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Acute Medical Threats: Evidence-Driven Strategies in Emergency Medicine
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Acute Medical Threats: Evidence-Driven Strategies in Emergency Medicine

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Emergency Medicine".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 3575

Special Issue Editor

Dr. Alexandra Arvanitaki

E-Mail Website
Guest Editor
1. Second Department of Cardiology, Ippokrateio General Hospital, School of Medicine, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54642 Thessaloniki, Greece
2. Department of Emergency Medicine, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, St. Kiriakidi 1, 54642 Thessaloniki, Greece
3. Adult Congenital Heart Disease Unit, Royal Brompton Hospital, Guy’s and St Thomas’ Foundation Trust, Sydney Street, London SW3 5NP, UK
Interests: emergency medicine; cardiovascular imaging; cardiac disease and pregnancy; congenital heart disease; pulmonary hypertension
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As acute medical crises—ranging from infectious disease outbreaks and mass casualty incidents to natural disasters—become increasingly prevalent, the importance of rapid, coordinated, and adaptable strategies in the field of emergency medicine has never been greater.

This Special Issue explores the latest approaches and strategies in managing acute medical threats encountered in emergency medicine. It emphasizes event-driven protocols designed to rapidly assess, triage, and treat critical conditions such as acute cardiovascular, respiratory, neurological, gastroenterological, and pregnancy-related events; traumatic and non-traumatic injuries; infectious outbreaks; chemical or biological incidents; and natural disasters. The issue reviews evidence-based practices, innovative technologies, and multidisciplinary coordination efforts aimed at improving patient outcomes during sudden, large-scale emergencies.

This issue aims to provide clinicians, researchers, and policymakers with a comprehensive overview of current best practices, innovative approaches, and future directions in managing acute medical threats. By emphasizing event-driven strategies, we seek to enhance preparedness, optimize response efforts, and ultimately improve patient outcomes in the face of unpredictable and formidable emergencies.

Dr. Alexandra Arvanitaki
Guest Editor

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Keywords

  • emergency medicine protocols
  • acute medical care
  • trauma management
  • resuscitation
  • critical care
  • infectious disease preparedness

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Published Papers (4 papers)

