KIDS SAVE LIVES: Basic Life Support Education for Schoolchildren: A Narrative Review and Scientific Statement From the International Liaison Committee on Resuscitation.

BACKGROUND: Basic life support education for schoolchildren has become a key initiative to increase bystander cardiopulmonary resuscitation rates. Our objective was to review the existing literature on teaching schoolchildren basic life support to identify the best practices to provide basic life support training in schoolchildren. METHODS: After topics and subgroups were defined, a comprehensive literature search was conducted. Systematic reviews and controlled and uncontrolled prospective and retrospective studies containing data on students <20 years of age were included. RESULTS: Schoolchildren are highly motivated to learn basic life support. The CHECK-CALL-COMPRESS algorithm is recommended for all schoolchildren. Regular training in basic life support regardless of age consolidates long-term skills. Young children from 4 years of age are able to assess the first links in the chain of survival. By 10 to 12 years of age, effective chest compression depths and ventilation volumes can be achieved on training manikins. A combination of theoretical and practical training is recommended. Schoolteachers serve as effective basic life support instructors. Schoolchildren also serve as multipliers by passing on basic life support skills to others. The use of age-appropriate social media tools for teaching is a promising approach for schoolchildren of all ages. CONCLUSIONS: Schoolchildren basic life support training has the potential to educate whole generations to respond to cardiac arrest and to increase survival after out-of-hospital cardiac arrest. Comprehensive legislation, curricula, and scientific assessment are crucial to further develop the education of schoolchildren in basic life support.

Algor-ethics: charting the ethical path for AI in critical care.

The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particularly in critical care, where physicians often deal with life-threating conditions requiring rapid actions and patients unable to participate in the decisional process. Moreover, development of AI-based CDSS is complex and should address different sources of bias, including data acquisition, health disparities, domain shifts during clinical use, and cognitive biases in decision-making. In this scenario algor-ethics is mandatory and emphasizes the integration of 'Human-in-the-Loop' and 'Algorithmic Stewardship' principles, and the benefits of advanced data engineering. The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.

The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives.

Within the domain of Natural Language Processing (NLP), Large Language Models (LLMs) represent sophisticated models engineered to comprehend, generate, and manipulate text resembling human language on an extensive scale. They are transformer-based deep learning architectures, obtained through the scaling of model size, pretraining of corpora, and computational resources. The potential healthcare applications of these models primarily involve chatbots and interaction systems for clinical documentation management, and medical literature summarization (Biomedical NLP). The challenge in this field lies in the research for applications in diagnostic and clinical decision support, as well as patient triage. Therefore, LLMs can be used for multiple tasks within patient care, research, and education. Throughout 2023, there has been an escalation in the release of LLMs, some of which are applicable in the healthcare domain. This remarkable output is largely the effect of the customization of pre-trained models for applications like chatbots, virtual assistants, or any system requiring human-like conversational engagement. As healthcare professionals, we recognize the imperative to stay at the forefront of knowledge. However, keeping abreast of the rapid evolution of this technology is practically unattainable, and, above all, understanding its potential applications and limitations remains a subject of ongoing debate. Consequently, this article aims to provide a succinct overview of the recently released LLMs, emphasizing their potential use in the field of medicine. Perspectives for a more extensive range of safe and effective applications are also discussed. The upcoming evolutionary leap involves the transition from an AI-powered model primarily designed for answering medical questions to a more versatile and practical tool for healthcare providers such as generalist biomedical AI systems for multimodal-based calibrated decision-making processes. On the other hand, the development of more accurate virtual clinical partners could enhance patient engagement, offering personalized support, and improving chronic disease management.

How technology can save lives in cardiac arrest.

PURPOSE OF REVIEW: Technology is being increasingly implemented in the fields of cardiac arrest and cardiopulmonary resuscitation. In this review, we describe how recent technological advances have been implemented in the chain of survival and their impact on outcomes after cardiac arrest. Breakthrough technologies that are likely to make an impact in the future are also presented. RECENT FINDINGS: Technology is present in every link of the chain of survival, from prediction, prevention, and rapid recognition of cardiac arrest to early cardiopulmonary resuscitation and defibrillation. Mobile phone systems to notify citizen first responders of nearby out-of-hospital cardiac arrest have been implemented in numerous countries with improvement in bystanders' interventions and outcomes. Drones delivering automated external defibrillators and artificial intelligence to support the dispatcher in recognising cardiac arrest are already being used in real-life out-of-hospital cardiac arrest. Wearables, smart speakers, surveillance cameras, and artificial intelligence technologies are being developed and studied to prevent and recognize out-of-hospital and in-hospital cardiac arrest. SUMMARY: This review highlights the importance of technology applied to every single step of the chain of survival to improve outcomes in cardiac arrest. Further research is needed to understand the best role of different technologies in the chain of survival and how these may ultimately improve outcomes.

