Background. Anaphylaxis is the most severe form of acute systemic allergic reactions. Several recommendations have been proposed to guide and standardize anaphylaxis approach and management. The objective of this study is to characterize a cohort of patients referred through an anaphylaxis fast-track system, to enhance and standardize anaphylaxis patient care. Methods. Observational study including patients with anaphylaxis admitted in the emergency department (ED) and/or referred through the fast-track system (June 2022 - June 2025). Collected data included demographics, clinical presentation, aetiology, treatment, adrenaline autoinjector (AAI) prescription, request for serum tryptase testing, biphasic reactions, diagnosis and follow-up care. Results. Over the 399 patients referred through the fast-track system for specialist evaluation, anaphylaxis was confirmed in 120 patients. Drug-induced anaphylaxis was the most prevalent, with nonsteroidal anti-inflammatory drugs the most reported (46.3%). Food-induced anaphylaxis was the second cause, with shellfish accounting for most cases (63.5%). The most common association of symptoms was mucocutaneous and respiratory symptoms (49.5%). Among the 120 confirmed cases, intramuscular adrenaline was administered in 68.4% (67/98) of patients admitted to ED and AAI in only 18.4% (18/98). Biphasic reactions were only reported in three patients. No fatalities or recurrent episodes were documented. Conclusions. These findings highlight the importance of standardized protocols or fast-track systems for anaphylaxis diagnosis and management, allowing for a rapid recognition, treatment and management of patients. Nevertheless, the need to continually improve medical education and training remains. This study is limited by its selected cohort, the exclusion of primary-care emergency department data owing to coding limitations, and its inherently descriptive design.

Foreign-body ingestion is a common clinical emergency, accounting for 120,000 yearly in the United States, yet data on object prevalence and outcomes remain limited. To address this gap, we evaluated national trends in ingested objects by type, year, and disposition. The National Electronic Injury Surveillance System (NEISS) was queried for pediatric (≤ 17 years) and adult (≥ 18 years) foreign-body ingestion events from 2014 to 2023, excluding medication, drug, and poisoning cases. In this retrospective cross-sectional study, descriptive analyses were performed in R 4.4.2, with trends assessed using linear regression, logistic regression, and chi-square or Fisher's exact tests. From 2014 to 2023, 34,406 ingestion events corresponded to an estimated 904,234 US cases; pediatrics accounted for 90.5% (median age 3 years). Adults most commonly ingested jewelry (9.8%), batteries (7.4%), and nails/screws/thumbtacks (NST; 7.3%), while children ingested coins (39.3%), batteries (9.6%), and jewelry (5.9%). Over 10 years, adult ingestion events rose from 8658 to 19,524 (p < 0.001), while pediatric events rose from 74,448 to 90,494 (p = 0.06). Adults had higher hospitalization rates (19.5% vs. 11.9%; p < 0.05). Among the three most common adult ingestions, hospitalization was most likely for batteries (OR = 8.6; p < 0.001) and NST (OR = 5.9; p < 0.001) compared to jewelry. In children, hospitalization was more likely for coins (OR = 0.34; p < 0.001) compared to jewelry. From 2014 to 2023, ingestion events increased for adults and children. Jewelry, batteries, and NST were common in both groups, but disposition differed, underscoring the need for targeted prevention strategies based on age and product.

Musculoskeletal conditions are the most common low acuity ED presentation. In response, over the last 13 years, Advanced Practice Physiotherapist (APP) roles have been established in emergency departments (ED) across Australia. Despite APP roles being well established in many Australian EDs, the current scope of practice for APPs as defined by discharge diagnosis is not accurately defined. To derive a scope of practice based on ED diagnosis for APPs in an Australian tertiary hospital ED. Descriptive classification study of patients who received care from an APP in the study ED between January 2015 and September 2024, with an iterative consensus process to establish the scope of APP practice by ED discharge diagnosis. APPs provided 37,771 individual episodes of care with 388 assigned International Classification of Diseases (10th revision) (ICD-10) discharge diagnoses. 204 ICD-10 diagnoses were rated as representing the local scope of practice for ED APPs. These codes accounted for 37,000 (98.2%) of the episodes of care. The APP team provided care for 33,713 adult patients, 4058 paediatric patients, 1197 patients who arrived by ambulance and 3477 patients referred to the ED by a GP. The consensus process demonstrated that APPs have clarity on their scope of practice and the capability to manage a differential diagnosis process that includes non-musculoskeletal diagnoses in the ED setting. Using 10 years of ED data, a consensus process mapped the local scope of practice of APPs in a single Western Australian Tertiary Hospital ED to 204 ICD-10 diagnoses.