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Research

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16 pages, 1688 KB  
Article
Development of a Triage-Level Predictive Model for Hospitalization in the Emergency Department
by Daniel Trotzky, Yoav Preisler, Almog Amoyal, Gal Pachys, Jonathan Mosery, Aya Cohen, Shiran Avisar and Tomer Ziv Baran
J. Clin. Med. 2026, 15(5), 1901; https://doi.org/10.3390/jcm15051901 - 2 Mar 2026
Viewed by 503
Abstract
Background/Objectives: Overcrowding in the emergency department (ED) is a global health issue. Early prediction of expected hospitalizations, based on parameters available from triage, is essential to enhance patient transfer from the ED to departments, thereby reducing ED congestion. Methods: A historical [...] Read more.
Background/Objectives: Overcrowding in the emergency department (ED) is a global health issue. Early prediction of expected hospitalizations, based on parameters available from triage, is essential to enhance patient transfer from the ED to departments, thereby reducing ED congestion. Methods: A historical cohort study included patients who visited two tertiary referral medical centers located in the center of Israel. Data derived from one medical center (MC-A) was used to build the prediction model and to test it, and data from the second medical center (MC-B) was used to validate it. Variables collected included age, sex, triage level, vital signs, initial admitting diagnosis, medical referrals, mode of arrival, time of arrival according to hospital shifts (morning, evening, and night), weekday (workdays/weekend), season, fall risk assessment, and significant comorbidities. Logistic regression was used to build the model, and the area under the ROC curve (AUC) and the discrimination slope (DS) were used to evaluate it. Results: The final cohort included 1436 patients: 1256 patients from MC-A and 180 from MC-B. The patients were divided randomly into a learning group (n = 879), a test group (n = 377), and a validation group (n = 180). We found that higher triage level (urgent+: OR 1.45, p = 0.039), lower O2 saturation (<95%: OR 3.32, p < 0.001), malignancy (OR 1.81, p = 0.044), cardiovascular disease (OR 2.93, p < 0.001), neurologic illness (OR 2.07, p = 0.014), arrival during the weekend (OR 1.57, p = 0.014), and fall season (OR 1.81, p = 0.003) were associated with higher probability of hospital admission. Our model showed a similar acceptable discrimination ability in all groups (learning: AUC = 0.77, 95%CI 0.73–0.80, and DS = 19%; testing: AUC = 0.76, 95%CI 0.70–0.82, and DS = 17%; validation: AUC = 0.71, 95%CI 0.61–0.80, and DS = 18%). Conclusions: The proposed prediction model can be easily implemented in hospital systems to provide management with an expected number of ED patient hospitalizations in the coming hours. The model can enhance patient flow, thereby reducing crowding in the ED. Full article
(This article belongs to the Special Issue Acute Medical Threats: Evidence-Driven Strategies in Emergency Medicine)
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15 pages, 2327 KB  
Article
Is Artificial Intelligence Ready for Emergency Department Triage? A Retrospective Evaluation of Multiple Large Language Models in 39,375 Patients at a University Emergency Department
by Ioannis Nedos, Sofia-Chrysovalantou Zagalioti, Christos Kofos, Theoni Katsikidou, Dimitra Vellidou, Konstantinos Astrinakis, Ioannis Karagiannis, Panagiotis Giannakopoulos, Styliani Michaloudi, Aikaterini Apostolopoulou, Efstratios Karagiannidis and Barbara Fyntanidou
J. Clin. Med. 2026, 15(4), 1512; https://doi.org/10.3390/jcm15041512 - 14 Feb 2026
Cited by 1 | Viewed by 1206
Abstract
Background: Large language models (LLMs) are increasingly proposed as clinical decision support tools. However, their reliability in the emergency department (ED) triage remains insufficiently validated. This study aimed to evaluate the performance and limitations of multiple LLMs in triage using a large retrospective [...] Read more.
Background: Large language models (LLMs) are increasingly proposed as clinical decision support tools. However, their reliability in the emergency department (ED) triage remains insufficiently validated. This study aimed to evaluate the performance and limitations of multiple LLMs in triage using a large retrospective dataset. Methods: We conducted a retrospective analysis of 39,375 anonymized patient cases from the ED of AHEPA University General Hospital, Thessaloniki, Greece (June 2024–July 2025), extracted from the hospital’s electronic medical record system. All cases were triaged in real time according to the Emergency Severity Index (ESI) by 25 emergency physicians. In cases of uncertainty, a senior emergency physician was consulted. Seven LLMs (ChatGPT-5 Thinking, ChatGPT-5 Instant, Gemini 2.5, Qwen 3, Grok 4.0, Deep Seek v3.1, and Claude Sonnet 4) were evaluated against the physician-assigned ESI level (reference standard). Outcomes included triage score agreement (quadratic weighted kappa, κw), clinic referral accuracy and admission prediction. Subgroup analyses were performed by referral clinic and admission outcome. The study was conducted in accordance with TRIPOD-AI reporting guidelines. Results: Model performance varied substantially. DeepSeek and Claude Sonnet 4 achieved the highest agreement with physician-assigned ESI (κw ≈ 0.467; raw accuracy: 61.7%). In contrast, GPT-5 Instant performed poorly across all evaluation metrics (κw = 0.176; 95% CI: 0.167–0.186). Claude Sonnet 4 demonstrated the best performance in clinic referral (67.1%; κ = 0.619) and admission prediction (κw ≈ 0.46). Subgroup analyses indicated higher performance in pediatric cases and organ-specific complaints, such as ophthalmology (up to 81% accuracy). LLMs also showed tendencies toward over- or under-triage. Conclusions: Current LLMs demonstrate promising but inconsistent capability in triage. While selected models achieved moderate alignment with physician ESI decisions, none achieved strong agreement (κ > 0.80). LLMs are most suitable as supervised decision support tools, particularly in anatomically well-defined clinical scenarios, rather than as autonomous systems. Full article
(This article belongs to the Special Issue Acute Medical Threats: Evidence-Driven Strategies in Emergency Medicine)
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13 pages, 234 KB  
Article
Disparities in Survival After In-Hospital Cardiac Arrest by Time of Day and Day of Week: A Single-Center Cohort Study
by Maria Aggou, Barbara Fyntanidou, Marios G. Bantidos, Andreas S. Papazoglou, Athina Nasoufidou, Aikaterini Apostolopoulou, Christos Kofos, Alexandra Arvanitaki, Nikolaos Vasileiadis, Dimitrios Vasilakos, Haralampos Karvounis, Konstantinos Fortounis, Eleni Argyriadou, Efstratios Karagiannidis and Vasilios Grosomanidis
J. Clin. Med. 2026, 15(3), 987; https://doi.org/10.3390/jcm15030987 - 26 Jan 2026
Viewed by 497
Abstract
Background: In-hospital cardiac arrest (IHCA) constitutes a high-impact clinical event, associated with substantial mortality, frequent neurological and functional impairment. There is a pressing need for primary IHCA studies that evaluate risk predictors, given the inherent challenges of IHCA data collection, previously unharmonized reporting [...] Read more.
Background: In-hospital cardiac arrest (IHCA) constitutes a high-impact clinical event, associated with substantial mortality, frequent neurological and functional impairment. There is a pressing need for primary IHCA studies that evaluate risk predictors, given the inherent challenges of IHCA data collection, previously unharmonized reporting frameworks, and the predominant focus of prior investigations on other domains. Among potential contributors, the “off-hours effect” has consistently been linked to poorer IHCA outcomes. Accordingly, we sought to examine whether in-hospital mortality after IHCA varies according to the time and day of occurrence within a tertiary academic center in Northern Greece. Methods: We conducted a single-center observational cohort study using a prospectively maintained in-hospital resuscitation registry at AHEPA University General Hospital, Thessaloniki. All adults with an index IHCA between 2017 and 2019 were included, and definitions followed Utstein-style recommendations. Results: Multivariable logistic regression adjusted for organizational, patient, and process-of-care factors demonstrated that afternoon/night arrests, weekend arrests, heart failure comorbidity, and need for mechanical ventilation were independent predictors of higher in-hospital mortality. Conversely, arrhythmia as the cause of IHCA and arrests occurring in the intensive care unit or operating room were associated with improved survival. Subgroup analyses confirmed consistent off-hours differences, with weekend events showing reduced 30-day and 6-month survival and worse functional status at discharge. Afternoon/night arrests were more frequent, characterized by longer response intervals and lower survival at both time points. Conclusions: Organizational factors during nights and weekends, rather than patient case mix, drive poorer IHCA outcomes, underscoring the need for targeted system-level improvements. Full article
(This article belongs to the Special Issue Acute Medical Threats: Evidence-Driven Strategies in Emergency Medicine)

