Triage Level Research Articles

A randomized controlled trial on using predictive algorithm to adapt level of psychological care for community college students: STAND triaging and adapting to level of care study protocol

BackgroundThere is growing interest in using personalized mental health care to treat disorders like depression and anxiety to improve treatment engagement and efficacy. This randomized controlled trial will compare a traditional symptom severity decision-making algorithm to a novel multivariate decision-making algorithm for triage to and adaptation of mental health care. The stratified levels of care include a self-guided online wellness program, coach-guided online cognitive behavioral therapy, and clinician-delivered psychotherapy with or without pharmacotherapy. The novel multivariate algorithm will be comprised of baseline (for triage and adaptation) and time-varying variables (for adaptation) in four areas: social determinants of mental health, early adversity and life stressors, predisposing, enabling, and need influences on health service use, and comprehensive mental health status. The overarching goal is to evaluate whether the multivariate algorithm improves adherence to treatment, symptoms, and functioning above and beyond the symptom-based algorithm.Methods/designThis trial will recruit a total of 1000 participants over the course of 5 years in the greater Los Angeles Metropolitan Area. Participants will be recruited from a highly diverse sample of community college students. For the symptom severity approach, initial triaging to level of care will be based on symptom severity, whereas for the multivariate approach, the triaging will be based on a comprehensive set of baseline measures. After the initial triaging, level of care will be adapted throughout the duration of the treatment, utilizing either symptom severity or multivariate statistical approaches. Participants will complete computerized assessments and self-report questionnaires at baseline and up to 40 weeks. The multivariate decision-making algorithm will be updated annually to improve predictive outcomes.DiscussionResults will provide a comparison on the traditional symptom severity decision-making and the novel multivariate decision-making with respect to treatment adherence, symptom improvement, and functional recovery. Moreover, the developed multivariate decision-making algorithms may be used as a template in other community college settings. Ultimately, findings will inform the practice of level of care triage and adaptation in psychological treatments, as well as the use of personalized mental health care broadly.Trial registrationClinicalTrials.gov NCT05591937, submitted August 2022, published October 2022.

A simplified scoring model for predicting bacteremia in the unscheduled emergency department revisits: The SADFUL score

Bacteremia is a severe complication of infectious disease. Patients with a high bacteremia risk in the emergency department (ED) but misidentified would lead to the unscheduled revisits. This study aimed to develop a simplified scoring model to predict bacteremia in patients with unscheduled ED revisits. Adult patients with unscheduled ED revisits within 72h with a final diagnosis of infectious disease were retrospectively included. The development cohort included patients visiting the ED from January 1, 2019 to December 31, 2021. Internal validation was performed in patients visiting the ED from January 1, 2022 to March 31, 2022. Variables including demographics, pre-comorbidities, triage levels, vital signs, chief complaints, and laboratory data in the index visit were analyzed. Bacteremia was the primary outcome determined by blood culture in either index visits or revisits. The SADFUL score for predicting bacteremia comprised the following predictors: "S"egmented neutrophil percentage (+3 points), "A"ge>55 years (+1 point), "D"iabetes mellitus (+1 point), "F"ever (+2 points), "U"pper respiratory tract symptoms (-2 points), and "L"eukopenia (2 points). The area under receiver operating characteristic curve with 95% confidence interval in the development (1802 patients, 190 [11%] with bacteremia) and the validation cohort (134 patients, 17 [13%] with bacteremia) were 0.78 (0.74-0.81) and 0.79 (0.71-0.88), respectively. The SADFUL score is a simplified useful tool for predicting bacteremia in patients with unscheduled ED revisits. The scoring model could help ED physicians decrease misidentification of patients at a high risk for bacteremia and potential complications.

An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration.

Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations. This retrospective cohort study included 134,112 ED visits with at least one electrocardiography (ECG) and chest X-ray (CXR) in a medical center from 2012 to 2022. Additionally, an independent community hospital provided 45,614 ED visits as an external validation set. We trained an eXtreme gradient boosting (XGB) model using initial assessment data to predict all-cause mortality in 7 days. Two deep learning models (DLMs) using ECG and CXR were trained to stratify mortality risks. The dynamic triage levels were based on output from the XGB-triage and DLMs from ECG and CXR. During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of the XGB-triage model was >0.866; furthermore, the AUCs of DLMs using ECG and CXR were >0.862 and >0.886, respectively. The dynamic triage scale provided a higher C-index (0.914-0.920 vs. 0.827-0.843) than the original one and demonstrated better predictive ability for 5-year mortality, 30-day ED revisit, and 30-day discharge. The AI-based risk scale provides a more accurate and dynamic stratification of mortality risk in ED patients, particularly in identifying patients who tend to be overlooked due to atypical symptoms.

User-Initiated Symptom Assessment With an Electronic Symptom Checker: Protocol for a Mixed Methods Validation Study.

The national Omaolo digital social welfare and health care service of Finland provides a symptom checker, Omaolo, which is a medical device (based on Duodecim Clinical Decision Support EBMEDS software) with a CE marking (risk class IIa), manufactured by the government-owned DigiFinland Oy. Users of this service can perform their triage by using the questions in the symptom checker. By completing the symptom checker, the user receives a recommendation for action and a service assessment with appropriate guidance regarding their health problems on the basis of a selected specific symptom in the symptom checker. This allows users to be provided with appropriate health care services, regardless of time and place. This study describes the protocol for the mixed methods validation process of the symptom checker available in Omaolo digital services. This is a mixed methods study using quantitative and qualitative methods, which will be part of the clinical validation process that takes place in primary health care centers in Finland. Each organization provides a space where the study and the nurse triage can be done in order to include an unscreened target population of users. The primary health care units provide walk-in model services, where no prior phone call or contact is required. For the validation of the Omaolo symptom checker, case vignettes will be incorporated to supplement the triage accuracy of rare and acute cases that cannot be tested extensively in real-life settings. Vignettes are produced from a variety of clinical sources, and they test the symptom checker in different triage levels by using 1 standardized patient case example. This study plan underwent an ethics review by the regional permission, which was requested from each organization participating in the research, and an ethics committee statement was requested and granted from Pirkanmaa hospital district's ethics committee, which is in accordance with the University of Tampere's regulations. Of 964 clinical user-filled symptom checker assessments, 877 cases were fully completed with a triage result, and therefore, they met the requirements for clinical validation studies. The goal for sufficient data has been reached for most of the chief symptoms. Data collection was completed in September 2019, and the first feasibility and patient experience results were published by the end of 2020. Case vignettes have been identified and are to be completed before further testing the symptom checker. The analysis and reporting are estimated to be finalized in 2024. The primary goals of this multimethod electronic symptom checker study are to assess safety and to provide crucial information regarding the accuracy and usability of the Omaolo electronic symptom checker. To our knowledge, this will be the first study to include real-life clinical cases along with case vignettes. DERR1-10.2196/41423.

Association between red blood cell distribution width and mortality in COVID-19

Objective: In this study, we consider the heterogeneity of the results on the prognostic role of red cell distribution width (RDW) in COVID-19, and this study aimed to determine the relationship between RDW and mortality in COVID-19 patients. Methods: This descriptive-analytical cross-sectional study was conducted on hospitalized COVID-19 patients whose disease was confirmed by PCR test positivity. Demographic characteristics, underlying diseases, and laboratory tests were the study variables. Patient outcomes were defined as in-hospital mortalities. The level of the Red Blood Cell Distribution Width-corpuscular volume (RDW-CV) in complete blood count (CBC) on arrival and the second day of hospitalization of patients and other variables were recorded in the checklist. The in-hospital mortalities were designated as the outcomes of the study. Results: In this study, 201 patients were included, and the in-hospital mortality was 28 (13.9%). The correlation between variables in predicting in-hospital mortality using univariate regression analysis showed that there is a significant relationship between triage level, chronic lung disease and age, oxygen saturation, lymphocytes, BUN, and Cr CRP with in-hospital mortality; however, this significant relationship with RDW values of the patients on arrival and the first day of the hospitalization as well as △RDW (RDW on arrival – the first-day RDW) was not observed. Finally, based on the multivariate logistic regression model, there was a significant relationship between the triage level, age, and oxygen saturation with in-hospital mortality. Conclusion: In COVID-19 patients, factors such as triage level, age, and oxygen saturation can play a significant role in predicting in-hospital mortality. The role of RDW in determining the outcome of the disease is still unclear.

