Triage Algorithm Research Articles

P-1786. Implementation of a Clinical Pathway to Educate Clinicians on Changing Epidemiology

Abstract Background Since 8/31/2022, 39,998 new arrivals have made Chicago their home, with the majority coming from Latin America. Epidemiological diversity in these countries of origin as well as infection risks encountered on the journey to the US have resulted in increased requests for advice from Infectious Diseases (ID) specialists and a need for rapid education of clinicians, raising the question of how to best address this knowledge gap. Initial Triage Algorithm Clinicians are directed to this algorithm when first presented with a febrile new arrival or returning traveler. Methods Using Chicago Department of Public Health (CDPH) data, Epic® (Verona, WI), CDC’s online information for clinicians, and expert advice, we quantified relevant epidemiologic changes, curated a list of high priority topics, and developed an algorithmic approach to fever in new arrivals for the general clinician. After three cycles of revision within our interdisciplinary Antimicrobial Stewardship Program (ASP), this document was uploaded to the highly utilized ASP website for all providers on 4/26/2024. Differential Diagnosis Assistance after Malaria Ruled Out Clinicians are directed to this figure after completing the initial algorithm and ruling out malaria to consider additional neglected tropical diseases in their differential diagnosis. Results On review of epidemiologic data, we found that Chicago had 0 Measles cases in 2019, 5 cases in 2023, and 64 cases in 2024 as of 4/23/2024. Varicella incidence in Chicago rose from a median of 53 cases/year during 2005-2022 to roughly 400 cases in 2023, with 322 confirmed cases arising from new arrival shelters, and nearly half occuring among adults 18 years and older. University of Chicago Medicine (UCM) diagnosed an average of 3.7 cases of malaria from 2010-2021, 7 cases in 2022, and 9 cases in 2023. The New Arrivals Clinical Pathway, informed by these epidemiologic changes, provides general guidance on diagnosis and treatment for high frequency infections and a basic risk assessment strategy for high consequence pathogens. It also provides instructions on how to isolate individuals based on risk factors and clinical features and directs clinicians to contact Infection Control and ID specialists when appropriate. New Arrivals Clinical Pathway Table of Contents, Section 1: Fever in a Returning Traveler/New Arrival Schema When first opening the New Arrivals Clinical Pathway, clinicians are presented with these stepwise instructions in section 1, easily navigable via hyperlinks to relevant content. Conclusion The ongoing influx of new arrivals into Chicago, coupled with a housing crisis and congregant living conditions in shelters have rapidly changed the epidemiology of infectious diseases in Chicago, in turn creating a need for rapid education of clinicians. Using an easily accessible, readable, algorithmic model assists non-ID providers and ID trainees in triage and initial management of these increasingly common diseases. New Arrivals Clinical Pathway Table of Contents, Section 2: List of Neglected/Tropical Diseases to Consider Section 2 provides a convenient reference for neglected tropical diseases for all clinicians taking care of recent arrivals and returning travelers. It is organized by geographic region, and provides details regarding diagnosis, treatment, and infection control requirements for each disease. Disclosures Jennifer Pisano, MD, Beckman Coulter: Advisor/Consultant

How Close Are We to Patient-Side Troponin Testing?

Laboratory-based high-sensitivity cardiac troponin testing has been the pillar for emergency stratification of suspected acute coronary syndrome for well over a decade. Point-of-care troponin assays achieving the requisite analytical sensitivity have recently been developed and could accelerate such assessment. This review summarises the latest assays and describes their potential diverse clinical utility in the emergency department, community healthcare, pre-hospital, and other hospital settings. It outlines the current clinical data but also highlights the evidence gap, particularly the need for clinical trials using whole blood, that must be addressed for safe and successful implementation of point-of-care troponin analysis into daily practice. Additionally, how point-of-care troponin testing can be coupled with advances in biosensor technology, cardiovascular screening, and triage algorithms is discussed.

Machine learning outperforms the Canadian Triage and Acuity Scale (CTAS) in predicting need for early critical care.

