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Importance Emergency department (ED) visits for people with dementia often involve care (including the admission or discharge decision) with lasting effects on quality of life. Home time—a claims-based measure of days alive and outside of institutional care—provides a patient-centered way to assess downstream outcomes of ED care in this population. Objective To examine variations in home time following an ED visit by patient, facility, and visit characteristics among veterans with dementia. Design, Setting, and Participants This national retrospective cohort study used US Department of Veterans Affairs (VA) health care system data sources. Participants were veterans aged 65 to 110 years with dementia with an eligible Veterans Health Administration ED visit in fiscal years 2017 (October 1, 2016, through September 30, 2017) and/or 2018 (October 1, 2017, through September 30, 2018). Data were analyzed from May 1, 2024, to May 15, 2025. Exposures Patient, ED facility, and ED visit factors. Main Outcomes and Measures Home time, operationalized for modeling as days not at home in the 180 days after the index ED visit. Associations between patient, ED facility, and ED visit characteristics and home time were assessed using a negative binomial model. Results The study included 51 707 veterans with dementia (mean [SD] age, 79.9 [8.59] years; 97.6% male; 52.2% married). The mean (SD) number of days not at home within 180 days was 21.7 (34.5). Of the total cohort, 4.5% never returned home in the 180 days after their ED visit, while 18.2% had all subsequent days at home. ED admission (vs discharge) disposition was the factor associated with the largest increase in days not at home (rate ratio [RR], 3.18; 95% CI, 3.08-3.29). Patient factors associated with more days not at home included very high frailty (RR, 1.27; 95% CI, 1.21-1.33), unhoused status (RR, 1.50; 95% CI, 1.39-1.63), unmarried status (never married: RR, 1.24 [95% CI, 1.16-1.32]; divorced, separated, or widowed: RR, 1.24 [95% CI, 1.20-1.28]), and depression (RR, 1.13; 95% CI, 1.09-1.17). Conclusions and Relevance The findings of this cohort study suggest that home time offers a patient-centered measure of post-ED outcomes for individuals with dementia. Future work should refine its application, particularly regarding the ED disposition decision, to better evaluate the long-term impact of ED care decisions.

Abstract Background People living with dementia (PLWD) face short‐term risks following emergency department (ED) visits. However, the long‐term impacts of ED visits on PLWD quality of life are not well characterized. Home time‐ days alive and outside hospitals or other care settings‐ is an emerging measure that can reflect longer‐term outcomes. We aimed to identify patient and ED visit characteristics associated with variation in home time after an ED visit. Method Retrospective observational study of Veteran PLWD with ≥1 VA ED visit between 10/01/2016 and 09/30/2018. We used VA and CMS data sources to define veteran characteristics and outcomes. Primary outcome was home time in the 180 days after an ED visit, defined as days alive and not in an ED, inpatient, post‐acute, or a nursing home setting. We used a negative binomial model to evaluate the associations between covariates and home time with VAMC‐level random effects to account for correlation among patients at the same VAMC. Result Our final cohort comprised 51,707 Veteran PLWD, predominantly male (97.6%) and white (73.0%), with mean age 79.9 (SD 8.59). Most lived in urban areas (74.4%), and 52.2% were married. Characteristics associated with reduced home time included older age (RR 1.06, 95% CI 1.05‐1.07), never married (RR 1.24; 1.16‐1.32) or divorced/widowed (RR 1.24; 1.20, 1.28), depression (RR 1.13; 1.09, 1.17), and ED visits for chronic conditions (RR 1.13; 1.08, 1.18). Female sex (RR 0.88; 0.79‐0.97) and lower clinical frailty (RR 0.79; 0.75, 0.83) were associated with more home time. Conclusion Patient and ED visit characteristics impact home time up to 180 days after an ED visit. Older, frailer, unmarried, male patients and those presenting with chronic conditions experienced reduced home time, suggesting directions for targeted interventions. Efforts such as enhanced discharge planning and addressing psychosocial factors (e.g., depression) could improve quality of life.