Online optimisation for ambulance routing in disaster response with partial or no information on victim conditions

In response to mass casualty incidents, medical aid must be provided to numerous victims synchronously under challenging circumstances including uncertainty about the condition of victims. Therefore, it is essential to have decision support tools which can generate fast solutions under uncertainty and utilise the available medical resources efficiently to provide victims with the needed treatments. We introduce an online optimisation problem for routing and scheduling of the ambulances under uncertainty about the triage levels and required treatment times of the victims in mass casualty incidents. Due to the lack of information in the initial emergency response phase, we assume that the triage level and treatment time of each victim can be disclosed online only once the condition of a victim is closely assessed by the medical team on one of the ambulances at the casualty location. We investigate this problem under two different scenarios with partial and no information about the conditions of victims. We follow the theoretical competitive analysis framework for online optimisation and prove the lower bounds on the competitive ratio of deterministic and randomised online solutions for both cases of partial and no prior information. Next, we introduce three novel online heuristics to solve this problem. We verify the quality of our online solutions against the offline optimal solutions that are provided under complete information on a comprehensive set of 1296 instances from the literature. Finally, we draw our conclusions in regard to the suitability of each of our solutions in various scenarios of information availability with different numbers of victims.

Is Mean Platelet Volume a Useful Predictor of Hospital Mortality in Covid-19 Patients?

Background: Elevated Mean Platelet Volume (MPV) can indicate the activation of platelets and the coagulation cascade, which has been suggested as a predictor of mortality in some diseases. The aim of this study was to investigate the role of MPV in predicting hospital mortality in patients with Covid-19.
 Methods: This was a cross-sectional single-center retrospective study. The data of all patients with a PCR-confirmed diagnosis of Covid-19 hospitalized within a 3-month period were analyzed. The patients’ files were reviewed, and the data were recorded into a checklist, including demographic characteristics, underlying diseases, and laboratory test results. Hospital mortality was designated as the main outcome of the study.
 Results: A total of 201 Covid-19 patients, confirmed by PCR, were included in this study. In-hospital mortality was observed in 28 patients (13.9%). The results of univariate regression analysis revealed that the triage level, history of chronic pulmonary disease, age, oxygen saturation, lymphocyte count, BUN, Cr, and CRP were significantly related to in-hospital mortality. However, MPV showed no significant relationship with in-hospital mortality.
 Conclusion: Low triage level, advanced age and hypoxia predicted a higher risk of in-hospital mortality in Covid-19 patients. The role of MPV as a predictor of patient outcome in Covid-19 remains inconclusive, necessitating further studies with larger sample sizes.

Leaving Without Being Seen From the Pediatric Emergency Department: A New Baseline