The seasonality of many respiratory pathogen outbreaks, such as influenza and respiratory syncytial virus, is driven by climate factors, such as specific humidity or temperature. However, it remains unclear whether climate plays a role in determining the seasonality of COVID-19, given that the evolution of novel strains likely plays a key role in shaping outbreak dynamics. Here we use Emergency Department data to explore spatial differences in COVID-19 outbreak dynamics over three years, from April 2022 through March 2025. We observe that outbreak patterns varied across latitude, with southern states experiencing larger summer peaks and northern states facing more evenly distributed summer to winter outbreaks or larger winter peaks. We find that specific humidity and temperature at the state level are significantly associated with observed differences in ED visits with a COVID-19 diagnosis, even after controlling for state-level variation in vaccination status. Our results imply a role for climate in influencing COVID-19 outbreak dynamics. We anticipate these findings will provide a foundational understanding of factors shaping SARS-CoV-2 transmission as COVID-19 becomes endemic in the United States.

During epidemics, emergency department (ED) syndromic surveillance of patient arrivals provides timely but non-virus-specific assessment of epidemic intensity. Surveillance of severe infection outcomes (intensive care admission or death) is less timely because outcomes can take weeks to occur. Time series models can be used to estimate the frequency of severe infection outcomes due to viruses. We developed and evaluated daily time series modelling applied to linked ED, infection and outcomes data from Australia to better predict population and health system burden during acute respiratory virus epidemics. In retrospective daily surveillance emulation, generalised additive models nowcasted (short-term forecast) the frequency of ED arrivals attributable to each of influenza and COVID-19 that will have a severe infection outcome within 28 days. Daily nowcasts spanned days -29 to -4 from each date for which surveillance was emulated. To validate the method, nowcasts were compared with subsequently observed severe infection outcome frequencies for December 2021 through February 2023. During this period, the mean daily day -4 nowcast error was 2.7 (34.2%), compared with 3.5 (43.8%) if outcomes known at day -1 were used. With increasing real-world data availability, this method could improve rapid, automated epidemic assessment for timely public health action.

In Maine, rabies postexposure prophylaxis (PEP) administration is reportable to public health. We sought to determine the objectives of the Maine Center for Disease Control and Prevention's (Maine CDC's) PEP administration surveillance system and whether the method of conducting surveillance through a manual health care provider (hereinafter, provider) reporting system meets these objectives. We also compared provider-reported PEP administrations with administrations identified in emergency department (ED) data. During September 2022, we interviewed 8 Maine CDC epidemiologists to determine system objectives. We obtained and compared PEP administration data from provider reporting system and ED data and summarized each dataset by year, exposing animal, and facility. We assessed the ability of each source to address surveillance system objectives by comparing data elements with each objective. Maine CDC epidemiologists described the following objectives of the surveillance system: (1) track potential human exposures to rabid or potentially rabid animals, (2) document PEP administration trends, and (3) ensure PEP is correctly administered. They determined the third objective is not being achieved by the current system. During January 2018-June 2022, we identified 538 provider-reported PEP administrations and 1191 PEP administrations through ED data. ED data were more timely than provider reports and identified more PEP administrations, but 28% of ED records did not contain information on the exposing animal. Maine CDC can use ED data to document PEP administration trends in near-real time. ED data obtained from syndromic surveillance might be used in tandem with or in place of Maine CDC's traditional PEP surveillance system. We are building more complex queries that more fully capture PEP administrations to have a thorough understanding of PEP administered in Maine.

Timely surveillance of opioid-related harm is critical to inform public health responses and policy evaluation. In Australia, where prescription and illicit opioids remain a leading cause of unintentional drug-induced deaths, emergency departments (ED) are a vital point of contact for acute opioid poisonings. Existing surveillance systems rely on structured coding, yet much relevant information is recorded in free-text fields, leading to underreporting or misclassification. This limits opportunistic identification of emerging patterns and weakens the evidence base for evaluating policy reforms. We aim to improve surveillance accuracy by applying natural language processing (NLP) to routinely collected ED data. Using medical concept annotation tools, we will develop models trained on 15 years of Victorian Emergency Minimum Dataset (VEMD) records. These models will analyse both unstructured and structured fields to identify opioid poisoning presentations and be validated against a manually coded gold standard using standard performance metrics. In the second phase, we will incorporate additional unstructured clinical information such as discharge summaries from hospital electronic records, which are not available in the VEMD data, thereby allowing more comprehensive and accurate classification. Finally, we will assess the utility of NLP-enhanced data in evaluating three major opioid policy changes. This study is the first to apply NLP at large scale to Australian ED data for drug poisonings. By improving the accuracy and consistency of opioid poisoning identification, this approach can strengthen routine surveillance and better inform timely policy and health system responses without increasing the workload for clinical staff.