This study investigates the potential to improve emergency department (ED) triage using machine learning models by comparing their predictive performance with the Canadian Triage Acuity Scale (CTAS) in identifying the need for critical care within 12h of ED arrival. Three machine learning models (LASSO regression, gradient-boosted trees, and a deep learning model with embeddings) were developed using retrospective data from 670,841 ED visits to the Jewish General Hospital from June 2012 to Jan 2021. The model outcome was the need for critical care within the first 12h of ED arrival. Metrics, including the areas under the receiver-operator characteristic curve (ROC) and precision-recall curve (PRC) were used for performance evaluation. Shapley additive explanation scores were used to compare predictor importance. The three machine learning models (deep learning, gradient-boosted trees and LASSO regression) had areas under the ROC of 0.926 ± 0.003, 0.912 ± 0.003 and 0.892 ± 0.004 respectively, and areas under the PRC of 0.27 ± 0.01, 0.24 ± 0.01 and 0.23 ± 0.01 respectively. In comparison, the CTAS score had an area under the ROC of 0.804 ± 0.006 and under the PRC of 0.11 ± 0.01. The predictors of most importance were similar between the models. Machine learning models outperformed CTAS in identifying, at the point of ED triage, patients likely to need early critical care. If validated in future studies, machine learning models such as the ones developed here may be considered for incorporation in future revisions of the CTAS triage algorithm, potentially improving discrimination and reliability.

Rapid Decision Algorithm for Patient Triage during Ebola Outbreaks.

The low specificity of Ebola virus disease clinical signs increases the risk for nosocomial transmission to patients and healthcare workers during outbreaks. Reducing this risk requires identifying patients with a high likelihood of Ebola virus infection. Analyses of retrospective data from patients suspected of having Ebola virus infection identified 13 strong predictors and time from disease onset as constituents of a prediction score for Ebola virus disease. We also noted 4 highly predictive variables that could distinguish patients at high risk for infection, independent of their scores. External validation of this algorithm on retrospective data revealed the probability of infection continuously increased with the score.

Impact of discordance in cardiac troponin T versus I on the performance of triage pathways for myocardial infarction

Abstract Background Biological, analytical, and regulatory differences between high-sensitivity cardiac troponin T (hs-cTnT) and hs-cTnI could lead to an underestimation of the performance of a hs-cTnT-based triage-algorithm if evaluated against a hs-cTnI-based reference standard, and vice versa. Unfortunately, the impact of hs-cTnT/hs-cTnI-discordance on diagnostic performance measures is largely unknown. Methods We quantified the impact of hs-cTnT/hs-cTnI-discordance on the diagnostic performance of the three most widely used and guideline recommended hs-cTnT/I-based triage algorithms for patients presenting with symptoms suggestive of myocardial infarction (MI): the ESC 0/1h-algorithm, the ESC 0/2h-algorithm and the High-STEACS pathway. For the central adjudication of the final diagnosis (reference standard), two independent cardiologists had access to all available clinical information and applied the 4th universal definition of MI, one time with serial hs-cTnT-Elecsys measurements (gold standard T) and a second time with serial hs-cTnI-Architect measurements (gold standard I). The primary diagnostic endpoint for the rule-out of NSTEMI was sensitivity, while for rule-in of NSTEMI specificity. Results Among 7728 eligible patients, the final diagnosis was NSTEMI in 1463 patients (18.9%). Of these, NSTEMI was diagnosed with only one of the two adjudication methods (either gold standard T or I) in 167 (11.4%) patients. When the three hs-cTnT-based early-triage algorithms were evaluated using gold standard T and gold standard I, sensitivity was very high and comparable. In contrast, when the three hs-cTnI-based early-triage algorithms were evaluated using gold standard T and gold standard I, sensitivity was again very high, but consistently lower when using gold standard T as compared to gold standard I. The difference in sensitivity was 1.1% (95%CI, 0.6–1.7) for ESC hs-cTnI-0/1h (Table 1A), 1.0% (95% CI, 0.5—1.6) for ESC hs-cTnI-0/2h (Table 1B), and 2.6% (95%CI, 1.7—3.5) for High-STEACS (Table 2). The differences observed in specificity mirrored those for sensitivity, i.e. always lower specificity when using the other hs-cTnT/I-assay for adjudication. Conclusion The impact of hs-cTnT/hs-cTnI-discordance on the diagnostic performance of hs-cTnT/I-based triage algorithms is modest, but large enough to result in a fallacious underestimation of the rule-out safety when evaluating hs-cTnI-based strategies using a hs-cTnT-based reference standard.