Importance The choice of induction agent during rapid sequence intubation (RSI) of critically ill adults may affect clinical outcomes. Although ketamine and etomidate are frequently used for RSI, their comparative effectiveness remains controversial. Objective To compare the safety of etomidate vs ketamine for emergency RSI in critically ill adults. Design, Setting, and Participants This cohort study used a target trial emulation with observational data collected between March 1, 2022, and April 30, 2024, from 18 emergency departments across Brazil, with data from the Brazilian Airway Registry Cooperation. Adults who underwent RSI and received either etomidate or ketamine as a sole hypnotic agent were included; those with preintubation cardiac arrest or immediate postintubation transfer were excluded. Exposure Administration of etomidate or ketamine as the induction agent for RSI. Main Outcomes and Measures The primary outcome was 28-day in-hospital mortality. Secondary outcomes included 7-day in-hospital mortality, first-attempt intubation success, and major adverse events (new hemodynamic instability, severe hypoxemia, and cardiac arrest) within 30 minutes after intubation. Inverse probability of treatment weighting was used to adjust for confounding. Risk ratios (RRs) and risk differences (RDs) with 95% CIs were used to compare outcomes between groups. Results Among 1810 patients (median age, 64 years [IQR, 50-74 years]; 1048 men [57.9%]), 514 received ketamine and 1296 received etomidate. The median shock index was higher in the ketamine group than the etomidate group (0.81 [IQR, 0.65-1.01] vs 0.76 [IQR, 0.59-0.99]), and preintubation vasopressor use was more common in the ketamine group than the etomidate group (191 of 514 [37.2%] vs 391 of 1296 [30.2%]). Weighted 28-day mortality was higher with etomidate than ketamine (60.5% [95% CI, 57.2%-63.8%] vs 54.4% [95% CI, 45.0%-63.9%]; RR, 1.14 [95% CI, 1.03-1.27]; RD, 7.6% [95% CI, 2.0%-13.3%]). Seven-day mortality was also higher with etomidate than ketamine (35.2% [95% CI, 32.0%-38.3%] vs 30.1% [95% CI, 23.5%-36.7%]; RR, 1.19 [95% CI, 1.04-1.35]). New hemodynamic instability within 30 minutes after intubation was more frequent with ketamine (24.2% [95% CI, 20.4%-28.0%] vs 18.9% [95% CI, 16.7%-21.0%]; RR, 0.78 [0.64-0.95]). There were no statistically significant differences in the other secondary outcomes. Conclusions and Relevance In this cohort study of critically ill adults undergoing RSI, etomidate use was associated with higher in-hospital mortality at 7 and 28 days compared with ketamine. These findings highlight the need for definitive randomized clinical trials to compare both agents.

ABSTRACT Background Intracranial and extracranial hemorrhages are significant causes of morbidity and mortality. Emergency medicine pharmacists (EMPs) can play a crucial role in anticoagulation reversal by identifying patients on anticoagulation, selecting the optimal anticoagulation reversal agent, and ensuring adequate dosing and prompt administration. The goal of this systematic review and meta‐analysis was to determine the impact EMPs have on time to anticoagulation reversal and patient outcomes in anticoagulation‐associated hemorrhages. Methods A medical librarian performed a literature search in EBSCO CINAHL, Ovid Cochrane Central Register of Controlled Trials, Ovid Embase, Ovid Medline, Scopus, and Web of Science Core Collection from inception to January 2025 for all types of oral anticoagulants and for life‐threatening bleeding that required emergent reversal. Newcastle‐Ottawa was used to assess the risk of bias, and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used to assess the level of certainty of the evidence. Random effects models estimating mean differences and 95% confidence intervals are reported. Results There were 2445 titles and abstracts screened, with 44 undergoing full‐text evaluation. Six observational studies including 219 patients who received an anticoagulation reversal agent with the presence of an EMP and 198 patients who received a reversal agent without an EMP were included. Time from emergency department (ED) presentation to anticoagulation reversal agent administration (75.60 min, 95% confidence interval [CI]: −101.7 to −49.5) and time from medication order to administration were faster with an EMP (−18.7 min, 95% CI: −23.5 to −14.0). There was no difference in short‐term mortality. The included studies had a high risk of bias and were deemed very low quality of evidence. Conclusion EMP involvement in patient care during anticoagulation reversal of acute hemorrhages improved time to anticoagulation reversal agent administration. Larger scale, higher‐quality studies are necessary to assess the value of EMPs in improving patient‐centered outcomes.

Abstract Heart failure is a common condition that affects millions of people worldwide and is associated with substantial morbidity and mortality. Acute decompensated heart failure refers to the worsening of symptoms that requires changes in drugs or the start of new treatments such as non-invasive positive pressure ventilation. This review summarizes the current data and provides an evidence based approach to the diagnosis and management of acute decompensated heart failure. The review discusses common nomenclature and classifications of the condition, followed by the diagnostic accuracy of medical history, physical examination, electrocardiography, radiographs of the chest, point-of-care ultrasound, and laboratory testing. Current and emerging medical treatments are also discussed.