BackgroundLeft without being seen (LWBS) rates are an important quality metric for pediatric emergency departments (EDs), with high-acuity LWBS children representing a patient safety risk. Since July 2021, our ED experienced a surge in LWBS after the most stringent COVID-19 quarantine restrictions ended. ObjectiveWe assessed changes in LWBS rates and examined associations of system factors and patient characteristics with LWBS. MethodsWe performed a retrospective study in a large, urban pediatric ED for all arriving patients, comparing the following three time-periods: before COVID-19 (PRE, January 2018–February 2020), during early COVID-19 (COVID, March 2020–June 2021), and after the emergence of COVID-19 variants and re-emergence of seasonal viruses (POST, July 2021–December 2021). We compared descriptive statistics of daily LWBS rates, patient demographic characteristics, and system characteristics. Negative binomial (system factors) and logistic regression (patient characteristics) models were developed to evaluate the associations between system factors and LWBS, and patient characteristics and LWBS, respectively. ResultsMean daily LWBS rates changed from 1.8% PRE to 1.4% COVID to 10.7% during POST. Rates increased across every patient demographic and triage level during POST, despite a decrease in daily ED volume compared with PRE. LWBS rates were significantly associated with patients with an Emergency Severity Index score of 2, mean ED census, and staff productivity within multiple periods. Patient characteristics associated with LWBS included lower assigned triage levels and arrival between 8 pm and 4 am. ConclusionsLWBS rates have shown a large and sustained increase since July 2021, even for high-acuity patients. We identified system factors that may provide opportunities to reduce LWBS. Further work should develop strategies to prevent LWBS in at-risk patients.

Comparison between local and three validated triage systems in an emergency department for 2126 children under 3 months.

Triage of patients less than 3 months old was not already studied. The aim was to evaluate Paediatric Emergency Department triage in patients less than 3 months old and newborns using a local system in comparison with three validated paediatric triage systems (Canadian Triage and Acuity Scale, Manchester Triage System and Emergency Severity Index) and to determine inter-system agreement. All admissions of patients less than 3 months old admitted to the Emergency Department of the Saint Vincent University Hospital between April 2018 and December 2019 were included. The local triage system level was determined prospectively for comparison with retrospectively calculated triage levels of the validated systems. Hospitalisation rates were compared and inter-system agreements determined. Among emergency admissions, 2126 were included (55% males, mean age 45 days). Hospitalisation rate increased with priority severity as determined by all triage systems studied. Cohen's kappa showed slight agreement between the local triage system and the Canadian Triage and Acuity Scale, Emergency Severity Index and Manchester Triage System (weighted kappa = 0.133, 0.185 and 0.157 respectively). Whether prospective or retrospective triage used, the systems studied exhibited good association with hospitalisation rate for patients aged less than 3 months and newborn infants.

Meeting the Behavioral Health Needs of Health Care Workers During COVID-19 by Leveraging Chatbot Technology: Development and Usability Study.

During the COVID-19 pandemic, health care systems were faced with the urgent need to implement strategies to address the behavioral health needs of health care workers. A primary concern of any large health care system is developing an easy-to-access, streamlined system of triage and support despite limited behavioral health resources. This study provides a detailed description of the design and implementation of a chatbot program designed to triage and facilitate access to behavioral health assessment and treatment for the workforce of a large academic medical center. The University of California, San Francisco (UCSF) Faculty, Staff, and Trainee Coping and Resiliency Program (UCSF Cope) aimed to provide timely access to a live telehealth navigator for triage and live telehealth assessment and treatment, curated web-based self-management tools, and nontreatment support groups for those experiencing stress related to their unique roles. In a public-private partnership, the UCSF Cope team built a chatbot to triage employees based on behavioral health needs. The chatbot is an algorithm-based, automated, and interactive artificial intelligence conversational tool that uses natural language understanding to engage users by presenting a series of questions with simple multiple-choice answers. The goal of each chatbot session was to guide users to services that were appropriate for their needs. Designers developed a chatbot data dashboard to identify and follow trends directly through the chatbot. Regarding other program elements, website user data were collected monthly and participant satisfaction was gathered for each nontreatment support group. The UCSF Cope chatbot was rapidly developed and launched on April 20, 2020. As of May 31, 2022, a total of 10.88% (3785/34,790) of employees accessed the technology. Among those reporting any form of psychological distress, 39.7% (708/1783) of employees requested in-person services, including those who had an existing provider. UCSF employees responded positively to all program elements. As of May 31, 2022, the UCSF Cope website had 615,334 unique users, with 66,585 unique views of webinars and 601,471 unique views of video shorts. All units across UCSF were reached by UCSF Cope staff for special interventions, with >40 units requesting these services. Town halls were particularly well received, with >80% of attendees reporting the experience as helpful. UCSF Cope used chatbot technology to incorporate individualized behavioral health triage, assessment, treatment, and general emotional support for an entire employee base (N=34,790). This level of triage for a population of this size would not have been possible without the use of chatbot technology. The UCSF Cope model has the potential to be scaled, adapted, and implemented across both academically and nonacademically affiliated medical settings.