Cardiac Arrest in a Young Woman: A Near Miss Diagnosis.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a rare channelopathy involving cardiac calcium metabolism that often shows up at an early age with misleading clinical symptoms such as emotion or exercise-related syncope with a normal resting ECG, however, if misdiagnosed, CPVT can lead to cardiac arrest in children or young adults. We describe the case of a 27-year-old woman with several misdiagnosed syncopal episodes leading to out-of-hospital cardiac arrest (OHCA). Her previous medical history, combined with automatic external defibrillator records (AED) and clinical data, strongly suggested the diagnosis of CPVT. Thus beta blocker therapy was immediately started and targeted genetic test undertaken, revealing a previously unreported heterozygous variant in the ryanodine receptor-2 (RYR2) gene.

Adult Basic Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations.

This 2020 International Consensus on Cardiopulmonary Resuscitation (CPR) and Emergency Cardiovascular Care Science With Treatment Recommendations on basic life support summarizes evidence evaluations performed for 22 topics that were prioritized by the Basic Life Support Task Force of the International Liaison Committee on Resuscitation. The evidence reviews include 16 systematic reviews, 5 scoping reviews, and 1 evidence update. Per agreement within the International Liaison Committee on Resuscitation, new or revised treatment recommendations were only made after a systematic review. Systematic reviews were performed for the following topics: dispatch diagnosis of cardiac arrest, use of a firm surface for CPR, sequence for starting CPR (compressions-airway-breaths versus airway-breaths-compressions), CPR before calling for help, duration of CPR cycles, hand position during compressions, rhythm check timing, feedback for CPR quality, alternative techniques, public access automated external defibrillator programs, analysis of rhythm during chest compressions, CPR before defibrillation, removal of foreign-body airway obstruction, resuscitation care for suspected opioid-associated emergencies, drowning, and harm from CPR to victims not in cardiac arrest. The topics that resulted in the most extensive task force discussions included CPR during transport, CPR before calling for help, resuscitation care for suspected opioid-associated emergencies, feedback for CPR quality, and analysis of rhythm during chest compressions. After discussion of the scoping reviews and the evidence update, the task force prioritized several topics for new systematic reviews.

Diabetes is most important cause for mortality in COVID-19 hospitalized patients: Systematic review and meta-analysis.

The presence of SARS-CoV-2 was officially documented in Europe at the end of February 2020. Despite many observations, the real impact of COVID-19 in the European Union (EU), its underlying factors and their contribution to mortality and morbidity outcomes were never systematically investigated. The aim of the present work is to provide an overview and a meta-analysis of main predictors and of country differences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-associated mortality rate (MR) in hospitalized patients. Out of 3714 retrieved articles, 87 studies were considered, including 35,486 patients (mean age 60.9 ± 8.2 years) and 5867 deaths. After adjustment for confounders, diabetes mellitus was the best predictors of MR in an age- and sex-dependent manner, followed by chronic pulmonary obstructive diseases and malignancies. In both the US and Europe, MR was higher than that reported in Asia (25[20;29] % and 20[17;23] % vs. 13[10;17]%; both p < 0.02). Among clinical parameters, dyspnea, fatigue and myalgia, along with respiratory rate, emerged as the best predictors of MR. Finally, reduced lymphocyte and platelet count, along with increased D-dimer levels, all significantly contributed to increased mortality. The optimization of glucose profile along with an adequate thrombotic complications preventive strategy must become routine practice in diseased SARS-CoV-2 infected patients.

Basic life support and systems saving lives.

PURPOSE OF REVIEW: To describe recent science in basic life support (BLS) after cardiac arrest and how evolving knowledge in resuscitation is changing current guidelines and practices. RECENT FINDINGS: The core elements of BLS have remained mostly unchanged since 2005 when Cardiopulmonary Resuscitation recommendations were changed from 2 ventilations to 15 compressions and up to three stacked shocks for shockable rhythms, to 30 compressions to 2 ventilations and single shocks. Since 2010, basic life support has largely focused on the importance of providing high-quality CPR for professional and lay rescuers alike. The most recent resuscitation updates has seen an increased focus on the systems perspective. The 'Systems Saving Lives' concept emphasizes the interconnection between community and Emergency Medical Services (EMS). The main changes in current resuscitation practice are within three important basic life support domains: recognition of cardiac arrest, interaction between rescuers and EMS and improving resuscitation quality. SUMMARY: This review highlights the importance of strengthening both community and emergency medical services efforts to improve outcomes in cardiac arrest. Strategies that enhance the communication and collaboration between lay rescuers and professional resuscitation systems are important new avenues to pursue in developing systems that save more lives.