Importance Validation of prognostic tools is an essential component of their integration into clinical practice. Objective To validate 4 delirium risk stratification tools in an independent emergency department (ED) cohort. Design, Setting, and Participants This prognostic study included a retrospective cohort of patients 65 years or older who presented to an academic ED between January 1, 2021, and December 31, 2024. Data were analyzed from February 10 to August 21, 2025. Exposure Delirium screening included the Delirium Triage Screen followed by the brief Confusional Assessment Method as part of the standard of care. Four delirium risk scores were obtained, including the Kennedy rule, the Zucchelli rule, the Mayo Delirium Prediction (MDP) tools, and the Recognizing Delirium in Emergency Medicine (REDEEM) score. Main Outcomes and Measures Main outcomes included risk score performances and diagnostic test accuracy measured using the area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, negative (NPV) and positive (PPV) predictive values, and positive and negative likelihood ratios (LRs) with 95% CIs. Secondary outcomes were discriminative and calibration measures. Results A total of 44 578 patients were included, of whom 1701 (3.8%) were diagnosed with delirium. The median age was 80.0 (IQR, 75.0-85.0) years, and 22 786 patients (51.1%) were female. The Kennedy rule had an AUROC of 0.777 (95% CI, 0.766-0.789). At a cutoff of 5, sensitivity was 0.55 (95% CI, 0.52-0.57); specificity, 0.85 (95% CI, 0.84-0.85); positive LR, 3.54 (95% CI, 3.37-3.72); negative LR, 0.54 (95% CI, 0.51-0.57); PPV, 0.12 (95% CI, 0.12-0.13); and NPV, 0.98 (95% CI, 0.98-0.98). The Zucchelli tool had an AUROC of 0.701 (95% CI, 0.686-0.713). At a cutoff of 5, sensitivity was 0.68 (95% CI, 0.66-0.70); specificity, 0.66 (95% CI, 0.65-0.66); positive LR, 1.99 (95% CI, 1.92-2.06); negative LR, 0.49 (95% CI, 0.45-0.52); PPV, 0.07 (95% CI, 0.07-0.08); and NPV, 0.98 (95% CI, 0.98-0.98). The MDP tool had an AUROC of 0.898 (95% CI, 0.891-0.905). At a 30% cutoff, sensitivity was 0.51 (95% CI, 0.48-0.53); specificity, 0.95 (95% CI, 0.95-0.95); positive LR, 9.69 (95% CI, 9.11-10.31); negative LR, 0.52 (95% CI, 0.50-0.55); PPV, 0.28 (95% CI, 0.26-0.29); and NPV, 0.98 (95% CI, 0.98-0.98). The REDEEM score demonstrated the highest AUROC at 0.921 (95% CI, 0.914-0.929). Using a cutoff of 11 or greater, sensitivity was 0.83 (95% CI, 0.81-0.85); specificity, 0.92 (95% CI, 0.91-0.92), negative LR, 0.18 (95% CI, 0.17-0.20); positive LR, 9.91 (95% CI, 9.54-10.29); PPV, 0.28 (95% CI, 0.27-0.29); and NPV, 0.99 (95% CI, 0.99-0.99). Conclusions and Relevance In this prognostic study comparing 4 delirium tools to improve delirium detection and identify high-risk patients, the REDEEM score and the MDP tool had better performance for delirium detection in the ED. These findings lay the groundwork for integrating validated risk stratification into ED workflows to improve early delirium detection and inform prevention strategies for high-risk older adults.

ABSTRACT Background The Geriatric Emergency Department (ED) guidelines recommend screening older adults for delirium using evidence‐based screening tools, but these tools are often underutilized. We aimed to identify barriers and facilitators to implementing an electronic health record (EHR)‐based delirium screening tool as intended. Methods This mixed methods and focused ethnographic study combined a review of structured delirium screening data with observations of real‐time delirium screenings and semi‐structured in‐person interviews at an academic ED that previously implemented the Delirium Triage Screen (DTS) and Brief Confusion Assessment Method (bCAM) tool. Content analysis of field notes from observations and interview transcripts was guided by the Exploration, Preparation, Implementation, and Sustainment implementation framework. Results From a total of 40,818 ED visits, 6196 (15.2%) had incomplete screens (i.e., without fidelity). We observed 62 encounters, of which 25 (40.3%) screenings were implemented without fidelity due to (1) not asking the required screening questions, (2) assuming patient responses, or (3) not completing the screen due to uncertainty regarding a patient's baseline mentation. We conducted 32 interviews. Identified barriers included staff prioritization of other tasks (e.g., stroke code, cleaning patients), limited knowledge of the screen's importance and utility, and language discordance. Facilitators included care partners providing baseline mental status information and nurses prefacing the screening to increase patient engagement. Conclusion The fidelity of ED delirium screening is influenced by an interplay of environmental, patient, provider, and caregiver factors. Fidelity may be improved by focusing education on the importance of screening, training screeners on how to determine whether a patient's mentation is acutely changed, providing clear action steps when a patient screens positive, and addressing how to administer the delirium screen to patients who do not speak English fluently.

