Introduction: Opioid-induced respiratory depression is a life-threatening complication of opioid overdose. This study aimed to develop a model for predicting the risk of respiratory depression following opioid overdose using ChatGPT-4o.
Methods: A retrospective cross-sectional study was conducted on 2,005 patients admitted following opioid overdose at Loghman Hakim Hospital, Tehran, Iran, from February 2021 to February 2024. Demographic data, clinical presentations, interventions, and outcomes of patients were extracted from electronic medical records and a predictive model was developed using a no-code methodology with the assistance of ChatGPT-4o.
Results: 2,005 patients with the mean age of 32.97 ± 14.86 (Range: 1-100) years were studied (74.5% male). Respiratory depression was observed in 18% of patients upon admission. Naloxone was administered to 37.6% of patients, with higher usage in those requiring intubation. Key predictors included low oxygen saturation (SpO₂), low respiratory rate (RR), and increased heart rate (HR). The predictive model achieved an accuracy of 94.4% (95% confidence interval (CI): 87.0-96.3), a recall of 81.0% (95% CI: 78.0-84.0) for respiratory depression, and an area under the curve (AUC) of 0.98 (95% CI: 0.95-0.99).
Conclusion: The study highlights critical clinical predictors of respiratory depression risk in opioid overdose patients and demonstrates the potential of machine learning models in enhancing early detection and intervention.
Introduction: Risk perception is a cognitive, multidimensional process through which individuals identify and assess potential threats. This study aimed to systematically review the recent research to identify the key factors influencing the risk perception within healthcare workers operating in critical and disaster scenarios.
Methods: This study was conducted as a systematic review in accordance with PRISMA guidelines. A search was performed for articles published between January 2014 and July 2025 in the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases. Of the 2,154 initial articles, 10 eligible studies were included in the analysis following screening and quality assessment. Quantitative, qualitative, and mixed-methods studies addressing factors influencing healthcare workers' risk perception during disasters were selected, and the data were coded and categorized using thematic analysis.
Results: The analysis of the 10 selected studies identified a central theme titled "Factors Influencing Risk Perception," which was further divided into five key domains: 1) Demographic and individual factors, 2) Experience and exposure to risk, 3) Knowledge resources and information capital, 4) Cognitive-emotional attitudes and beliefs, and 5) Protective behaviors and measures.
Conclusion: This review demonstrates that healthcare workers' risk perception during disasters is a multifaceted phenomenon shaped by the interaction of individual, experiential, knowledge-based, emotional, and behavioral factors. Understanding these dimensions is crucial for explaining responses and designing interventions to enhance resilience and preparedness among healthcare workers. Based on the conceptual framework, it is recommended that educational programs and organizational policies consider demographic differences, experiences, and the psychosocial needs of staff.
Cardiac Advanced Life Support (CALS) differs from conventional Advanced Cardiac Life Support (ACLS) in utilizing targeted resuscitation protocols designed explicitly for post-cardiac surgery patients. The hallmark of CALS is the performance of prompt re-sternotomy and internal cardiac massage within 5 minutes of cardiac arrest if the patient is unresponsive to external chest compressions and rapid defibrillation. The standardized algorithms for ACLS, fundamental to managing cardiac arrest, present a significant and potentially dangerous dilemma when applied to patients who have undergone minimally invasive cardiac surgery (MICS). While MICS offers benefits like reduced trauma and faster recovery, it creates a unique physiological landscape that conflicts with conventional resuscitation. This letter highlights the urgent need to re-evaluate the ACLS protocol for this growing patient population. We advocate for the immediate development of a specialized MICS-specific resuscitation guideline that moves beyond a one-size-fits-all approach to in-hospital cardiac arrest.
Introduction: Calcium channel blocker (CCB) poisoning is a critical toxicological emergency that can result in severe complications, particularly cardiovascular effects. This study aimed to evaluate the accuracy of Machine learning (ML) models in predicting the outcomes of CCB poisoning.