Seasonal patterns and dynamics of acute pancreatitis emergency department visits in Korea.

To describe emergency department (ED) chest pain presentations and outcomes, and sex-based differences in Western Australia. We conducted a retrospective cohort study using linked ED, hospital, morbidity, mortality, and pathology data from the Western Australian Data Linkage System as part of the AUS-MOCHA project. We included the index presentation for adults presenting with non-traumatic chest pain between January 2016 and October 2020. Comorbidities were identified via a 10-year lookback of linked morbidity data. The primary outcome was diagnosis of acute coronary syndrome (ACS) or all-cause death within 30 days of ED discharge. The study included 64,404 patients (mean age 54.8 years, 49.2% female). Most presented 'out-of-hours' (62.2%) and received an Australasian Triage Scale (ATS) Category of 2 (85.2%). Females were less likely to receive an ATS 1 and more likely to receive ATS 3 or 4. This difference persisted after propensity matching. However, males were more comorbid and had a higher incidence of 30-day outcomes than females. There was no statistically significant difference in ATS categories between males and females when assessing a propensity matched cohort of only those who experienced 30-day outcome. Among patients discharged home from the ED, 0.4% were diagnosed with ACS and 0.1% died within 30 days. Patients who were discharged home from EDs had a low risk of subsequent ACS or death within 30 days. Females were triaged less urgently than males, yet had lower 30-day ACS and death rates. Among patients with 30-day outcomes, triage scores did not differ by sex.

Agreement between self-report of coronary heart disease by participants in the 45 and Up Study and hospital administration data: A record linkage study.

Predicting antibiotic treatment outcomes could help tackle antibiotic resistance by guiding prescribing decisions. Existing approaches do not quantitatively incorporate the judgment of clinician users. Our antibiotic decision-making algorithm predicted treatment outcomes for 13 antibiotics using clinical prediction models trained on prescribing and urine culture data from 93,906 patients, then weighted outcomes using treatment decisions made by 49 clinicians in an antibiotic choice ranking exercise. In a simulation using Emergency Department data, the algorithm chose more correctly-targeted World Health Organization Access category antibiotics (75.6% of cases versus 11.9%, 95% confidence interval of difference 57.6% to 69.7%, p < 0.001) and oral antibiotics (69% versus 22.6%, 95% confidence interval of difference 39.5% to 53.4%, p < 0.001) than human prescribers, and fewer intravenous antibiotics (31.2% versus 65.8%, 95% confidence interval of difference −41.9% to −27.1%, p < 0.001). These results show that our algorithm could improve antibiotic prescribing decisions by combining human judgment with data-driven probability predictions.

The burden of wrist joint dislocations: A nationwide 20-year analysis and review of incidence and injury mechanism.

The fragmentation of patient data across multiple healthcare institutions presents a significant challenge to realizing timely and effective treatment. Although electronic medical records have replaced traditional paper records, they often remain isolated within individual hospital information systems, limiting data exchange and preventing physicians from accessing complete medical histories during patient admission. These restrictions hinder the efficiency of diagnosis and treatment, particularly in critical care settings, such as emergency departments. Cloud computing provides a promising solution by enabling controlled electronic medical record sharing, thereby improving the continuity and quality of care. This study presents a system-level, multi-layered hybrid cloud architecture framework designed to facilitate seamless and managed exchange of electronic medical records among healthcare organizations. To further enhance operational efficiency, the system integrates fingerprint authentication based on hashed identifiers for rapid patient identification and an Internet of Things bracelet for real-time monitoring of vital signs. System performance was evaluated using discrete-event simulation implemented in the OMNeT++ framework, with simulation parameters informed by real emergency department data from three hospitals in Saudi Arabia. The evaluation considers multiple workflow scenarios and incorporates repeated simulation runs to assess performance stability. The simulation results indicate consistent reductions in average patient waiting times, while treatment durations remain stable and patient throughput increases. These findings highlight the potential of the proposed framework to enhance electronic medical record management, streamline clinical workflows, and improve operational efficiency in time-critical environments.