Barriers and facilitators for implementation of AI algorithms for ECG analysis and triage in patients with chest pain

Abstract Introduction Numerous artificial intelligence (AI) algorithms have been developed to assist in clinical decision-making, with a focus on areas like electrocardiogram (ECG) analysis due to the signal complexity and the varying levels of expertise among healthcare professionals. However, ensuring acceptance of these algorithms by end-users is paramount for successful implementation. Purpose This study aimed to identify barriers and facilitators related to implementation of an AI algorithm for ECG analysis and triage in patients with chest pain. Methods A three-round modified electronic Delphi study was conducted among Dutch potential end-users of an ECG-AI algorithm, including physicians, nurses and ambulance professionals. During the first round, participants brainstormed on barriers and facilitators, which were subsequently mapped to the Consolidated Framework for Implementation Research (CFIR). In the following two rounds, participants reviewed the identified barriers and facilitators for relevance and ranked them according to their importance. Results Twenty-five out of the initial forty respondents completed all three rounds, resulting in a dropout rate of 38%. Four barriers and twelve facilitators were identified. The most important facilitator was "Faster recognition of subtle ECG abnormalities", while "Poor model performance" emerged as the most important barrier. The most frequently mentioned corresponding CFIR domains were the innovation and inner setting domains. Conclusion The identification of barriers and facilitators provides valuable insights into the acceptance and utilisation of ECG-AI algorithms by end-users. By considering the CFIR domains, including innovation and inner setting, this study offers a comprehensive understanding of the contextual factors that influence the successful implementation of AI algorithms in healthcare settings. Addressing these barriers and facilitators will be crucial to facilitating future implementation processes.Barriers and facilitators

Automated Unmanned Aerial System for Camera-Based Semi-Automatic Triage Categorization in Mass Casualty Incidents

Using drones to obtain vital signs during mass-casualty incidents can be extremely helpful for first responders. Thanks to technological advancements, vital parameters can now be remotely assessed rapidly and robustly. This motivates the development of an automated unmanned aerial system (UAS) for patient triage, combining methods for the automated detection of respiratory-related movements and automatic classification of body movements and body poses with an already published algorithm for drone-based heart rate estimation. A novel UAS-based triage algorithm using UAS-assessed vital parameters is proposed alongside a robust UAS-based respiratory rate assessment and pose classification algorithm. A pilot concept study involving 15 subjects and 30 vital sign measurements under outdoor conditions shows that with our approach, an overall triage classification accuracy of 89% and an F1 score of 0.94 can be achieved, demonstrating its basic feasibility.

Mental health screening for parents following surgical neonatal intensive care unit (NICU) discharge

Admission to the surgical neonatal intensive care unit (sNICU) is a stressful experience. Care is often complex, with inherent risks and potential complications. This study describes the implementation of an outpatient mental health screening process for parents of infants admitted to a sNICU. Parents of infants aged >34 weeks gestation with a congenital anomaly requiring neonatal surgery participated in this prospective observational study. Standardised measures to screen for parenting stress (Parenting Stress Index™ Fourth Edition Short Form) and depressive symptoms (Center for Epidemiologic Studies Depression Scale) were administered at the first outpatient visit scheduled when the infant's corrected gestational age was 4 months. A triage algorithm was developed, to review the initial screening results prompting appropriate action and intervention. Positive screens were triaged as evaluate (assess within 48 h), targeted information (email contact), or escalate response (same day risk assessment). Demographic factors associated with parental stress and depressive symptoms were explored. Forty parents (response rate: 88 %) participated in screening. A high portion of parents (52.5 %) required secondary screening, for parenting stress (n = 10), depressive symptoms (n = 5) or both stress and depressive symptoms (n = 6). Socioeconomic disadvantage was positively associated with parenting stress (p = 0.02) and greater depressive symptoms with parent education levels (p = 0.01). Results indicate screening of parent mental health in the outpatient setting is feasible. Use of a triage algorithm helped prioritise parent follow-up and facilitate workflows. Parent mental health screening should be prioritised within and beyond the sNICU to support family and infant outcomes during this critical period of development.