Methods: This retrospective cross-sectional study analyzed the medical records of patients diagnosed with CCB poisoning at Loghman Hakim Hospital between 2019 and 2024. The accuracy of machine learning (ML) models in predicting the outcomes of CCB poisoning and identifying its predictive factors was evaluated. Various ML models, including XGBoost, CatBoost, Random Forest, and AdaBoost, were trained on clinical and laboratory data. Then, feature selection was performed to identify the most relevant variables. The hold-out set was randomly selected to avoid selection bias. Model performance was assessed using accuracy, precision, recall, F1-score, and macro-averaged area under the receiver operating characteristic (ROC) curve (AUC).
Results: 274 CCB poisoning cases with the mean age of 31.99± 17.47 (range: 1.5 to 89) years were evaluated (70.4% female). Feature selection identified 18 key prognostic factors, including body temperature, whole bowel irrigation, need for cardiology consultation, arterial oxygen saturation, Glasgow coma scale (GCS)-eye response, electrocardiography (ECG) findings, serum level of alkaline phosphatase (ALP), pH-venous blood gas (VBG), HCO3-VBG, serum level of lactate dehydrogenase (LDH), blood sugar, pulse rate, fraction of inspired oxygen (FiO2), time elapsed from ingestion to admission, troponin, serum level of alanine aminotransferase (ALT), serum level of creatinine, and serum level of potassium. Among the ML models, XGBoost and CatBoost demonstrated the highest predictive performance, with macro-averaged AUC values of 0.9899 (95%confidence interval (CI): 0.98-0.99) and 0.9983 (95%CI: 0.997-0.999), respectively. These models outperformed traditional statistical approaches, providing enhanced risk stratification for patients with CCB poisoning.
Conclusion: This study highlights the potential of ML-based models for predicting outcomes in CCB poisoning, offering a data-driven framework for early risk stratification. The superior performance of XGBoost and CatBoost suggests their clinical applicability. Future research should focus on external validation in multi-center settings and real-time integration into clinical decision-making systems.
Introduction: Many international search and rescue teams were deployed to the devastating earthquake of Southeastern Turkey and Northern Syria on February 6th, 2023, including the Jordan International Search and Rescue Team (JSAR). This study aims to explore the challenges faced by the JSAR team members during their deployment.
Methods: We employed a qualitative face-to-face semi-structured interview approach. Eighteen respondents were interviewed using an interview guide. Interviews took between 25 and 60 minutes (mean 45 minutes). Data were transcribed verbatim and an inductive thematic approach was used to analyze data and develop codes, categories, and themes.
Results: The challenges were categorized into three main themes; logistical, coordination, and environmental. Logistical challenges included delays in deployment due to government and flight arrangements, difficulties in transporting excess equipment, and a lack of fuel upon arrival that led to delays in setting up camp and heating. Coordination challenges involved disruption in operation schedule and difficulties working with local volunteer responders. Environmental challenges encompassed extreme cold temperatures, which affected personnel comfort and performance, and recurrent aftershocks, which complicated rescue operations and posed safety risks.
Conclusion: The JSAR experience highlights that technical readiness alone is insufficient for effective disaster response. Findings from this study underscore significant gaps in logistics, coordination, and environmental adaptation. These gaps can be addressed through improved pre-deployment coordination, context-specific resource planning, and better collaboration mechanisms between host countries and international teams, which would be crucial for enhancing the effectiveness of international search and rescue operations. Host governments, International Search and Rescue Advisory Group (INSARAG) stakeholders, and emergency management bodies can build on these lessons to better integrate specialized teams, reduce procedural delays, and enhance global disaster response systems.
Introduction: Mechanical chest compression devices provide consistent depth and reduced pauses during cardiopulmonary resuscitation (CPR), but their clinical impact on routine practice in emergency department (ED) remains uncertain. This study aimed to compare the outcomes of mechanical versus manual compressions among adults with in-hospital cardiac arrest managed in ED.
Methods: A single-center, comparative study of consecutive adult cardiac arrests in the ED (n = 372) was carried out. Patients were allocated by time period to either manual CPR (n = 195) during the retrospective phase (September 2024 to January 2025) or mechanical CPR (n = 177) with LUCAS-3 during the prospective phase (January to June 2025). The primary outcome was return of spontaneous circulation (ROSC). Secondary outcomes were survival at 6 hours and 24 hours post-arrest. Baseline differences were summarized with standardized mean differences, and survival was described with Kaplan-Meier curves (0-24 h). Logistic regression estimated odds ratios (ORs) for ROSC and 6-hour survival.