Pulmonary embolism in United States emergency departments, 2010–2018

Little is known about pulmonary embolism (PE) in the United States emergency department (ED). This study aimed to describe the disease burden (visit rate and hospitalization) of PE in the ED and to investigate factors associated with its burden. Data were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2010 to 2018. Adult ED visits with PE were identified using the International Classification of Diseases codes. Analyses used descriptive statistics and multivariable logistic regression accounting for the NHAMCS’s complex survey design. Over the 9-year study period, there were an estimated 1,500,000 ED visits for PE, and the proportion of PE visits in the entire ED population increased from 0.1% in 2010–2012 to 0.2% in 2017–2018 (P for trend = 0.002). The mean age was 57 years, and 40% were men. Older age, obesity, history of cancer, and history of venous thromboembolism were independently associated with a higher proportion of PE, whereas the Midwest region was associated with a lower proportion of PE. The utilization of chest computed tomography (CT) scan appeared stable, which was performed in approximately 43% of the visits. About 66% of PE visits were hospitalized, and the trend remained stable. Male sex, arrival during the morning shift, and higher triage levels were independently associated with a higher hospitalization rate, whereas the fall and winter months were independently associated with a lower hospitalization rate. Approximately 8.8% of PE patients were discharged with direct-acting oral anticoagulants. The ED visits for PE continued to increase despite the stable trend in CT use, suggesting a combination of prevalent and incident PE cases in the ED. Hospitalization for PE remains common practice. Some patients are disproportionately affected by PE, and certain patient and hospital factors are associated with hospitalization decisions.

Correlates ofthe Veterans Visiting Emergency Departments inTaiwan: A Comparison Before andAfter the Coronavirus Disease 2019 Pandemic.

Taiwan has a substantial number of veterans, but knowledge regarding their emergency department (ED) visits during the coronavirus disease 2019 (COVID-19) pandemic remains limited. This study examined the characteristics of veterans' ED visits during Taiwan's COVID-19 epidemic. This was a cross-sectional study conducted at the ED of a large veteran medical center located in Taipei, Taiwan, from May 2018 to October 2021. We analyzed the numbers and features of visits in summer and autumn according to the first wave of the COVID-19 epidemic in Taiwan in 2021. Medical institutions were positively associated with veteran status. Emergency department complaints of trauma (adjusted odds ratio [AOR] = 1.15, 95% CI: 1.06-1.25; summer P < .01) and chest pain/tightness (AOR = 1.65, 95% CI: 1.45-1.87; summer P < .01; AOR = 1.4, 95% CI: 1.26-1.55; P < .01) were associated with increased odds of being a veteran. Triage levels above 2 were positively associated with veteran status in the autumn model (AOR = 1.14, 95% CI: 1.07-1.22; P < .01). Patients hospitalized after ED visits were associated with reduced odds of veteran status (P < .01). Those who spent a long time in the ED were more likely to be veterans than those who spent a shorter time in the ED (P < .01). Veterans were less likely to visit the ED regardless of the time frame of the study period (P < .01), except during the COVID-19 outbreak in the autumn (2019-2020). The distinctions in ED visits highlighted the individuality of veterans' medical needs. Our findings suggest that the veteran medical system can add to the focus on improving senior-friendly care, fall prevention, quality of life of institutionalized veterans, access for homeless veterans, and care for ambulatory care-sensitive conditions.

Deep learning in optical coherence tomography: Where are the gaps?

Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post-processing algorithms. Currently, there is a growing interest in applying deep learning (DL) methods to analyse OCT images automatically. This review summarises the trends in DL-based OCT image analysis in ophthalmology, discusses the current gaps, and provides potential research directions. DL in OCT analysis shows promising performance in several tasks: (1) layers and features segmentation and quantification; (2) disease classification; (3) disease progression and prognosis; and (4) referral triage level prediction. Different studies and trends in the development of DL-based OCT image analysis are described and the following challenges are identified and described: (1) public OCT data are scarce and scattered; (2) models show performance discrepancies in real-world settings; (3) models lack of transparency; (4) there is a lack of societal acceptance and regulatory standards; and (5) OCT is still not widely available in underprivileged areas. More work is needed to tackle the challenges and gaps, before DL is further applied in OCT image analysis for clinical use.

The severity of clinical symptoms according to cancer diagnosis in fever patients visiting the emergency department: a retrospective analysis

Purpose: This study aimed to understand the general characteristics and biomarkers of inflammation in adult patients who visited the emergency department with fever and to determine whether the severity of clinical symptoms varies according to cancer diagnosis. Methods: Data were collected retrospectively from 4,002 adult patients with fever who visited the emergency department at a tertiary hospital from January 2018 to December 2018 using medical records. Results: On average, cancer patients were older than non-cancer patients (p &lt; .001), and differences were observed between cancer and non-cancer patients in the origin of fever and biomarkers associated with inflammation. A higher proportion of cancer patients than non-cancer patients had a Korean Triage and Acuity Scale level of 1 to 3 (p &lt; .001), and more cancer patients than non-cancer patients met two or more criteria for systemic inflammatory response syndrome (p = .001). More life-saving interventions in the emergency department were required in cancer patients than in non-cancer patients (p &lt; .001), and cancer patients spent more time in the emergency department than non-cancer patients (p &lt; .001). Conclusion: This study showed that the general characteristics and biomarkers of inflammation differed among adult patients with fever depending on cancer diagnosis. Furthermore, among adult patients with fever, cancer patients had more severe clinical symptoms than non-cancer patients. The results of this study are hoped to be helpful as a basis of nursing knowledge for adult patients with fever in the emergency department and as evidence for the classification of severity in patients with fever according to cancer diagnosis.

Predictive Validity of a New Triage System for Outcomes in Patients Visiting Pediatric Emergency Departments: A Nationwide Study in Korea.

Triage is essential for rapid and efficient intervention in patients visiting an emergency department. In Korea, since 2016, the Pediatric Korean Triage and Acuity Scale (PedKTAS) has been implemented nationwide for the triage of patients visiting pediatric emergency departments (PEDs). The aim of this study was to evaluate the validity of the PedKTAS in patients who visit PEDs. This study was a retrospective observational study of national registry data collected from all emergency medical centers and institutions throughout Korea. We analyzed data from patients aged <15 years who visited emergency departments nationwide from January 2016 to December 2019. The hospitalization and intensive care unit (ICU) admission rates were analyzed on the basis of triage level. In total, 5,462,964 pediatric patients were included in the analysis. The hospitalization rates for PedKTAS Levels 1-5, were 63.5%, 41.1%, 17.0%, 6.5%, and 3.7%, respectively, and were significantly different (p < 0.001). The ICU admission rates for PedKTAS Levels 1-5 were 14.4%, 6.0%, 0.3%, 0.1%, and 0.1%, respectively, and were significantly different (p < 0.001). The hospitalization and ICU admission rates were highest for PedKTAS Level 1, and differences were significant based on the level. We identified that the PedKTAS is suitable for predicting the emergency status of pediatric patients who visit PEDs.

Characteristics of patients who visited emergency departments for headache in South Korea: A descriptive cross-sectional study.