Applying queueing theory to evaluate wait-time-savings of triage algorithms

In the past decade, artificial intelligence (AI) algorithms have made promising impacts in many areas of healthcare. One application is AI-enabled prioritization software known as computer-aided triage and notification (CADt). This type of software as a medical device is intended to prioritize reviews of radiological images with time-sensitive findings, thus shortening the waiting time for patients with these findings. While many CADt devices have been deployed into clinical workflows and have been shown to improve patient treatment and clinical outcomes, quantitative methods to evaluate the wait-time-savings from their deployment are not yet available. In this paper, we apply queueing theory methods to evaluate the wait-time-savings of a CADt by calculating the average waiting time per patient image without and with a CADt device being deployed. We study two workflow models with one or multiple radiologists (servers) for a range of AI diagnostic performances, radiologist’s reading rates, and patient image (customer) arrival rates. To evaluate the time-saving performance of a CADt, we use the difference in the mean waiting time between the diseased patient images in the with-CADt scenario and that in the without-CADt scenario as our performance metric. As part of this effort, we have developed and also share a software tool to simulate the radiology workflow around medical image interpretation, to verify theoretical results, and to provide confidence intervals for the performance metric we defined. We show quantitatively that a CADt triage device is more effective in a busy, short-staffed reading setting, which is consistent with our clinical intuition and simulation results. Although this work is motivated by the need for evaluating CADt devices, the evaluation methodology presented in this paper can be applied to assess the time-saving performance of other types of algorithms that prioritize a subset of customers based on binary outputs.

Prospective Evaluation of Artificial Intelligence Triage of Incidental Pulmonary Emboli on Contrast-Enhanced CT Examinations of the Chest or Abdomen.

BACKGROUND. Artificial intelligence (AI) algorithms improved detection of incidental pulmonary embolism (IPE) on contrast-enhanced CT (CECT) examinations in retrospective studies; however, prospective validation studies are lacking. OBJECTIVE. The purpose of this study was to assess the effect on radiologists' real-world diagnostic performance and report turnaround times of a radiology department's clinical implementation of an AI triage system for detecting IPE on CECT examinations of the chest or abdomen. METHODS. This prospective single-center study included consecutive adult patients who underwent CECT of the chest or abdomen for reasons other than pulmonary embolism (PE) detection from May 12, 2021, to June 30, 2021 (phase 1), or from September 30, 2021, to December 4, 2021 (phase 2). Before phase 1, the radiology department installed a commercially available AI triage algorithm for IPE detection that automatically processed CT examinations and notified radiologists of positive results through an interactive floating widget. In phase 1, the widget was inactive, and radiologists interpreted examinations without AI assistance. In phase 2, the widget was activated, and radiologists interpreted examinations with AI assistance. A review process involving a panel of radiologists was implemented to establish the reference standard for the presence of IPE. Diagnostic performance and report turnaround times were compared using the Pearson chi-square test and Wilcoxon rank sum test, respectively. RESULTS. Phase 1 included 1467 examinations in 1434 patients (mean age, 53.8 ± 18.5 [SD] years; 753 men, 681 women); phase 2 included 3182 examinations in 2886 patients (mean age, 55.4 ± 18.2 years; 1520 men, 1366 women). The frequency of IPE was 1.4% (20/1467) in phase 1 and 1.6% (52/3182) in phase 2. Radiologists without AI, in comparison to radiologists with AI, showed significantly lower sensitivity (80.0% vs 96.2%, respectively; p = .03), without a significant difference in specificity (99.9% vs 99.9%, p = .58), for the detection of IPE. The mean report turnaround time for IPE-positive examinations was not significantly different between radiologists without AI and radiologists with AI (78.3 vs 74.6 minutes, p = .26). CONCLUSION. An AI triage system improved radiologists' sensitivity for IPE detection on CECT examinations of the chest or abdomen without significant change in report turnaround times. CLINICAL IMPACT. This prospective real-world study supports the use of AI assistance for maximizing IPE detection.