Objectives: Electronic health records (EHR)-based triggers (eTriggers) have been used to study diagnostic errors in the emergency department (ED), often with suboptimal performance. Our objective was to investigate incremental value of multi-factor machine learning (ML) approaches to improve eTrigger performance. Methods: Patients presenting to an academic ED were categorized into trigger-positive and trigger-negative using standard trigger (T) definitions: (T1) ED return visits resulting in admission within 10 days; (T2) care escalation from the inpatient unit to the ICU within 24 hours; and (T3) deaths within 24 hours of admission. We trained and evaluated 6 supervised ML models. Results: A total of 124,053 consecutive encounters (5791 T-positive and 118,262 T-negative) were included. Among the T-positive, 4159 (72%) were associated with T1, 1415 (24%) with T2, and 217 (4%) with T3. The T-based positive predictive values (PPV) were 5.2% for T1, 8.2% for T2, and 6.5% for T3. ML models trained and evaluated on balanced training dataset and imbalanced test set had low classification performances (accuracy: 0.72–0.95; PPV: 0.00–0.16; F1-score: 0.00–0.23). Higher performances were observed in balanced test sets (accuracy: 0.80–0.97; PPV: 0.82–1.00; F1-score: 0.79–0.97). Comparing models trained on clinically annotated data with models trained on T-based labels identified other important factors. Conclusions: Utilizing machine learning to refine e-triggers slightly improves the identification of diagnostic errors, as evidenced by an increase in PPV values. We identified new potential factors contributing to ED diagnostic errors. These findings open new avenues to construct or modify more accurate e-triggers for diagnostic error identification.

Background and importance Emergency airway management in the emergency department (ED) is a high-risk procedure associated with patient outcomes. First-attempt success is a widely recognized quality metric, as multiple attempts are associated with an increased risk of peri-intubation complications. In Brazil, where emergency medicine is a recently established specialty, many ED are staffed by physicians without formal emergency medicine training. The association between emergency medicine training and intubation outcomes in this setting has not been well characterized. Objective To assess the association of physician specialty with first-attempt success and immediate peri-intubation complications in Brazilian EDs. Design Secondary analysis of a multicenter, prospective cohort study from the Brazilian Airway Registry Cooperation. Setting and participants This study included adult patients who underwent tracheal intubation in EDs between March 2022 and April 2024. Patients were excluded if the intubation occurred outside the ED, during cardiopulmonary resuscitation, or for elective procedures. Intubations performed by medical students were also excluded. Physicians were categorized by specialty as emergency medicine or nonemergency medicine. Outcome measures and analysis The primary outcome was first-attempt success; secondary outcomes included peri-intubation complications (severe hypoxemia, hemodynamic instability, and cardiac arrest). Multivariable logistic regression was used to assess the association between physician specialty and outcomes. Main results Among 2582 patients, 1087 (42.1%) were intubated by emergency physicians and 1495 (57.9%) by other physicians (mainly internal medicine and surgery). Intubations by emergency physicians were associated with a higher rate of first-attempt success [80.4 vs. 70.9%, adjusted odds ratio [aOR]: 1.63, 95% confidence interval (CI): 1.34–1.97]. There was also a higher odds of intubations without major complications (aOR: 1.20, 95% CI: 1.01–1.42). Conclusion In this study, there was a higher rate of first-attempt success in intubations performed by board-certified emergency physicians compared with other physicians working in Brazilian EDs.

Background Delirium is a state of acute brain failure causing confusion, reduced focus, and changes in alertness. It is common in older patients, occurring in 6-38% of older emergency department (ED) patients. Evidence suggests that systematic screening improves detection rates and early detection, management and reporting are critical. However, examples of operationalizing this in clinical non-research care are scarce. Objective To describe and disseminate examples of best practices for delirium screening, management, and monitoring within the ED. Methods A group of emergency physicians interested in geriatric emergency medicine (GEM) and evidence-based medicine gathered monthly to discuss ways of implementing delirium care. Members were asked to submit any electronic health record (EHR)-based interventions for delirium in current use at their institutions. One group member categorized the EHR tools by their use for screening, prevention, management, and monitoring of delirium. As a group, categorizations were reviewed and agreed upon and similarities, differences and themes were identified. Results Current EHR interventions from 5 EDs were included. We found that: 1) Screening for delirium in EDs is challenging due to the nature of the environment and the variety of tools available. 2) Prevention tools included order sets. 3) Management was accomplished with best practice alerts and order sets to help screen, prevent, address underlying causes, and manage agitation. Delirium prevention order sets emphasize optimizing patient mobility, comfort, and communication. Assessment order sets include routine labs and consultations, while management order sets for agitation provide safe medication options considering older adults’ unique physiology. 4) Utilizing dashboards for compliance monitoring offers real-time feedback, and provides an overview of adherence to clinical guidelines, regulatory standards, and patient safety protocols. Conclusion These real-world examples can be used in EDs implementing delirium programs. Further studies on how EHR interventions impact screening, diagnosis, and treatment of delirium in the ED are needed.