Influence of time-to-diagnosis on time-to-percutaneous coronary intervention for emergency department ST-elevation myocardial infarction patients: Time-to-electrocardiogram matters.

Objectives: Earlier electrocardiogram (ECG) acquisition for ST-elevation myocardial infarction (STEMI) is associated with earlier percutaneous coronary intervention (PCI) and better patient outcomes. However, the exact relationship between timely ECG and timely PCI is unclear. Methods: We quantified the influence of door-to-ECG (D2E) time on ECG-to-PCI balloon (E2B) intervention in this three-year retrospective cohort study, including patients from 10 geographically diverse emergency departments (EDs) co-located with a PCI center. The study included 576 STEMI patients excluding those with a screening ECG before ED arrival or non-diagnostic initial ED ECG. We used a linear mixed-effects model to evaluate D2E's influence on E2B with piecewise linear terms for D2E times associated with time intervals designated as ED intake (0-10 min), triage (11-30 min), and main ED (>30 min). We adjusted for demographic and visit characteristics, past medical history, and included ED location as a random effect. Results: The median E2B interval was longer (76 vs 68 min, p < 0.001) in patients with D2E >10 min than in those with timely D2E. The proportion of patients identified at the intake, triage, and main ED intervals was 65.8%, 24.9%, and 9.7%, respectively. The D2E and E2B association was statistically significant in the triage phase, where a 1-minute change in D2E was associated with a 1.24-minute change in E2B (95% confidence interval [CI]: 0.44-2.05, p = 0.003). Conclusion: Reducing D2E is associated with a shorter E2B. Targeting D2E reduction in patients currently diagnosed during triage (11-30 min) may be the greatest opportunity to improve D2B and could enable 24.9% more ED STEMI patients to achieve timely D2E.

The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.

BACKGROUND: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. OBJECTIVE: Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. METHODS: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. RESULTS: A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. CONCLUSIONS: LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

Shorter Door-to-ECG Time Is Associated with Improved Mortality in STEMI Patients.

Background: Delayed intervention for ST-segment elevation myocardial infarction (STEMI) is associated with higher mortality. The association of door-to-ECG (D2E) with clinical outcomes has not been directly explored in a contemporary US-based population. Methods: This was a three-year, 10-center, retrospective cohort study of ED-diagnosed patients with STEMI comparing mortality between those who received timely (<10 min) vs. untimely (>10 min) diagnostic ECG. Among survivors, we explored left ventricular ejection fraction (LVEF) dysfunction during the STEMI encounter and recovery upon post-discharge follow-up. Results: Mortality was lower among those who received a timely ECG where one-week mortality was 5% (21/420) vs. 10.2% (26/256) among those with untimely ECGs (p = 0.016), and in-hospital mortality was 6.0% (25/420) vs. 10.9% (28/256) (p = 0.028). Data to compare change in LVEF metrics were available in only 24% of patients during the STEMI encounter and 46.5% on discharge follow-up. Conclusions: D2E within 10 min may be associated with a 50% reduction in mortality among ED STEMI patients. LVEF dysfunction is the primary resultant morbidity among STEMI survivors but was infrequently assessed despite low LVEF being an indication for survival-improving therapy. It will be difficult to assess the impact of STEMI care interventions without more consistent LVEF assessment.

Maximizing Equity in Acute Coronary Syndrome Screening across Sociodemographic Characteristics of Patients.

We compared four methods to screen emergency department (ED) patients for an early electrocardiogram (ECG) to diagnose ST-elevation myocardial infarction (STEMI) in a 5-year retrospective cohort through observed practice, objective application of screening protocol criteria, a predictive model, and a model augmenting human practice. We measured screening performance by sensitivity, missed acute coronary syndrome (ACS) and STEMI, and the number of ECGs required. Our cohort of 279,132 ED visits included 1397 patients who had a diagnosis of ACS. We found that screening by observed practice augmented with the model delivered the highest sensitivity for detecting ACS (92.9%, 95%CI: 91.4-94.2%) and showed little variation across sex, race, ethnicity, language, and age, demonstrating equity. Although it missed a few cases of ACS (7.6%) and STEMI (4.4%), it did require ECGs on an additional 11.1% of patients compared to current practice. Screening by protocol performed the worst, underdiagnosing young, Black, Native American, Alaskan or Hawaiian/Pacific Islander, and Hispanic patients. Thus, adding a predictive model to augment human practice improved the detection of ACS and STEMI and did so most equitably across the groups. Hence, combining human and model screening--rather than relying on either alone--may maximize ACS screening performance and equity.

Beyond chest pain: Incremental value of other variables to identify patients for an early ECG.