A Low-Cost, Low-Tech Virtual Mass Casualty Training Simulation for Undergraduate Medical Education.

Mass casualty incident (MCI) training effectively increases trainees' knowledge and confidence when implemented in a live, in-person setting. In-person MCI training is resource-intensive, but virtual MCI training models are an alternative with similar effectiveness at a lesser cost. However, most of these validated virtual options are based on high-tech virtual reality (VR) programs. We designed and implemented a low-tech, low-cost virtual MCI training model for third-year medical students, using Google JamboardTM (Google, Mountain View, CA) and ZoomTM(Zoom Video Communications, Inc., San Jose, CA) as the primary technological platforms. Learners were instructed on the adult simple triage and rapid treatment (START) and the pediatric JumpSTART triage algorithms over ZoomTM. In small groups, students used a gameboard on Google JamboardTM to simulate a scene at an MCI where they were tasked with triaging 25 patients in 30 minutes, followed by a debriefing session. Students were surveyed on their perceived understanding of the triage algorithms and confidence before and after the event using a 5-point Likert scale (poor, fair, good, very good, and excellent). Pre- and post-event scores were compared using paired, two-sample, and two-tailed t-tests. We considered a p value of <0.01 significant to correct for multiplicity using the Bonferroni method. Learners reported an increased understanding of the triage algorithms (adult and pediatric), scene setup, andunderstanding of emergency medical servicetraining/transportation, as well as increased confidence in participating in an MCI (all p < 0.001). Virtual MCI training can be used as an alternative or supplement to in-person MCI training. Low-tech virtual MCI training models can increase the accessibility of these valuable training activities without sacrificing the quality of learning. Areas for further investigation include low-tech virtual MCI training models' ability to effectively recreate situational and environmental distractions and other challenges better simulated by in-person and high-tech VR training events.

Improving Triage Accuracy in Prehospital Emergency Telemedicine: Scoping Review of Machine Learning-Enhanced Approaches.

Prehospital telemedicine triage systems combined with machine learning (ML) methods have the potential to improve triage accuracy and safely redirect low-acuity patients from attending the emergency department. However, research in prehospital settings is limited but needed; emergency department overcrowding and adverse patient outcomes are increasingly common. In this scoping review, we sought to characterize the existing methods for ML-enhanced telemedicine emergency triage. In order to support future research, we aimed to delineate what data sources, predictors, labels, ML models, and performance metrics were used, and in which telemedicine triage systems these methods were applied. A scoping review was conducted, querying multiple databases (MEDLINE, PubMed, Scopus, and IEEE Xplore) through February 24, 2023, to identify potential ML-enhanced methods, and for those eligible, relevant study characteristics were extracted, including prehospital triage setting, types of predictors, ground truth labeling method, ML models used, and performance metrics. Inclusion criteria were restricted to the triage of emergency telemedicine services using ML methods on an undifferentiated (disease nonspecific) population. Only primary research studies in English were considered. Furthermore, only those studies using data collected remotely (as opposed to derived from physical assessments) were included. In order to limit bias, we exclusively included articles identified through our predefined search criteria and had 3 researchers (DR, JS, and KS) independently screen the resulting studies. We conducted a narrative synthesis of findings to establish a knowledge base in this domain and identify potential gaps to be addressed in forthcoming ML-enhanced methods. A total of 165 unique records were screened for eligibility and 15 were included in the review. Most studies applied ML methods during emergency medical dispatch (7/15, 47%) or used chatbot applications (5/15, 33%). Patient demographics and health status variables were the most common predictors, with a notable absence of social variables. Frequently used ML models included support vector machines and tree-based methods. ML-enhanced models typically outperformed conventional triage algorithms, and we found a wide range of methods used to establish ground truth labels. This scoping review observed heterogeneity in dataset size, predictors, clinical setting (triage process), and reported performance metrics. Standard structured predictors, including age, sex, and comorbidities, across articles suggest the importance of these inputs; however, there was a notable absence of other potentially useful data, including medications, social variables, and health system exposure. Ground truth labeling practices should be reported in a standard fashion as the true model performance hinges on these labels. This review calls for future work to form a standardized framework, thereby supporting consistent reporting and performance comparisons across ML-enhanced prehospital triage systems.