ObjectivesThe objective was to develop an artificial intelligence (AI) model for predicting acute coronary occlusion myocardial infarction (OMI) in patients with chest pain at the emergency department (ED), using information that is widely available early in the ED assessment.MethodsIn a cohort of 24,511 consecutive adult ED patients with chest pain from 5 Swedish hospitals, OMI cases were identified through register data and manual review of health records and angiographies. Ambulance patients bypassing the ED due to ST-elevation myocardial infarction (STEMI) were not included in the cohort. A deep-learning AI model was created to predict OMI using the electrocardiogram, optionally combined with other early ED data, including medical history and initial lab values. The model was internally validated on held-out data and compared with the STEMI criteria.ResultsA total of 467 patients (1.9%) were identified as OMI, corresponding to 29% of all acute myocardial infarction cases. The 30-day mortality rate was 6.6% for OMI, compared with 3.3% for non-OMI. Only 5.4% of the OMI cases received angiography within the guideline-recommended maximum of 90 minutes after ED arrival. The AI model achieved an area under the receiver operating characteristic (AUC) of 95.3% (95% CI, 93.8%-97.3%), with a sensitivity of 62% compared with 27% for the STEMI criteria (difference 34.5%; 95% CI, 22.9%-45.2%) at the same specificity (97.4%).ConclusionOur AI model identified OMI in ED patients with chest pain with an AUC of 95%, doubling sensitivity compared with the STEMI criteria at the same specificity. Using the model could reduce time to intervention, as only about 1 in 20 OMI cases currently receive timely angiography.

This study aims to investigate the effect of seasonal influenza vaccination on hospitalization rates among patients presenting to the emergency department with influenza-like illness. A retrospective, single-center observational study was conducted, involving adult patients with influenza (ICD-10 codes J10 and J11) diagnosed in the emergency department between May 2024 and April 2025. Clinical and demographic information was collected from electronic records, and vaccination status was confirmed through follow-up phone calls. To tackle the "zero event" problem-no hospitalizations among vaccinated individuals-advanced statistical modeling was employed, including standard logistic regression, and Bayesian logistic regression using Markov Chain Monte Carlo (MCMC) simulations. Odds ratios (OR) and 95% Highest Density Intervals (HDI) were calculated to assess the effectiveness of vaccination. A total of 878 patients were enrolled: 3.3% (n = 29) received vaccinations, while 2.7% (n = 24) required hospitalization. None of the vaccine recipients were hospitalized. Standard logistic regression indicated that age was a significant indicator of hospitalization. Furthermore, Bayesian logistic regression followed, which confirmed vaccination's statistically significant protective effect. The OR for vaccination was 0.526 (95% HDI: 0.336-0.739), indicating a 47% reduction in hospitalization risk among vaccinated individuals. Seasonal influenza vaccination was significantly associated with a lower risk of hospitalization in patients presenting with influenza-like illness to the emergency department. These findings support public health initiatives to enhance influenza vaccine coverage, particularly for the elderly.

Ankle sprain is damage to ligaments due to excessive stress, with an incidence rate of 2-7 cases per 1000 people per year based on emergency department data. Basketball athletes have a high risk of developing an ankle sprain. Good knowledge of handling, especially the PRICE method, is essential for athletes. Against this background and the lack of similar research, this research was conducted to fill this knowledge gap. Analyze the relationship between PRICE knowledge and the application of ankle sprain treatment in basketball athletes in Ternate. The design in this study uses observational analysis with a cross sectional design and uses fisher's exact test, the research was conducted in Ternate City in January 2024. The research sample was 60 samples using accidental sampling techniques. Data collection through interviews using questionnaires. Based on 60 samples, the majority were male (86.7%), aged 17-25 years old (75%), PRICE knowledge in the good category (51.7%), application of ankle sprain treatment in the category of adequate (61.7%). Based on bivariate analysis, a p-value of 0.003 (p&lt;0.005) was obtained in PRICE knowledge with the application of ankle sprain treatment in basketball athletes. There is a relationship between PRICE's knowledge and the application of ankle sprain treatment in basketball athletes in Ternate.