BACKGROUND: Chest pain (CP) is the hallmark symptom for acute coronary syndrome (ACS) but is not reported in 20-30% of patients, especially women, elderly, non-white patients, presenting to the emergency department (ED) with an ST-segment elevation myocardial infarction (STEMI). METHODS: We used a retrospective 5-year adult ED sample of 279,132 patients to explore using CP alone to predict ACS, then we incrementally added other ACS chief complaints, age, and sex in a series of multivariable logistic regression models. We evaluated each model's identification of ACS and STEMI. RESULTS: Using CP alone would recommend ECGs for 8% of patients (sensitivity, 61%; specificity, 92%) but missed 28.4% of STEMIs. The model with all variables identified ECGs for 22% of patients (sensitivity, 82%; specificity, 78%) but missed 14.7% of STEMIs. The model with CP and other ACS chief complaints had the highest sensitivity (93%) and specificity (55%), identified 45.1% of patients for ECG, and only missed 4.4% of STEMIs. CONCLUSION: CP alone had highest specificity but lacked sensitivity. Adding other ACS chief complaints increased sensitivity but identified 2.2-fold more patients for ECGs. Achieving an ECG in 10 min for patients with ACS to identify all STEMIs will be challenging without introducing more complex risk calculation into clinical care.

Teaching Principles of Medical Innovation and Entrepreneurship Through Hackathons: Case Study and Qualitative Analysis.

BACKGROUND: Innovation and entrepreneurship training are increasingly recognized as being important in medical education. However, the lack of faculty comfort with the instruction of these concepts as well as limited scholarly recognition for this work has limited the implementation of curricula focused on these skills. Furthermore, this lack of familiarity limits the inclusion of practicing physicians in health care innovation, where their experience is valuable. Hackathons are intense innovation competitions that use gamification principles to increase comfort with creative thinking, problem-solving, and interpersonal collaboration, but they require further exploration in medical innovation. OBJECTIVE: To address this, we aimed to design, implement, and evaluate a health care hackathon with 2 main goals: to improve emergency physician familiarity with the principles of health care innovation and entrepreneurship and to develop innovative solutions to 3 discrete problems facing emergency medicine physicians and patients. METHODS: We used previously described practices for conducting hackathons to develop and implement our hackathon (HackED!). We partnered with the American College of Emergency Physicians, the Stanford School of Biodesign, and the Institute of Design at Stanford (d.school) to lend institutional support and expertise in health care innovation to our event. We determined a location, time frame, and logistics for the competition and settled on 3 use cases for teams to work on. We planned to explore the learning experience of participants within a pragmatic paradigm and complete an abductive thematic analysis using data from a variety of sources. RESULTS: HackED! took place from October 1-3, 2022. In all, 3 teams developed novel solutions to each of the use cases. Our investigation into the educational experience of participants suggested that the event was valuable and uncovered themes suggesting that the learning experience could be understood within a framework from entrepreneurship education not previously described in relation to hackathons. CONCLUSIONS: Health care hackathons appear to be a viable method of increasing physician experience with innovation and entrepreneurship principles and addressing complex problems in health care. Hackathons should be considered as part of educational programs that focus on these concepts.

Using Machine Learning to Predict Hospital Disposition With Geriatric Emergency Department Innovation Intervention.

STUDY OBJECTIVE: The Geriatric Emergency Department Innovations (GEDI) program is a nurse-based geriatric assessment and care coordination program that reduces preventable admissions for older adults. Unfortunately, only 5% of older adults receive GEDI care because of resource limitations. The objective of this study was to predict the likelihood of hospitalization accurately and consistently with and without GEDI care using machine learning models to better target patients for the GEDI program. METHODS: We performed a cross-sectional observational study of emergency department (ED) patients between 2010 and 2018. Using propensity-score matching, GEDI patients were matched to other older adult patients. Multiple models, including random forest, were used to predict hospital admission. Multiple second-layer models, including random forest, were then used to predict whether GEDI assessment would change predicted hospital admission. Final model performance was reported as the area under the curve using receiver operating characteristic models. RESULTS: We included 128,050 patients aged over 65 years. The random forest ED disposition model had an area under the curve of 0.774 (95% confidence interval [CI] 0.741 to 0.806). In the random forest GEDI change-in-disposition model, 24,876 (97.3%) ED visits were predicted to have no change in disposition with GEDI assessment, and 695 (2.7%) ED visits were predicted to have a change in disposition with GEDI assessment. CONCLUSION: Our machine learning models could predict who will likely be discharged with GEDI assessment with good accuracy and thus select a cohort appropriate for GEDI care. In addition, future implementation through integration into the electronic health record may assist in selecting patients to be prioritized for GEDI care.

Predicting Early Seizures After Intracerebral Hemorrhage with Machine Learning.