Potential Impact of an Artificial Intelligence-based Mammography Triage Algorithm on Performance and Workload in a Population-based Screening Sample.

To evaluate potential screening mammography performance and workload impact using a commercial artificial intelligence (AI)-based triage device in a population-based screening sample. In this retrospective study, a sample of 2129 women who underwent screening mammograms were evaluated. The performance of a commercial AI-based triage device was compared with radiologists' reports, actual outcomes, and national benchmarks using commonly used mammography metrics. Up to 5 years of follow-up examination results were evaluated in cases to establish benignity. The algorithm sorted cases into groups of "suspicious" and "low suspicion." A theoretical workload reduction was calculated by subtracting cases triaged as "low suspicion" from the sample. At the default 93% sensitivity setting, there was significant improvement (P <.05) in the following triage simulation mean performance measures compared with actual outcome: 45.5% improvement in recall rate (13.4% to 7.3%; 95% CI, 6.2-8.3), 119% improvement in positive predictive value (PPV) 1 (5.3% to 11.6%; 95% CI, 9.96-13.4), 28.5% improvement in PPV2 (24.6% to 31.6%; 95% CI, 24.8-39.1), 20% improvement in sensitivity (83.3% to 100%; 95% CI, 100-100), and 7.2% improvement in specificity (87.2% to 93.5%; 95% CI, 92.4-94.5). A theoretical 62.5% workload reduction was possible. At the ultrahigh 99% sensitivity setting, a theoretical 27% workload reduction was possible. No cancers were missed by the algorithm at either sensitivity. Artificial intelligence-based triage in this simulation demonstrated potential for significant improvement in mammography performance and predicted substantial theoretical workload reduction without any missed cancers.

Implementing virtual primary care: experiences, perspectives and identification of improvement opportunities in an academic primary care setting

BackgroundOne of the biggest changes to primary care triggered by the COVID-19 pandemic was the rapid integration of virtual care (VC). VC offers benefits to patients and providers but implementation...

Artificial Intelligence in Military Medicine.

Artificial intelligence (AI) has garnered significant attention for its pivotal role in the national security and health care sectors. However, its utilization in military medicine remains relatively unexplored despite its immense potential. AI operates through evolving algorithms that process extensive datasets, continuously improving accuracy and emulating human learning processes. Generative AI, a type of machine learning, uses algorithms to generate new content, such as images, text, videos, audio, and computer code. These models employ deep learning to encode simplified representations of training data and generate new work resembling the original without being identical. Although many AI applications in military medicine are theoretical, the U.S. Military has implemented several initiatives, often without widespread awareness among its personnel. This article aims to shed light on two resilience initiatives spearheaded by the Joint Artificial Intelligence Center, which is now the Chief Digital and Artificial Intelligence Office. These initiatives aim to enhance commanders' dashboards for predicting troop behaviors and develop models to forecast troop suicidality. Additionally, it outlines 5 key AI applications within military medicine, including (1) clinical efficiency and routine decision-making support, (2) triage and clinical care algorithms for large-scale combat operations, (3) patient and resource movements in the medical common operating picture, (4) health monitoring and biosurveillance, and (5) medical product development. Even with its promising potential, AI brings forth inherent risks and limitations that require careful consideration and discussion. The article also advocates for a forward-thinking approach for the U.S. Military to effectively leverage AI in advancing military health and overall operational readiness.

Interpretation of elevated baseline concentrations and serial changes of high-sensitivity cardiac troponin T in confirmed muscular dystrophies.

