGROUND: High-quality chest compressions (CC) are an important factor of neonatal resuscitation. Mechanical CC devices may provide superior CC delivery and improve resuscitation outcomes. We aimed to compare the hemodynamic effects of CC delivered by machine and human using a neonatal piglet model. METHODS: Twelve asphyxiated piglets were randomized to receive CC during resuscitation using an automated mechanical CC device ("machine") or the two-thumb encircling technique ("human"). CC was superimposed with sustained inflations. RESULTS: Twelve newborn piglets (age 0-3 days, weight 2.12 ± 0.17 kg) were included in the study. Machine-delivered CC resulted in an increase in stroke volume, and minimum and maximum rate of left ventricle pressure change (dp/dtmin and dp/dtmax) compared to human-delivered CC. CONCLUSIONS: During machine-delivered CC, stroke volume and left ventricular contractility were significantly improved. Mechanical CC devices may provide improved cardiopulmonary resuscitation outcomes in neonatal cardiac arrest induced by asphyxia. IMPACT: Machine chest compression leads to changes in hemodynamic parameters during resuscitation of asphyxiated neonatal piglets, namely greater stroke volume and left ventricular contractility, compared with standard two-thumb compression technique. Mechanical chest compression devices may provide improved cardiopulmonary resuscitation outcomes in neonatal and pediatric asphyxia-induced cardiac arrest.

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This JAMA Insights Clinical Update discusses the newer treatment option of extracorporeal cardiopulmonary resuscitation, particularly for patients with cardiac arrest who are not responsive to initial treatment.

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En el presente artículo de la serie «Seguridad en procedimientos dermatológicos» se aborda la reanimación cardiopulmonar básica (sin empleo de ningún dispositivo), instrumentalizada (usando un desfibrilador externo automático) y farmacológica (haciendo uso de adrenalina). Se plantean las recomendaciones actualizadas en 2021 del Consejo Europeo de Resucitación de forma resumida, con material gráfico que las sistematiza de forma estructurada (AU)

This article in the series on safety in dermatologic procedures covers the delivery of basic cardiopulmonary resuscitation (using no devices), instrumental resuscitation (using an automated external defibrillator), and pharmacological resuscitation (using adrenaline). We provide a brief overview of the updated 2021 European Resuscitation Council guidelines and offer an algorithm and visual aids to support recommended practices (AU)

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This article in the series on safety in dermatologic procedures covers the delivery of basic cardiopulmonary resuscitation (using no devices), instrumental resuscitation (using an automated external defibrillator), and pharmacological resuscitation (using adrenaline). We provide a brief overview of the updated 2021 European Resuscitation Council guidelines and offer an algorithm and visual aids to support recommended practices (AU)

En el presente artículo de la serie «Seguridad en procedimientos dermatológicos» se aborda la reanimación cardiopulmonar básica (sin empleo de ningún dispositivo), instrumentalizada (usando un desfibrilador externo automático) y farmacológica (haciendo uso de adrenalina). Se plantean las recomendaciones actualizadas en 2021 del Consejo Europeo de Resucitación de forma resumida, con material gráfico que las sistematiza de forma estructurada (AU)

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The Covid-19 outbreak challenged health systems around the world to design and implement cost-effective devices produced locally to meet the increased demand of mechanical ventilators worldwide. This study evaluates the physiological responses of healthy swine maintained under volume- or pressure-controlled mechanical ventilation by a mechanical ventilator implemented to bring life-support by automating a resuscitation bag and closely controlling ventilatory parameters. Physiological parameters were monitored in eight sedated animals (t0) prior to inducing deep anaesthesia, and during the next six hours of mechanical ventilation (t1-7). Hemodynamic conditions were monitored periodically using a portable gas analyser machine (i.e. BEecf, carbonate, SaO2, lactate, pH, PaO2, PaCO2) and a capnometer (i.e. ETCO2). Electrocardiogram, echocardiography and lung ultrasonography were performed to detect in vivo alterations in these vital organs and pathological findings from necropsy were reported. The mechanical ventilator properly controlled physiological levels of blood biochemistry such as oxygenation parameters (PaO2, PaCO2, SaO2, ETCO2), acid-base equilibrium (pH, carbonate, BEecf), and perfusion of tissues (lactate levels). In addition, histopathological analysis showed no evidence of acute tissue damage in lung, heart, liver, kidney, or brain. All animals were able to breathe spontaneously after undergoing mechanical ventilation. These preclinical data, supports the biological safety of the medical device to move forward to further evaluation in clinical studies.

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