Review

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16 pages, 965 KB  
Review
The Importance of the “Damage Control” Strategy in Multiple Organ Injuries, Pathophysiology and Principles of Hemorrhage Control
by Oliwia Klimek, Jakub Dudek, Anna Czesyk, Bartosz Sierant, Wiktoria Górecka, Grzegorz Gogolewski, Tomasz Jurek, Zuzanna Ochocka and Amelia Jankowska
J. Clin. Med. 2026, 15(7), 2549; https://doi.org/10.3390/jcm15072549 - 26 Mar 2026
Viewed by 1062
Abstract
Background/Objectives: Damage Control Resuscitation (DCR) is a critical strategy in the management of severe trauma, focusing on the optimisation of the patient’s physiological condition. This study reviews current DCR strategies, emphasizing the mitigation of the “diamond of death”—hypothermia, acidosis, coagulopathy, and hypocalcemia—while [...] Read more.
Background/Objectives: Damage Control Resuscitation (DCR) is a critical strategy in the management of severe trauma, focusing on the optimisation of the patient’s physiological condition. This study reviews current DCR strategies, emphasizing the mitigation of the “diamond of death”—hypothermia, acidosis, coagulopathy, and hypocalcemia—while addressing complex disturbances like respiratory distress syndrome (ARDS) and (acute kidney injury) AKI in high-ISS (Injury Severity Score) patients. Methods: A systematic review of 59 contemporary sources was conducted, encompassing clinical trials (e.g., CRASH-2), military-to-civilian protocol translations, and guidelines from the C and European Resuscitation Council. The analysis focused on pre-hospital interventions, in-hospital transfusion protocols, and the impact of transport logistics on survival. Results: Evidence highlights that aggressive crystalloid resuscitation (over 5 L) significantly increases mortality, favoring balanced blood component therapy (1:1:1 ratio) or Whole Blood guided by viscoelastic testing like rotational thromboelastometry (ROTEM) or thromboelastography (TEG). Pre-hospital success is driven by rapid hemorrhage control via tourniquets, early administration of Tranexamic Acid (TXA), no aggressive crystalloids, permissive hypotension, proactive calcium supplementation is recommended in early care. Furthermore, the integration of Helicopter Emergency Medical Services (HEMS) is independently associated with improved survival in multi-organ trauma by reducing time to definitive care and facilitating “en-route” damage control. Conclusions: The evolution of rescue strategies focused on mitigating the effects of the diamond of death, combined with the implementation of permissive hypotension and optimized HEMS logistics, constitutes the foundation of a modern model aimed at minimizing mortality in multi-organ trauma. Full article
(This article belongs to the Special Issue Acute Medical Threats: Evidence-Driven Strategies in Emergency Medicine)
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