Concentrations of high-sensitivity cardiac troponin T (hs-cTnT) are frequently elevated in stable patients with confirmed muscle dystrophies. However, sparse information is available on the interpretation of serial concentration changes. Hs-cTnT was collected in 35 stable outpatients with confirmed skeletal muscle dystrophies at 0 and 1h and after 6-12months during scheduled outpatient visits. We simulated the effectiveness of the European Society of Cardiology (ESC) 0/1h algorithm and assessed biological variation at 6-12months using two established methods: reference change value (RCV) and minimal important difference (MID). Median baseline hs-cTnT concentrations were 34.4ng/L [inter-quartile range (IQR): 17.5-46.2], and values > 99th percentile upper limit of normal were present in 34 of 35 patients. All patients were stable without cardiovascular adverse events during a follow-up of 6.6months (IQR: 6-7). Median concentration change was 1.9ng/L (IQR: 0.7-3.2) and 0.8ng/L (IQR: 0-7.0) at 60min and 6-9months, respectively. Applying the criteria of the ESC 0/1h algorithm for triage of suspected acute coronary syndrome (ACS) showed poor overall effectiveness of baseline hs-cTnT values. No patient would qualify for rule-out based on hs-cTnT less than the limit of detection, whereas five cases would qualify for rule-in based on hs-cTnT≥52ng/L. Biological variabilities at 6-12months per MID and RCV were 1.2ng/L [95% confidence interval (CI): 0.7-2.1] and 28.6% (95% CI: 27.9-29.6), respectively. A total of 8 (22.9%) and 25 (71.4%) cases exceeded the biological variation range, suggesting some additional myocardial damage. The high prevalence of elevated hs-cTnT could negatively impact the effectiveness of rule-out and rule-in strategies based on a single hs-cTnT value. Knowledge of the physiological and biological variation of hs-cTnT after 6-12months is helpful to detect the progression of cardiac involvement or to search for cardiac complications including but not limited to arrhythmias that may trigger acute or chronic myocardial damage.

Asymptomatic Chronic Large Pericardial Effusions: To Drain or to Observe?

Pericardial effusions, especially large ones, have traditionally been regarded with concern by clinicians due to the sometimes unpredictable development of life-threatening cardiac tamponade. In the European Society of Cardiology Guidelines on pericardial diseases, the simplified algorithm for pericardial effusion triage and management recommends pericardial drainage in cases of cardiac tamponade and/or suspicion of bacterial or neoplastic etiology. In the presence of acute pericarditis, empiric anti-inflammatory treatment should be given, while when a specific indication known to be associated with pericardial effusion is found, then treatment of the underlying cause is indicated. Notably, the most challenging subgroup of patients includes those with large, asymptomatic, C-reactive-protein-negative, idiopathic effusions. In the latter subjects, pericardial drainage is proposed in cases of chronic effusions (lasting more than three months). However, this recommendation is based on scant data stemming from small-sized non-randomized studies. Nevertheless, recent evidence in a larger cohort of patients pointed out that a watchful waiting strategy is a safe option in terms of complication-free survival. This review summarizes the contemporary evidence on this challenging topic and provides recommendations for tailoring individual patient treatments.

Utilization of an Asynchronous Online Learning Module Followed by Simulated Scenario to Train Emergency Medicine Residents in Mass-Casualty Triage.

This is a combined independent study and simulation session designed to teach and drill Mass Casualty Incident (MCI) Triage and is intended for emergency medicine residents at all levels. The training of emergency medicine residents to assume leadership roles in disaster response is important. However, lack of accepted specific educational goals on the national level leads to significant variability between residencies. The purpose of this session is to train EM residents in the use of the Simple Triage and Rapid Treatment (START) and pediatric JumpSTART algorithms for triage in mass casualty incidents (MCIs) using an asynchronous model. By the end of this small group session, learners will be able to: 1) describe START triage for adult MCI victims; 2) describe JumpSTART triage for pediatric MCI victims; 3) demonstrate the ability to apply the START and JumpSTART triage algorithms in a self-directed learning environment; 4) demonstrate the ability to apply the START and JumpSTART triage algorithms in a simulated mass casualty scenario under time constraints; and 5) demonstrate appropriate use of acute life-saving interventions as dictated by the START and JumpSTART triage algorithms in a high-pressure simulated environment. This session utilizes an online independent study module that was created de novo for this specific purpose by the authors followed by a high-pressure in-person simulation session where learners practice applying the START triage model with multiple simulated patients under time constraint. Learner feedback was collected after completion of the session. Retention of learning objectives was tested at four months via multiple-choice quiz. The session was very well received by our residents, who appreciated the opportunity to practice applying START triage under pressure. The average score on the pretest was 49%. Response rates to the post-test were low, but residents scored an average of 73%, indicating a trend towards retention of learning objectives. Overall, the utilization of a de novo online learning module followed by simulation proved to be a well-received method of teaching MCI triage to emergency medicine residents. We consider this to be an effective way to train MCI Triage with minimal in-conference time utilization. We plan to implement this training annually to provide our residents with longitudinal reinforcement of this vital skill. Mass casualty, MCI, triage, START triage, JumpSTART Triage, disaster, disaster preparedness, disaster curriculum, prehospital, EMS.

