Faculty Bibliography

OBJECTIVES/OBJECTIVE:Multicenter research of geriatric emergency department (GED) care remains limited. Our objectives were to: 1. Prospectively collect data prioritized by the Geriatric Emergency care Applied Research (GEAR) network, a transdisciplinary taskforce for GED care, and create a multicenter GED research repository of prospective and electronic health record (EHR) data, 2. Assess concordance between prospective and EHR data. METHODS:The GEAR Standardization Study (GEARSS) is a multicenter, prospective study of older emergency department (ED) patients (65+) focusing on the 4Ms of age-friendly care (mobility, medication safety, mentation, what matters) and elder mistreatment. Demographic and clinical data were collected via interviews by trained research assistants (RA) on Days 0, 4, 30, and 90 and linked to EHR. Prevalence of chronic comorbidities and incident delirium were measured and reported using descriptive statistics. Prospective and EHR data concordance was assessed with Cohen's Kappa. RESULTS:999 participants were recruited from 5 EDs (3/25/2021-6/30/2022) across 3 institutions: Grady Health System, Northwestern Memorial Hospital, and Yale New Haven Health. The cohort was 57.0% female, 55.2% White, 39.1% Black, and 3.4% Hispanic, and the mean age was 75.1 years. For rheumatologic disease, peptic ulcer disease, diabetes, renal disease, and cancer, prevalence differed between prospective and EHR data by > 10%. About two-thirds of participants were at risk for falls. Concordance between prospective and EHR data was good for ethnicity (K = 0.73); excellent for sex (K = 1.00), age (K = 1.00), and race (K = 0.98); fair for disposition (K = 0.53); slight for ED observation status (K = 0.33) and dementia diagnosis (K = 0.24); poor for delirium presence (K = 0.07). CONCLUSION/CONCLUSIONS:In GEARSS, demographic variables aligned strongly between prospective and EHR data, while diagnosis, disposition, and mentation factors did not. This multicenter data source provides preliminary findings for common geriatric syndromes and conditions. Choice of measures using these data should be driven by GED research questions.

BACKGROUND:Care transitions from the emergency department (ED) to the community represent a critical period that can significantly impact clinical outcomes of older adults, yet there is a lack of standardized tools to measure patient-reported experiences and outcomes during this transition. Our objective was to develop and validate the Patient-Reported Outcome Measure-Older adult care Transitions in the ED (PROM-OTED) tool to measure care transition outcomes within 4-10 days after ED discharge. METHODS:Older adults (65+ years) discharged from four EDs were enrolled between November 2021 and April 2024 in a multiphase process: qualitative interviews, item generation, member checking, cognitive debriefing, technical expert panel review, and psychometric evaluation and validation. We employed descriptive statistics, item analysis, interitem correlation, and factor analyses to assess the tool's validity and reliability. RESULTS:Across all phases, we enrolled 290 older adults. The final 18-item PROM-OTED tool included items that addressed understanding of discharge instructions, medication management, follow-up care, and quality of life. The tool demonstrated feasibility with a mean (±SD) completion time of 4.97 (±3.04) min and was able to be administered electronically or via telephone. The tool additionally demonstrated excellent internal consistency (Cronbach's alpha 0.9376, McDonald's omega 0.9988) and good test-retest reliability (r = 0.8437). Exploratory factor analysis supported a robust factor structure and significant correlations between the PROM-OTED tool with the Care Transitions Measure-3, a general measure of hospital discharge quality of care, support its concurrent validity. CONCLUSIONS:The PROM-OTED tool is a reliable and preliminarily valid instrument for use during the immediate post-ED period, with potential clinical applications in enhancing discharge practices and assessing care transition outcomes of older adults during observational or interventional studies.

INTRODUCTION/UNASSIGNED:The rapidly ageing population and multimorbidity are associated with increased emergency department (ED) visits by older adults. In the ED, older adults have higher risk of hospitalization, functional and cognitive decline, and mortality. Boarding, holding admitted patients in the ED awaiting a hospital bed, exacerbates these negative outcomes, which disproportionately affect older adults. METHODS/UNASSIGNED:We conducted a cross-sectional analysis to investigate US boarding trends by age using clinical administrative data from 5 health systems and publicly available NHAMCS data from 2018 to 2024. RESULTS/UNASSIGNED:Boarding ≥3 h in the ED has increased across diverse hospital types, with oldest adults (85+) facing the greatest risk (System 4: IRR [95% CI] = 1.18 [1.15-1.20], System 5: IRR [95% CI] = 1.20 [1.17-1.23], System 3 [Community Hospital]: IRR [95% CI] = 1.25 [1.19-1.33]). These results were recapitulated at the national level in NHAMCS (IRR [95% CI] = 1.30 [1.05-1.61]). DISCUSSION/UNASSIGNED:The trend of increased boarding has serious implications for patients, caregivers, and health systems. The 2025 CMS Age-Friendly Hospital Measure offers opportunities to improve processes and procedures to mitigate the negative effects of hospital boarding on older patients. We highlight opportunities to address this challenge, including ongoing quality improvement initiatives, bed prioritization algorithms, and alternate admission pathways.