KIDS SAVE LIVES: a narrative review of associated scientific production.

PURPOSE OF REVIEW: In sudden out-of-hospital cardiac arrest, bystander cardiopulmonary resuscitation (CPR) is one of the most important elements of the chain of survival. Since 2015, international health societies and associations have recognized KIDS SAVE LIVES (KSL) as an essential initiative on CPR principles dissemination among schoolchildren. Children can be potential multipliers of the CPR competencies by teaching families, relatives, and friends. This review aimed to determine the main CPR issues raised in the KSL-associated publications. RECENT FINDINGS: We found 12 Editorials, 9 Letters, 2 Special Reports, 4 Reviews, 2 Guidelines, 9 Original Articles and 17 Conference Presentations on KSL history, the schoolchildren CPR education, and KSL program implementation in several countries. In nine original studies, the main issues were instructors' and learners' CPR knowledge, skills, and retention, gender and physical aspects affecting CPR performance, types of KSL programs and new technologies to teach CPR. SUMMARY: The KSL-associated literature is limited to support KSL benefits. However, the KSL could potentially contribute to improve out-of-hospital CPR performed by lay people at earlier age in different countries. Children are an important target group to diffuse CPR principles ('CHECK-CALL-COMPRESS'), as they are curious, motivated and enjoy teaching others.

Remodelling of a regional emergency hub in response to the COVID-19 outbreak in Emilia-Romagna.

Emilia-Romagna was one of the most affected Italian regions during the COVID-19 outbreak in February 2020. We describe here the profound regional, provincial and municipal changes in response to the COVID-19 pandemic, to cope with the numbers of patients presenting with COVID-19 illness, as well as coping with the ongoing need to care for patients presenting with non-COVID-19 emergencies. We focus on the structural and functional changes in one particular hospital within the city of Bologna, the regional capital, which acted as the central emergency hub for time-sensitive pathologies for the province of Bologna. Finally, we present the admissions profile to our emergency department in relation to the massive increase of infected patients observed in our region as well as the organisational response to prepare for the second wave of the pandemic.

[Basic life support]. / Basismaßnahmen zur Wiederbelebung Erwachsener (Basic Life Support): Leitlinien des European Resuscitation Council 2021.

The European Resuscitation Council has produced these basic life support guidelines, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include cardiac arrest recognition, alerting emergency services, chest compressions, rescue breaths, automated external defibrillation (AED), cardiopulmonary resuscitation (CPR) quality measurement, new technologies, safety, and foreign body airway obstruction.

[Executive summary]. / Kurzfassung: Leitlinien des European Resuscitation Council 2021.

Informed by a series of systematic reviews, scoping reviews and evidence updates from the International Liaison Committee on Resuscitation, the 2021 European Resuscitation Council Guidelines present the most up to date evidence-based guidelines for the practice of resuscitation across Europe. The guidelines cover the epidemiology of cardiac arrest; the role that systems play in saving lives, adult basic life support, adult advanced life support, resuscitation in special circumstances, post resuscitation care, first aid, neonatal life support, paediatric life support, ethics and education.

Between human and AI: assessing the reliability of AI text detection tools.

OBJECTIVE: Large language models (LLMs) such as ChatGPT-4 have raised critical questions regarding their distinguishability from human-generated content. In this research, we evaluated the effectiveness of online detection tools in identifying ChatGPT-4 vs human-written text. METHODS: A two texts produced by ChatGPT-4 using differing prompts and one text created by a human author were analytically assessed using the following online detection tools: GPTZero, ZeroGPT, Writer ACD, and Originality. RESULTS: The findings revealed a notable variance in the detection capabilities of the employed detection tools. GPTZero and ZeroGPT exhibited inconsistent assessments regarding the AI-origin of the texts. Writer ACD predominantly identified texts as human-written, whereas Originality consistently recognized the AI-generated content in both samples from ChatGPT-4. This highlights Originality's enhanced sensitivity to patterns characteristic of AI-generated text. CONCLUSION: The study demonstrates that while automatic detection tools may discern texts generated by ChatGPT-4 significant variability exists in their accuracy. Undoubtedly, there is an urgent need for advanced detection tools to ensure the authenticity and integrity of content, especially in scientific and academic research. However, our findings underscore an urgent need for more refined detection methodologies to prevent the misdetection of human-written content as AI-generated and vice versa.

Cardiac arrest and cardiopulmonary resuscitation in the next decade: Predicting and shaping the impact of technological innovations.

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.

Logistic and cognitive-emotional barriers experienced by first responders when alarmed to get dispatched to out-of-hospital cardiac arrest events: a region-wide survey.

