Bellolio, Fernanda

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.

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.

Background/objectives: Falls are a leading cause of traumatic injury and hospitalization for adults over the age of 65. While common, bed-related falls are relatively understudied when compared to ambulatory falls. The aim of this study is to characterize the risk factors for the hospitalization of older adults presenting to U.S. emergency departments (EDs) after a fall from bed. Methods: This was a cross-sectional study using publicly available data from the U.S. Consumer Product Safety Commission’s National Electronic Injury Surveillance System (NEISS) from 2014 to 2023, including all adults over the age of 65 presenting to the NEISS’s participating EDs with bed-related fall injuries. We identified fall injuries using a keyword search of the NEISS narratives and determined how the fall occurred by manually reviewing a randomized 3% sample of the narratives. We summarized demographics and injury patterns with descriptive statistics. We constructed a multivariable logistic regression model to identify risk factors for hospitalization and used Poisson regression to assess temporal trends in fall incidence and hospital admissions. Results: An estimated average of 320,751 bed-related fall injuries presented to EDs annually from 2014 to 2023. ED visits increased by 2.85% per year, while hospital admissions rose by 5.67% per year (p < 0.001). The most common injury patterns were superficial injuries (contusions, abrasions, lacerations, avulsions, and punctures) (28.6%), fractures (21.7%), and internal injuries (including concussions) (21.6%). Most of the falls occurred while transitioning into or out of bed (34.4%) or falling out of bed (56.8%). Hospitalization was required in 34.1% of cases and was associated with male sex, medication use at time of injury, and fracture injuries. Conclusions: Bed-related falls and associated hospitalizations are increasing among older adults. ED providers should understand risk factors for hospitalization in these common injuries such as male sex, medication use at time of injury, and high-risk injury patterns. Additionally, prevention efforts should focus on helping older adults remain safely in bed and then assisting with transitions into or out of bed.

Background and importance Rapid sequence intubation in emergency settings may involve opioid pretreatment to blunt hemodynamic responses during laryngoscopy and intubation; however, opioids may lead to postintubation hypotension, increasing morbidity and mortality. Global clinical practice varies significantly, reflecting uncertainty about opioids’ benefits and risks. Objective Our goal was to evaluate the association between opioid pretreatment and hemodynamic instability, focusing on postintubation hypotension in adult patients undergoing emergency intubation. Design, settings, and participants We performed a systematic review of randomized controlled trials and observational studies comparing opioid administration vs. no opioids before adult emergency intubations. Searches included Cochrane, Embase, Medline, Scopus, and Web of Science databases until November 2024. Elective intubations were excluded. Eight (6708 patients) studies met criteria. Outcomes measure and analysis The primary outcome was postintubation hypotension, variably defined across studies. Secondary outcomes included hypoxemia, first-attempt success, cardiac arrest, and vasopressor use. Independent reviewers conducted data extraction, risk-of-bias assessments, and certainty evaluation. Results were qualitatively synthesized. Main results Among 6708 (2757 opioids and 3951 controls) patients, postintubation hypotension incidence varied widely (risk differences: −12.5% favoring opioids to +13.2% favoring controls). The single low-risk randomized study reported opioids increased hypotension (risk difference: 13%, odds ratio: 2.15, 95% confidence interval: 1.22–3.78). Observational studies yielded inconsistent results. Secondary outcomes showed no consistent differences. Evidence certainty was very low because of risk of bias, inconsistency, and imprecision. Conclusions Current evidence regarding opioid pretreatment effect on postintubation hypotension risk during emergency intubation is limited and conflicting.

Background Family participation in the delivery of nonpharmacological measures has shown in past studies to prevent 17-75% of incident delirium. A scalable and sustainable method to partner with family (i.e., care partners) and support their delivery of nonpharmacologic measures is needed. Objectives To test the feasibility, acceptability, and usability of iPREPARED in a pilot randomized controlled clinical trial (RCT). iPREPARED is a mobile health technology co-designed with patients and care partners containing resources on nonpharmacological interventions to prevent delirium. Methods Hospitalized adults ( > 65 years) with > 1 delirium risk factor and a care partner (family or friend > 18yo) willing to participate were enrolled. The primary outcomes were feasibility, acceptability, and usability. Descriptive statistics are reported. Results In total, 23 dyads completed the study (median age 72 years (IQR 64, 79), 60% male, and 100% Caucasian). Dyads reported that iPREPARED was acceptable, with moderate usability. Recruitment in the emergency department was feasible whereas recruitment once hospitalized and admitted was more difficult. Conclusion The study design was feasible. iPREPARED was acceptable and usable as an intervention.