This study aimed to describe the characteristics of adult patients who visit emergency departments (EDs) for non-traumatic headache in South Korea. Little is known about East Asian patients who visit EDs for headache. This observational, descriptive, cross-sectional study retrospectively analyzed 2019 National Emergency Department Information System data, including age, sex, co-occurring fever, symptom duration, insurance type, transportation mode, ED level, triage level, ED visit time, specialist consultations at the ED, disposition from the ED, and outcomes. The proportion of patients with a life-threatening secondary headache and the diagnostic codes were investigated. A total of 227,288 patients were observed in this study, accounting for 2.2% (227,288/10,238,360) of all ED visits. Females (63.1%; 143,493/227,288) visited EDs more than males, and patients aged 50-60 years (21.0%; 47,637/227,288) visited most frequently. A total of 61.5% (93,789/151,494) of ED visits were within 24 h after headache onset, and 57.6% (131,056/227,288) of visits were assigned to non-urgent triage levels. The most frequent discharge codes were "R51: Headache (not better specified)" from the ED and ward, and "I60: Subarachnoid hemorrhage" from the intensive care unit. The rate of migraine diagnosis was 7.2% (16,471/227,288). A total of 3.1% (7153/227,288) of patients were diagnosed with life-threatening secondary headaches, most commonly subarachnoid hemorrhage (1.2%; 2744/227,288) and cerebral infarction (0.6%; 1341/227,288). In South Korea, the characteristics of patients who visited the ED for non-traumatic headache were not very different from those in existing studies; however, patients tended to visit EDs early and be classified as non-urgent, and emergency physicians tended to enter the diagnosis code "R51: Headache (not better specified)", resulting in a much lower rate of migraine diagnoses. Non-urgent early visitors coded with "R51" may include those who have not yet been diagnosed with primary headache and have not been treated, but who need further research. Not applicable.

Considerations for Pediatric Mass Shooting Triage Training: A Qualitative Analysis

Introduction:Accurate triage is crucial for pediatric patients because their physiological differences make them more vulnerable to traumatic injury and mortality. However, pediatric trauma patients are challenging for EMS personnel for several reasons including infrequent clinical encounters and inadequate training. Despite the need for increased training, little is known about EMS readiness to perform triage and lifesaving interventions during pediatric mass casualty incidents (MCIs). Simulation skills assessment correlates with EMS performance in the field and can be used to determine MCI readiness.Pediatric patients are often omitted from MCI training and protocols. Feedback from EMS clinicians who participate in pediatric MCI simulations may be useful for educators seeking to optimize pediatric mass shooting triage training.Method:This was an observational study assessing EMS clinician accuracy in triaging eight children and two adults in a mass shooting simulation involving intimate partner violence (IPV) set at a private residence. Participating EMS clinicians were attendees of continuing education classes at Yale New Haven Health Centers for EMS. Participants worked in pairs, and triage decisions were documented during the simulation with an evaluation tool and video recording. After the simulation, pairs completed the demographic survey and completed a semi-structured debriefing. Facilitator prompts included correct triage level for each patient, the role IPV plays in mass shootings, and the participants feedback. Recordings of the debriefings were transcribed and analyzed using grounded theory. During the evaluation process, the major themes will be identified and coded. The transcriptions will be re-evaluated and any additional sub-themes will be identified and coded.Results:As of November 2022, eight paramedics have participated with more sessions scheduled for spring 2023. A preliminary review indicates potential themes will fall under the categories of simulation implementation and clinical approach to triage.Conclusion:These findings may assist EMS agencies with their pediatric MCI training and response.

Risk stratification of patients with chest pain who have an unscheduled revisit to the emergency department

