Objective To evaluate the association between bystander type and successful bystanderdelivered shocks among out-of-hospital cardiac arrest (OHCA) patients with an initial shockable rhythm.
Methods This single-center, retrospective observational study included non-traumatic adult OHCA patients with witnessed and an initial shockable rhythm who were transported to our hospital between April 2013 and June 2024. The term “successful bystander-delivered shocks” was defined as use of an automated external defibrillator by a bystander before emergency medical services arrival. Bystanders were categorized as family members, lay bystanders, on-duty staff, or healthcare professionals. On-duty staff were defined as persons working at the scene at the time of OHCA, and lay bystanders as non-staff passers-by . The primary outcome was successful bystander-delivered shocks . Multivariable logistic regression analyses were performed.
Results 285 patients were included. Median age was 59 [49–69] years, 254 (89.1%) were male, and 184 (64.6%) received bystander-initiated cardiopulmonary resuscitation. The bystander was a family member in 54 cases, a lay bystander in 79 cases, on-duty staff in 131 cases, and a healthcare professional in 21 cases. Successful bystander-delivered shocks differed markedly by bystander type: 3.7% among family members, 36.7% among lay bystanders, 59.5% among on-duty staff, and 76.2% among healthcare professionals. In multivariable logistic regression, the adjusted odds ratios for successful bystander-delivered shocks by bystander type were on-duty staff, 2.29 [1.26–4.17]; healthcare professionals, 6.26 [1.96–19.9]; and family members, 0.08 [0.02–0.36], compared with lay bystanders.
Conclusion OHCA patients for whom the rescuing bystander was on-duty staff or a healthcare professional were significantly more likely to receive successful bystanderdelivered automated external defibrillator shocks than those rescued by lay bystanders.
Objective This study integrated a machine learning–based Score for Emergency Risk Prediction (SERP), developed using objective mortality endpoints, with the Patient Acuity Category Scale (PACS) and evaluated its effectiveness in clinical use.
Methods This single-center, retrospective cohort study included all emergency department (ED) patients from a large tertiary hospital between January 1, 2018, and December 31, 2019. Using a reclassification framework, SERP was incorporated into PACS to derive two enhanced triage models. PACS+ model 1 down-triaged patients with low predicted 30-day mortality risk and up-triaged those with high risk. PACS+ model 2 up-triaged only high-risk patients, whereas low-risk patients retained their original category. Predictive performance in the test cohort was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis.
Results The derivation cohort included 97,188 ED visits, and the test cohort included 97,212 ED visits. In the derivation set, the mean age of the patients was 58.97±18.41 years, and 47,993 (49.4%) were female. Overall, 19.9%, 57.4%, 22.5%, and 0.2% of patients were triaged to PACS categories P1–P4, respectively. The 30-day mortality rate was 2.8% in the derivation set and 2.7% in the test cohort. For 30-day mortality prediction, PACS+ model 1 (AUC, 0.828; 95% confidence interval [CI], 0.820–0.836) and PACS+ model 2 (AUC, 0.812; 95% CI, 0.805–0.818) outperformed PACS (AUC, 0.722; 95% CI, 0.714–0.729). PACS+ model 1 consistently achieved greater net benefit across the range of clinical thresholds.
Conclusion Integrating machine learning–based SERP with PACS improved 30-day mortality prediction in ED triage.
Objective Children are particularly vulnerable to heat-related illnesses due to their unique physiological and behavioral characteristics. Understanding the epidemiology and clinical features of heat-related illnesses in children is crucial for guiding targeted preventive measures and management strategies. This descriptive study aims to investigate the characteristics of pediatric patients with heat-related illness who were transferred to emergency departments in Japan.
Methods This study was a secondary analysis of the Heatstroke Study, led by the Heatstroke and Hypothermia Surveillance Committee of the Japanese Association for Acute Medicine. This study included pediatric patients (<18 years) with heat-related illness transferred to emergency departments in the summer from 2017 to 2021. We summarized the circumstances of onset, clinical characteristics, and outcomes.
Results Of the 3,154 registered patients, 146 children were included. Of them, 60% were male, with a median age of 15 years (interquartile range, 13–16 years). The most cases occurred in August (47%), and most (80%) were associated with sports activities and with outdoor settings (70%). Cases with a body temperature above 40 °C were rare (3%). Most cases were admitted to hospitals (75% to the general ward and 16% to the intensive care unit), and patients admitted to intensive care unit had altered consciousness with increased serum creatinine. There were two cases of mortality, both of which were out-of-hospital cardiac arrest.
Conclusion Most pediatric cases with heat-related illness were middle or high school students, occurred in August, and were related to outdoor sports activity. Patients admitted to hospitals suffered altered consciousness, dehydration, and acute kidney injury.
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Artificial intelligence (AI) and machine learning (ML) have potential to revolutionize emergency medical care by enhancing triage systems, improving diagnostic accuracy, refining prognostication, and optimizing various aspects of clinical care. However, as clinicians often lack AI expertise, they might perceive AI as a “black box,” leading to trust issues. To address this, “explainable AI,” which teaches AI functionalities to end-users, is important. This review presents the definitions, importance, and role of explainable AI, as well as potential challenges in emergency medicine. First, we introduce the terms explainability, interpretability, and transparency of AI models. These terms sound similar but have different roles in discussion of AI. Second, we indicate that explainable AI is required in clinical settings for reasons of justification, control, improvement, and discovery and provide examples. Third, we describe three major categories of explainability: pre-modeling explainability, interpretable models, and post-modeling explainability and present examples (especially for post-modeling explainability), such as visualization, simplification, text justification, and feature relevance. Last, we show the challenges of implementing AI and ML models in clinical settings and highlight the importance of collaboration between clinicians, developers, and researchers. This paper summarizes the concept of “explainable AI” for emergency medicine clinicians. This review may help clinicians understand explainable AI in emergency contexts.
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