Results: Mechanical and manual chest compression groups comprised 177 and 195 patients, respectively. Unadjusted outcomes favored mechanical CPR. ROSC occurred in 54 (30.5%) versus 32 (16.4%), with an absolute risk difference of 14.1% and Six-hour survival was 25 (14.1%) versus 5 (2.6%). After adjustment, mechanical CPR remained associated with higher odds of ROSC (OR = 2.44, 95% confidence interval (CI): 1.18-4.42) and 6-hour survival (OR = 6.71, 95% CI: 2.94-18.94). By 24 hours, no patient survived in the mechanical group, whereas one patient (0.5) survived in the manual group (P>0.05). Kaplan-Meier curves showed early separation that narrowed by 24 hours.
Conclusion: It seems that mechanical chest compression during CPR is associated with increased ROSC and better early survival, compared to manual compression. Due to the limited sample size, non-randomized design with time-based allocation, single-center setting, potential residual confounding, and absence of neurologic outcomes, these results should be interpreted with caution.
Introduction: Understanding the epidemiological patterns of poisoning cases in specific regions is essential for health authorities to implement preventive measures and strategic planning. This study aimed to describe the epidemiologic characteristics of acute poisoning cases registered in Tehran province's emergency medical services (EMS).
Methods: This retrospective cross-sectional study was conducted on all registered acute poisoning cases from 2022 to 2024 in the Asayar database of Tehran Province's EMS. The cases were included through census sampling and descriptive analysis was used for evaluating the epidemiologic characteristics of registered cases.
Results: 76,113 acute poisoning cases were registered by Tehran Province EMS during the study period. The mean age of cases was 34.3 ± 15.0 years (59.1% male). The most frequent method of poisoning was oral, with 71,521 (94.0%) cases, and inhalational, with 3,236 (4.2%) cases. The highest number of cases was reported in the eastern region of Tehran with 15,058 cases. Seasonal distribution of poisonings was as follows: 20,201 (26.6%) cases in summer, 21,322(28.0%) cases in winter, 21,105 (27.7%) cases in autumn, and 13,485 (17.7%) cases in spring. Most poisonings occurred in residential settings, accounting for 72,194 (94.9%) cases. The most frequent used antidote was naloxone, in 12,662 (16.6%) cases, and atropine, in 961 (1.3%) cases.
Conclusion: Based on the findings of this study, the most vulnerable population groups to the poisoning were young individuals, males, and those with a history of psychiatric illness and substance abuse, predominantly affected by oral route. The geographical and temporal distribution of poisonings highlights the need for targeted preventive interventions, public education, and enhancement of prehospital emergency service infrastructure in high-risk areas.
ST segment elevation patterns on Electrocardiogram (ECG) are a crucial finding in the diagnosis and treatment of acute coronary syndrome. An ST segment elevation pattern can be a sign of acute myocardial ischemia requiring immediate intervention. However, ST elevation patterns have been reported to occur due to obstructive intraabdominal pathology, a diagnosis often confirmed by cardiac catheterization. Here we report a 75-year-old female who presented to the emergency department with worsening chest and epigastric abdominal pain. ECG demonstrated ST-segment elevations in inferior leads (II, III, and aVF) with reciprocal changes in the lateral leads (I and aVL). Physical exam was suggestive of a bowel obstruction at the site of a large incarcerated ventral hernia, which was later confirmed by imaging. Due to the lack of typical chest pain symptoms and a strong suspicion of obstructive intraabdominal pathology, activation of the catheterization laboratory was deferred. Decompression of the bowel obstruction was achieved with a nasogastric tube, which resulted in immediate resolution of ST-segment elevations. During her admission, her ventral hernia was repaired, and left heart catheterization was deferred per cardiology recommendations. While an ST-segment elevation due to occlusive myocardial infarction is a diagnosis that cannot be missed and requires an emergent workup, it is important to be aware that it is possible for a small bowel obstruction (SBO) to present with ECG changes consistent with an ST-segment elevation myocardial infarction (STEMI). We also found that ST-segment elevations due to obstructive intraabdominal pathology are more reportedly seen in the literature in the inferior leads than any other contiguous leads, which is a novel pattern not discussed in past literature.
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