Bellolio, M Fernanda

Introduction Community-acquired pneumonia (CAP) is common and remains the leading infectious cause of death worldwide. Antibiotics remain the mainstay of treatment, but organisms are often not identified, necessitating empirical treatment. Current treatment guidelines recommend combination therapy with beta-lactams and macrolides or monotherapy with fluoroquinolones for non-severe CAP. However, rising antibiotic resistance and safety concerns have led to increased doxycycline use, despite limited evidence supporting its efficacy. Our goal is to conduct a pragmatic, open-label, randomised clinical trial comparing the effectiveness of azithromycin with that of doxycycline in combination with beta-lactam therapy in adult patients hospitalised for CAP. Methods and analysis Patients presenting to the emergency department or directly referred to the hospital with a clinical presentation concerning for CAP and need for hospitalisation are eligible. We plan to enrol 1120 adults hospitalised at six hospitals across four states, including rural, suburban and urban settings and academic and community hospitals. Randomisation will be 1:1 using an electronic health record (EHR)-embedded screening and randomisation tool triggered by the treating clinician’s orders for azithromycin or doxycycline. Outcomes will be ascertained pragmatically via EHR-based data retrieval and will not require additional study visits. Our primary outcome is days alive and out of the hospital through day 28. Secondary outcomes include oxygen-free days; a composite of a need for advanced respiratory support or in-hospital mortality with analyses of each component separately; an ordinal measure of clinical deterioration defined by a prespecified severity scale; and 60-day and 180-day mortality. Safety outcomes are the incidence of Clostridioides difficile infection and QT prolongation. Analyses will follow an intention-to-treat framework, with per-protocol analyses for sensitivity. The primary endpoint will be compared between groups using a stratified van Elteren test, with supportive analyses using proportional odds regression and analysis of covariance adjusted for key covariates. Ethics and dissemination The Mayo Clinic Institutional Review Board reviewed and approved this protocol with a waiver of written consent, requiring verbal consent to be obtained by the clinician initiating the antibiotics (IRB# 24–0 06 503). Findings will be disseminated through peer-reviewed publications and international conference presentations. Trial registration number NCT07164131 .

ABSTRACT Background Older adults experience age‐related physiological changes that alter the pharmacokinetics and pharmacodynamics of certain medications, which may necessitate reduced dosing. Our objective was to assess emergency department (ED) and hospital outcomes of older patients administered opioids by emergency medical services (EMS) clinicians. Methods Retrospective cohort study of adults ≥ 65 years who received an opioid between 2019 and 2024 within a health system‐affiliated EMS agency. We excluded patients with an advanced airway and those with no documented weight. Maximum single and cumulative weight‐based opioid dosages were classified as low, therapeutic, or high. Outcomes were measured in the ED/hospital and compared using Poisson regression with Huber/White standard errors, adjusted for age, sex, vitals, Glascow Coma Scale, emergency severity index, and chief concern. Results were reported as adjusted risk ratios (aRRs). Results A total of 6406 patients, including 747 (11.7%, 95% CI: 10.9–12.5) with a high opioid dose, 3663 (57.2%, 95% CI: 56.0–58.4) with a therapeutic dose, and 1996 (31.2%, 95% CI: 30.0–32.3) with a low dose, were included. Receipt of a high maximum single opioid dose versus a low/therapeutic dose (excluding patients receiving a concomitant sedative) was associated with increased risk of altered responsiveness (aRR = 1.28, 95% CI: 1.03–1.58) and delirium (aRR = 2.43, 95% CI: 1.08–5.45) in the ED/hospital setting. When 311 (4.9%) patients who received a concomitant sedative were included, patients who received a high opioid dose similarly had an increased risk of altered responsiveness (aRR = 1.28, 95% CI: 1.06–1.56) and delirium (RR = 2.65, 95% CI: 1.25–5.59), compared to a low/therapeutic dose. There was no difference in outcomes of hypoxia, death within 30 days, or ED length of stay between dose groups. Conclusion Prehospital administration of high opioid doses to older patients was associated with increased rates of altered responsiveness and delirium. These findings highlight how out‐of‐hospital care can impact a patient's ED and hospital course.

ABSTRACT Background One in five emergency department (ED) visits is by older adults aged 65+ years. Clinical practice guidelines are needed to optimize patient care by translating the best available evidence into actionable recommendations to guide person‐centered management for this medically complex and growing patient population. Our objective was to understand contributors' experiences and share best practices to inform other guideline working groups. Methods We conducted a qualitative study based on thematic analysis with an interpretivist paradigm in June–July 2025, using semi‐structured interviews of Geriatric Emergency Department (GED) Guidelines 2.0 contributors to understand their experiences. Interviews were transcribed, independently reviewed by team members, and reflexively coded to identify themes. Results We interviewed 18 participants. Four main themes emerged: (1) motivations for participation, (2) varying opportunities to be involved, (3) personal benefits, and (4) challenges for improvement. Participants contrasted the strengths of shared learning and development with the burdens of organization and coordination. Conclusion Contributors to the GED Guidelines 2.0 reported a positive experience. They joined because they wanted to contribute to better care of older patients and to achieve personal and professional goals. Many cited that their roles aligned with their strengths and expertise. They also gained skills and knowledge on systematic reviews and benefited from networking. Future groups planning a similar process should consider having multiple levels of leadership, experts in systematic reviews, regular reminders, and creative incentives to improve the process and foster improved networking opportunities within their disciplines.

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 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.

Importance Diagnostic errors represent a major patient safety concern, with the potential to significantly impact patient outcomes. To address this, various trigger-based strategies have been developed to identify diagnostic errors, aiming to enhance clinical decision-making and improve patient safety. Objective To evaluate the performance of three pre-established triggers (T) in the emergency department (ED) setting and assess their effectiveness in detecting diagnostic errors. Design Consecutive cohort, retrospective observational design. Setting Academic ED with 80 000 annual visits. Participants Adults and children presenting to a single ED in the USA between 1 May 2018 and 1 January 2020. Intervention/outcomes Electronic health records (EHRs) were retrieved and categorised into trigger-positive and trigger-negative cases using the following criteria: T1—unscheduled returnvisits to the ED with admission within 7–10 days of theinitial visit; T2—care escalation from the inpatient unitto the intensive care unit (ICU) within 6, 12 or 24 hoursof ED admission; and T3—all deaths in the ED or within24 hours of ED admission, excluding palliative care. A random sample of trigger-positive cases was reviewed using the SaferDx tool to determine the presence or absence of a diagnostic error. Results A total of 5791 trigger-positive and 118262 trigger-negative cases were identified. Among trigger-positive cases, 4159 (72%) were associated with T1, 1415 (24%) with T2, and 217 (4%) with T3. A preliminary chart review of 462 trigger-positive and 251 trigger-negative cases showed most were error-negative (279 and 217, respectively). Detailed reviews found 32 diagnostic errors among 183 trigger-positive cases, yielding PPVs of 5.4% (T1), 8.9% (T2), and 6.9% (T3). No errors were found in 34 reviewed trigger-negative cases, resulting in a 100% NPV. Sepsis was the most common diagnosis among error-positive cases (n=11, 34.4%). Those with non-specific chief complaints like altered mental status or shortness of breath had higher diagnostic error risk. Conclusion and relevance While previously proposed EHR-based triggers can identify some diagnostic errors, they are insufficient for detecting all cases. To improve error detection performance, we recommend exploring data-driven strategies, such as machine learning techniques, to more effectively identify underlying contributing factors to diagnostic errors and enhance detection accuracy in the ED.