AimsAcute cardiovascular (CV) emergencies are critical conditions that require urgent attention in the emergency department (ED). Failure to make a timely diagnosis may result in unscheduled ED revisits and severe outcomes. Therefore, this study aimed to investigate the risk factors associated with potentially missed acute CV emergencies. Methods and resultsThis retrospective study enrolled adult patients who presented with chest pain and returned to the ED within 72 h. Demographic information, pre-existing medical conditions, chief complaints, triage level and vital signs, electrocardiography (ECG) reports, and laboratory data were collected from medical charts by independent physicians. The primary outcome was the diagnosis of acute CV diseases, including ACS, pulmonary embolism, unstable arrhythmia, acute decompensated heart failure, and aortic dissection. Multivariable logistic regression was used to analyze the association between variables and acute CV emergencies. A total of 453 eligible patients were included, with 60 (13.2%) patients diagnosed as acute CV emergencies at the ED revisit. Risk factors for acute CV emergencies included male gender (adjusted odds ratio [aOR] = 2.71, 95% confidence interval [CI] = 1.17–6.25), abnormal ECG rhythm (aOR = 10.33, 95% CI = 4.68–22.83), and abnormal changes in high sensitivity Troponin-T (hs-cTnT) during sequential follow-up (aOR = 6.52, 95% CI = 2.19–19.45). ConclusionsMale gender, abnormal ECG rhythm, and a significant increase in sequential follow-up hs-cTnT levels were identified as significant risk factors for acute CV emergencies. ED physicians should recognize these high-risk patients with chest pain to prevent misdiagnosis and potential severe complications.

Five Level Triage vs. Four Level Triage in a Quaternary Emergency Department: National Analysis on Waiting Time, Validity, and Crowding-The CREONTE (Crowding and RE-Organization National TriagE) Study Group.

Background and Objectives: Triage systems help provide the right care at the right time for patients presenting to emergency departments (EDs). Triage systems are generally used to subdivide patients into three to five categories according to the system used, and their performance must be carefully monitored to ensure the best care for patients. Materials and Methods: We examined ED accesses in the context of 4-level (4LT) and 5-level triage systems (5LT), implemented from 1 January 2014 to 31 December 2020. This study assessed the effects of a 5LT on wait times and under-triage (UT) and over-triage (OT). We also examined how 5LT and 4LT systems reflected actual patient acuity by correlating triage codes with severity codes at discharge. Other outcomes included the impact of crowding indices and 5LT system function during the COVID-19 pandemic in the study populations. Results: We evaluated 423,257 ED presentations. Visits to the ED by more fragile and seriously ill individuals increased, with a progressive increase in crowding. The length of stay (LOS), exit block, boarding, and processing times increased, reflecting a net raise in throughput and output factors, with a consequent lengthening of wait times. The decreased UT trend was observed after implementing the 5LT system. Conversely, a slight rise in OT was reported, although this did not affect the medium-high-intensity care area. Conclusions: Introducing a 5LT improved ED performance and patient care.

Study about the Manchester Triage System subtriage in patients that visited the Emergency Department due to headache

IntroductionHeadache is a frequent cause of consultation; it is important to detect patients with secondary headache, particularly high-risk secondary headache. Such systems as the Manchester Triage System (MTS) are used for this purpose. This study aims to evaluate the frequency of undertriage in patients attending the emergency department due to headache. Material and methodsWe studied a series of consecutive patients who came to the emergency department with headache and presenting some warning sign, defined as the presence of signs leading the physician to request an emergency neuroimaging study and/or assessment by the on-call neurologist. The reference diagnosis was established by neurologists. We evaluated the MTS triage level assigned and the presence of warning signs that may imply a higher level than that assigned. ResultsWe registered a total of 1120 emergency department visits due to headache, and 248 patients (22.8%) were eligible for study inclusion. Secondary headache was diagnosed in 126 cases (50.8% of the sample; 11.2% of the total), with 60 cases presenting high-risk secondary headache (24.2%; 5.4%). According to the MTS, 2 patients were classified as immediate (0.8%), 26 as very urgent (10.5%), 147 as urgent (59.3%), 68 as normal (27.4%), and 5 as not urgent (2%). The percentage of patients undertriaged was 85.1% in the very urgent classification level and 23.3% in the urgent level. ConclusionDuring the study period, at least one in 10 patients attending the emergency department due to headache had secondary headache; one in 20 had high-risk secondary headache. The MTS undertriaged most patients with warning signs suggesting a potential emergency.