Background Rising global temperatures have resulted in an increase in the frequency and severity of heatwaves. Pregnancy is a time of substantial physiological change and thus maternal and neonatal health outcomes are susceptible to adverse effects of heat. There is a lack of knowledge about thermoregulation in pregnancy, including how effectively pregnant people adapt in different climate contexts and what specific environmental conditions present the largest thermoregulatory challenges. The primary aim of this study is to examine the associations between exposure to heatwaves during pregnancy and maternal, neonatal, and child health outcomes in Western Australia (WA) climatic regions. Methods All neonates born in WA between 1991 and 2023 will be identified from perinatal records (~960,000 neonates). Perinatal records will be linked to weather data from the ERA5 database using Statistical Area 2 geocoding to identify heatwave exposed pregnancies. Commonly used definitions of heatwave exposure will be tested against three neonatal outcomes (pre-term birth, low birth weight, and congenital anomalies). From this, a single definition will be selected to examine maternal, neonatal, and child health outcomes using linked hospital, emergency department, and education assessment data, as well as data from the WA congenital anomalies and death register. The association between heatwaves during pregnancy and health outcomes will be performed using generalised estimating equations, adjusting for covariates including cigarette smoking, parity, maternal age, and Aboriginality where appropriate. Stratification by socio-economic status and geographic region will also be carried out. Discussion Examining the association between exposure to heatwaves in pregnancy and maternal, neonatal and child outcomes is important given the increasing frequency of heatwaves. The study will highlight which heat definitions confers the highest vulnerability as well as demographic and geographic subgroups who are particularly vulnerable to the effects of heatwaves. It will provide valuable information for future planning, including targets for intervention.

Abstract Background&amp;#xD;The 2014 Hazelwood coal mine fire shrouded the regional Victoria, Australia town of Morwell in smoke for six weeks. Building on previous analyses, we explored linked survey and healthcare data to investigate longer-term effects of fire-related PM2.5 exposure on ambulance attendances, emergency department presentations, and hospital admissions.&amp;#xD;Methods&amp;#xD;Hazelwood Health Study Adult Survey cohort data were combined with modelled air pollution data to estimate individual fire-related PM2.5 exposure and linked with ambulance, emergency department, and hospital admission data up to 2022. Associations between fire-related PM2.5 and health service use were evaluated using a recurrent event survival model over the 8-year post-fire period and within time strata (&lt;2.5 years, 2.5-5 years, &gt;5 years) and by condition, sex, and age. &amp;#xD;Results&amp;#xD;There were no detectable effects of fire-related PM2.5 on overall health service use, although there were some for specific conditions including increased ambulance attendances for respiratory conditions, which attenuated over time, and injuries, which were sustained over the follow-up period. PM2.5 exposure was associated with an increase in cardiovascular emergency presentations, but only among women, which attenuated after 2.5 years; among men, PM2.5 exposure was associated with reductions in cardiovascular emergency presentations and hospital admissions, as well as reduced mental health hospital admissions that were sustained over the follow-up period.&amp;#xD;Conclusions&amp;#xD;The temporary increase in respiratory ambulance attendances associated with PM2.5 exposure aligned with previous findings on the mine fire’s effects on respiratory health. The increase in injury-related ambulance attendances is somewhat in line with prior findings and may be attributable to neurological damage from fire-related PM2.5 exposure. Reductions in cardiovascular health service use were likely attributable to immortal time bias due to increased cardiovascular death in the first months after the fire.

Neighborhood spikes in drug and alcohol overdoses and subsequent suicide: A serial cross-sectional study of California ZIP codes.

The seasonality of many respiratory pathogen outbreaks, such as influenza and respiratory syncytial virus, is driven by climate factors, such as specific humidity or temperature. However, it remains unclear whether climate plays a role in determining the seasonality of COVID-19, given that the evolution of novel strains likely plays a key role in shaping outbreak dynamics. Here we use Emergency Department data to explore spatial differences in COVID-19 outbreak dynamics over three years, from April 2022 through March 2025. We observe that outbreak patterns varied across latitude, with southern states experiencing larger summer peaks and northern states facing more evenly distributed summer to winter outbreaks or larger winter peaks. We find that specific humidity and temperature at the state level are significantly associated with observed differences in ED visits with a COVID-19 diagnosis, even after controlling for state-level variation in vaccination status. Our results imply a role for climate in influencing COVID-19 outbreak dynamics. We anticipate these findings will provide a foundational understanding of factors shaping SARS-CoV-2 transmission as COVID-19 becomes endemic in the United States.