Out-of-hospital cardiac arrest (OHCA) is a major public health concern with low survival rates. First responders (FRs) and public access defibrillation (PAD) programs can significantly improve survival, although barriers to response activation persist. The Emilia Romagna region in Italy has introduced a new system, the DAE RespondER App, to improve the efficiency of FR dispatch in response to OHCA. The study aimed to evaluate the association between different logistic factors, FRs' perceptions, and their decision to accept or decline dispatch to an OHCA scene using the DAE RespondER App. A cross-sectional web survey was conducted, querying 14,518 registered FRs using the DAE RespondER app in Emilia Romagna. The survey explored logistic and cognitive-emotional perceptions towards barriers in responding to OHCAs. Statistical analysis was conducted, with responses adjusted using non-response weights. 4,644 responses were obtained (32.0% response rate). Among these, 1,824 (39.3%) had received at least one dispatch request in the past year. Multivariable logistic regression showed that being male, having previous experience with OHCA situations, and having an automated external defibrillator (AED) available at the moment of the call were associated with a higher probability of accepting the dispatch. Regarding FRs' perceptions, logistic obstacles were associated with mission rejection, while higher scores in cognitive-emotional obstacles were associated with acceptance. The study suggests that both logistical and cognitive-emotional factors are associated with FRs' decision to accept a dispatch. Addressing these barriers and further refining the DAE RespondER App can enhance the effectiveness of PAD programs, potentially improving survival rates for OHCA. The insights from this study can guide the development of interventions to improve FR participation and enhance overall OHCA response systems.

Testing ChatGPT ability to answer laypeople questions about cardiac arrest and cardiopulmonary resuscitation.

INTRODUCTION: Cardiac arrest leaves witnesses, survivors, and their relatives with a multitude of questions. When a young or a public figure is affected, interest around cardiac arrest and cardiopulmonary resuscitation (CPR) increases. ChatGPT allows everyone to obtain human-like responses on any topic. Due to the risks of accessing incorrect information, we assessed ChatGPT accuracy in answering laypeople questions about cardiac arrest and CPR. METHODS: We co-produced a list of 40 questions with members of Sudden Cardiac Arrest UK covering all aspects of cardiac arrest and CPR. Answers provided by ChatGPT to each question were evaluated by professionals for their accuracy, by professionals and laypeople for their relevance, clarity, comprehensiveness, and overall value on a scale from 1 (poor) to 5 (excellent), and for readability. RESULTS: ChatGPT answers received an overall positive evaluation (4.3 ± 0.7) by 14 professionals and 16 laypeople. Also, clarity (4.4 ± 0.6), relevance (4.3 ± 0.6), accuracy (4.0 ± 0.6), and comprehensiveness (4.2 ± 0.7) of answers was rated high. Professionals, however, rated overall value (4.0 ± 0.5 vs 4.6 ± 0.7; p = 0.02) and comprehensiveness (3.9 ± 0.6 vs 4.5 ± 0.7; p = 0.02) lower compared to laypeople. CPR-related answers consistently received a lower score across all parameters by professionals and laypeople. Readability was 'difficult' (median Flesch reading ease score of 34 [IQR 26-42]). CONCLUSIONS: ChatGPT provided largely accurate, relevant, and comprehensive answers to questions about cardiac arrest commonly asked by survivors, their relatives, and lay rescuers, except CPR-related answers that received the lowest scores. Large language model will play a significant role in the future and healthcare-related content generated should be monitored.

Acute Lung Injury after Cardiopulmonary Resuscitation: A Narrative Review.

Although cardiopulmonary resuscitation (CPR) includes lifesaving maneuvers, it might be associated with a wide spectrum of iatrogenic injuries. Among these, acute lung injury (ALI) is frequent and yields significant challenges to post-cardiac arrest recovery. Understanding the relationship between CPR and ALI is determinant for refining resuscitation techniques and improving patient outcomes. This review aims to analyze the existing literature on ALI following CPR, emphasizing prevalence, clinical implications, and contributing factors. The review seeks to elucidate the pathogenesis of ALI in the context of CPR, assess the efficacy of CPR techniques and ventilation strategies, and explore their impact on post-cardiac arrest outcomes. CPR-related injuries, ranging from skeletal fractures to severe internal organ damage, underscore the complexity of managing post-cardiac arrest patients. Chest compression, particularly when prolonged and vigorous, i.e., mechanical compression, appears to be a crucial factor contributing to ALI, with the concept of cardiopulmonary resuscitation-associated lung edema (CRALE) gaining prominence. Ventilation strategies during CPR and post-cardiac arrest syndrome also play pivotal roles in ALI development. The recognition of CPR-related lung injuries, especially CRALE and ALI, highlights the need for research on optimizing CPR techniques and tailoring ventilation strategies during and after resuscitation.