BACKGROUND: Seizures are a harmful complication of acute intracerebral hemorrhage (ICH). "Early" seizures in the first week after ICH are a risk factor for deterioration, later seizures, and herniation. Ideally, seizure medications after ICH would only be administered to patients with a high likelihood to have seizures. We developed and validated machine learning (ML) models to predict early seizures after ICH. METHODS: We used two large datasets to train and then validate our models in an entirely independent test set. The first model ("CAV") predicted early seizures from a subset of variables of the CAVE score (a prediction rule for later seizures)-cortical hematoma location, age less than 65 years, and hematoma volume greater than 10 mL-whereas early seizure was the dependent variable. We attempted to improve on the "CAV" model by adding anticoagulant use, antiplatelet use, Glasgow Coma Scale, international normalized ratio, and systolic blood pressure ("CAV + "). For each model we used logistic regression, lasso regression, support vector machines, boosted trees (Xgboost), and random forest models. Final model performance was reported as the area under the receiver operating characteristic curve (AUC) using receiver operating characteristic models for the test data. The setting of the study was two large academic institutions: institution 1, 634 patients; institution 2, 230 patients. There were no interventions. RESULTS: Early seizures were predicted across the ML models by the CAV score in test data, (AUC 0.72, 95% confidence interval 0.62-0.82). The ML model that predicted early seizure better in the test data was Xgboost (AUC 0.79, 95% confidence interval 0.71-0.87, p = 0.04) compared with the CAV model AUC. CONCLUSIONS: Early seizures after ICH are predictable. Models using cortical hematoma location, age less than 65 years, and hematoma volume greater than 10 mL had a good accuracy rate, and performance improved with more independent variables. Additional methods to predict seizures could improve patient selection for monitoring and prophylactic seizure medications.

Health behavior practice among understudied Chinese and Filipino Americans with cardiometabolic diseases.

Lifestyle modification and health behavior practice among the individuals with cardiometabolic diseases (CMD) are important for secondary prevention and disease control. This study was designed to investigate and compare health behavior practices among Chinese and Filipino Americans with CMD. Three hundred seventy-four Asian Americans (211 Chinese and 163 Filipino) who reside in the greater Philadelphia region and had either CMD or no identified disease were included in the study. Information on smoking, alcohol intake, physical activity, and salt and sweets consumption was collected, as well as demographic and acculturative characteristics. Of the 374 participants, 241 (64.4%) had CMD and 133 (35.6%) had no identified disease. The majority of Chinese and Filipino Americans with CMD failed to meet the dietary and physical activity guidelines, and only a small percentage of them restricted their amount of salt added to food and amount of sweets consumption. Compared to participants with no disease, Chinese participants with CMD were more likely to "never" add salt to food (AOR 4.42 compared to "frequently"). Filipino Americans with CMD were less likely to be those who "never" consume sweets than those who frequently consume sweets (AOR = 0.12). Among the participants with CMD, Chinese participants with CMD were less likely to restrict drinking (AOR 0.11) than Filipinos with CMD. The findings suggest that tailored interventions for Chinese and Filipino Americans with CMD should be developed to enhance their compliance to behavioral guidelines to prevent further disease progression and complications.

A novel pharmacological strategy by PTEN inhibition for improving metabolic resuscitation and survival after mouse cardiac arrest.

Sudden cardiac arrest (SCA) is a leading cause of death in the United States. Despite return of spontaneous circulation, patients die due to post-SCA syndrome that includes myocardial dysfunction, brain injury, impaired metabolism, and inflammation. No medications improve SCA survival. Our prior work suggests that optimal Akt activation is critical for cooling protection and SCA recovery. Here, we investigate a small inhibitor of PTEN, an Akt-related phosphatase present in heart and brain, as a potential therapy in improving cardiac and neurological recovery after SCA. Anesthetized adult female wild-type C57BL/6 mice were randomized to pretreatment of VO-OHpic (VO) 30 min before SCA or vehicle control. Mice underwent 8 min of KCl-induced asystolic arrest followed by CPR. Resuscitated animals were hemodynamically monitored for 2 h and observed for 72 h. Outcomes included heart pressure-volume loops, energetics (phosphocreatine and ATP from (31)P NMR), protein phosphorylation of Akt, GSK3ß, pyruvate dehydrogenase (PDH) and phospholamban, circulating inflammatory cytokines, plasma lactate, and glucose as measures of systemic metabolic recovery. VO reduced deterioration of left ventricular maximum pressure, maximum rate of change in the left ventricular pressure, and Petco2 and improved 72 h neurological intact survival (50% vs. 10%; P < 0.05). It reduced plasma lactate, glucose, IL-1ß, and Pre-B cell colony enhancing factor, while increasing IL-10. VO increased phosphorylation of Akt and GSK3ß in both heart and brain, and cardiac phospholamban phosphorylation while reducing p-PDH. Moreover, VO improved cardiac bioenergetic recovery. We concluded that pharmacologic PTEN inhibition enhances Akt activation, improving metabolic, cardiovascular, and neurologic recovery with increased survival after SCA. PTEN inhibitors may be a novel pharmacologic strategy for treating SCA.