A core tenant of the Geriatric Emergency care Applied Research Network 2.0-Advancing Dementia Care (GEAR 2.0-ADC) is the inclusion of community members during all stages of clinical research. As such, we deliberately integrated and supported patient and public involvement in the evaluation and selection of GEAR 2.0-ADC Pilot Research Grants by developing and adapting traditional grant application review structures, with input from community members, to create the GEAR 2.0-ADC Community Review Committee approach. Community members, including persons living with dementia, effectively participated in all three rounds of research grant application review and selection, complementing the traditional scientific review process. The structure and flexibility of the GEAR 2.0-ADC Community Review Committee approach serve as a model for patient and public grant application review involvement with strong potential for applications across grant reviews in other medical specialties.

INTRODUCTION/BACKGROUND:The study aimed to develop and validate the Emergency Department Dementia Algorithm (EDDA) to detect dementia among older adults (65+) and support clinical decision-making in the emergency department (ED). METHODS:In a multisite retrospective study of 759,665 ED visits, electronic health record data from Yale New Haven Health (2014-2022) were used to train three supervised and semi-unsupervised positive-unlabeled machine learning models (XGBoost, Random Forest, LASSO). A separate test set of 400 ED encounters underwent adjudicated chart review for validation. RESULTS:EDDA achieved an area under the receiver-operating characteristic curve (AUROC) of 0.85 in the test set and 0.93 in the validation set. Positive-unlabeled learning improved performance. Agreement between EDDA and clinician-adjudicated dementia diagnoses was moderate (kappa = 0.50), with 17% of EDDA-positive patients having undiagnosed probable dementia. DISCUSSION/CONCLUSIONS:EDDA enhances dementia detection in the ED, with potential for real-time implementation to improve patient outcomes and care transitions. HIGHLIGHTS/CONCLUSIONS:Developed a machine learning algorithm using electronic health record data to detect dementia in the emergency department (ED). Algorithm designed to balance detection accuracy with ease of ED implementation. Parsimonious model with limited but predictive variables selected for rapid ED use. Focused on real-time application, optimizing ED workflows, and clinician support. Aims to enhance ED dementia detection, patient safety, and care coordination.

Alzheimer's disease is the neurodegenerative disorder responsible for approximately 60% to 70% of all cases of dementia and is expected to affect 152 million by 2050. Recently, anti-amyloid therapies have been developed and approved by the Food and Drug Administration as disease-modifying treatments given as infusions every 2 to 5 weeks for Alzheimer's disease. Although this is an important milestone in mitigating Alzheimer's disease progression, it is critical for emergency medicine clinicians to understand what anti-amyloid therapies are and how they work to recognize, treat, and mitigate their adverse effects. Anti-amyloid therapies may be underrecognized contributors to emergency department visits because they carry the risk of adverse effects, namely amyloid-related imaging abnormalities. Amyloid-related imaging abnormalities are observed as abnormalities on magnetic resonance imaging as computed tomography is not sensitive enough to detect the microvasculature abnormalities causing vasogenic edema (amyloid-related imaging abnormalities-E) microhemorrhages and hemosiderin deposits (amyloid-related imaging abnormalities-H). Patients presenting with amyloid-related imaging abnormalities may have nonspecific neurologic symptoms, including headache, lethargy, confusion, and seizures. Anti-amyloid therapies may increase risk of hemorrhagic conversion of ischemic stroke patients receiving thrombolytics and complicate the initiation of anticoagulation. Given the novelty of anti-amyloid therapies and limited real-world data pertaining to amyloid-related imaging abnormalities, it is important for emergency medicine clinicians to be aware of these agents.

INTRODUCTION/BACKGROUND:The Geriatric Emergency Care Applied Research Network 2.0-Advancing Dementia Care (GEAR 2.0-ADC) aims to advance research efforts to improve the emergency care of persons living with dementia (PLWDs). OBJECTIVE:To support this objective, GEAR 2.0-ADC convened a virtual consensus conference to prioritize emergency care research opportunities for PLWDs inclusive of perspectives of PLWDs to ensure identification of research gaps in response to their experiences and priorities. Inclusion of PLWDs as research partners is increasingly recognized as a best practice, however, approaches to facilitating consensus participation are lacking. METHODS:Best practices for supporting the engagement of PLWDs in a consensus conference, applied across its three phases (pre-conference, during the conference, and post-conference), include: establishing a learning environment focused on research priorities before the event, presenting information in ways that align with participants' learning preferences while accommodating cognitive impairments, and providing multiple opportunities and methods for gathering post-conference feedback from PLWDs. RESULTS:These strategies were identified by PLWDs and care partners (CPs) through semi-structured interviews, who were involved in the convening process, aimed at exploring ways to enhance facilitation techniques for participants. CONCLUSION/CONCLUSIONS:Additionally, these summarized insights aim to encourage the use of community-engaged approaches in discussions and consensus-building around research priorities in emergency care, particularly for PLWDs and their CPs.