Development and implementation of a rehabilitation triage algorithm for disasters in the subacute phase: an experience from the 2024 Noto peninsula earthquake

Early rehabilitation during disasters such as earthquakes is an important but often undervalued responsibility of medical teams. Both the development of simple tools for assessing the life functioning of evacuees and the standardization of intervention methods are urgent issues in disaster rehabilitation. The Japanese Rehabilitation Assistance Team (JRAT), an officially recognized organization consisting of 10 suborganisations, was established after gaining important experience with disaster rehabilitation in the aftermath of the Great East Japan Earthquake. We, the Hiroshima JRAT, were dispatched to the Monzen area of Wajima City to support disaster recovery after the 2024 Noto Peninsula Earthquake. Since disaster rehabilitation assistance was not well established in the Monzen area of Wajima City, the medical team consulted JRAT for guidance on triaging evacuees based on their life functioning. We developed a rehabilitation triage algorithm that classifies evacuees into four triage tags based on their ability to perform three simple movements: sitting up, standing up from the floor, and one-leg standing. In our triage algorithm, an inability to sit up is classified as a red tag (difficulty living in a shelter without assistance), an inability to stand up from the floor is classified as a yellow tag (individual intervention: introduction of beds and handrails), one-leg standing for &amp;lt; 2 s is classified as a green tag (group intervention: installation of toilets and bath rails and use of walkers to prevent falls), and standing on one leg for more than 2 s is classified as a white tag (independent in ADLs). We worked with medical teams to triage 922 evacuees in 27 evacuation centers, resulting in the extraction of four red and eight yellow evacuees in 1 day. Our rehabilitation triage system has the potential to be a simple assessment tool for classifying the life functioning of disaster victims and linking them to appropriate supports.

Improving care pathways for children with severe illness through implementation of the ASPIRE mHealth primary ETAT package in Malawi.

Providing emergency care in low resource settings relies on delivery by lower cadres of health workers (LCHW). We describe the development, implementation and mixed methods evaluation of a mobile health (mHealth) triage algorithm based on the WHO Emergency, Triage, Assessment, and Treatment (ETAT) for primary-level care. We conducted an observational study design of implementation research. Key stakeholders were engaged throughout implementation. Clinicians and LCHW at eight primary health centres in Blantyre district were trained to use an mHealth algorithm for triage. An mHealth patient surveillance system monitored patients from presentation through referral to tertiary and final outcome. A total of 209,174 children were recorded by ETAT between April 2017 and September 2018, and 155,931 had both recorded mHealth and clinician triage outcome data. Concordance between mHealth triage by lower cadres of HCW and clinician assessment was 81·6% (95% CI [81·4, 81·8]) over all outcomes (kappa: 0·535 (95% CI [0·530, 0·539]). Concordance for mHealth emergency triage was 0.31 with kappa 0.42. The most common mHealth recorded emergency sign was breathing difficulty (74·1% 95% CI [70·1, 77·9]) and priority sign was raised temperature (76·2% (95% CI [75·9, 76·6]). A total of 1,644 referrals out of 3,004 (54·7%) successfully reached the tertiary site. Both providers and carers expressed high levels of satisfaction with the mHealth ETAT pathway. An mHealth triage algorithm can be used by LCHWs with moderate concordance with clinician triage. Implementation of ETAT through an mHealth algorithm documented successful referrals from primary to tertiary, but half of referred patients did not reach the tertiary site. Potential harms of such systems, such as cases requiring referral being missed during triage, require further evaluation. The ASPIRE mHealth primary ETAT approach can be used to prioritise acute illness and support future resource planning within both district and national health system.