Publications

STUDY OBJECTIVES: To compare end-of-life predictions as measured by the physician-answered surprise question (SQ), "Would you be surprised if this patient died in the next 6 months?"), the Geriatric End-of-Life Screening Tool (GEST) artificial intelligence (AI) model, and a new collaborative GEST+SQ model for predicting 6-month mortality in older emergency department (ED) patients.

METHODS: This was a single-site prospective cohort study (Nov 2022 to June 2023) at a tertiary academic ED of patients aged 65 years and older. Answers to the SQ were collected within the electronic health record at ED disposition and GEST scores were calculated from available records using laboratory, vital signs, demographic and historical data. Six-month mortality was adjudicated via electronic health record and state records. SQ and GEST were compared using sensitivity and specificity. A new logistic regression model was developed combining SQ and GEST (GEST+SQ) and compared with GEST alone, using area under receiver-operating characteristic curves (ROC-AUC) for discrimination and expected calibration error for calibration. We modeled a sequential screening pathway where low- and high-risk patients received only GEST screening, whereas intermediate-risk patients received both GEST and SQ, reporting the proportion of patients for whom adding the SQ to GEST would change a theoretical referral to intervention.

RESULTS: From 9,256 eligible patients, 3,479 had SQ responses (37.6%), with 13.3% 6-month mortality. When matching GEST sensitivity to SQ (83.8%), GEST had greater specificity than the SQ (61.5% [56.7 to 67.1] vs. 50.8% [49.1 to 52.6]). At matching specificity (50.8%), GEST sensitivity (90.0% [87.0 to 92.7]) exceeded the SQ (83.8% [80.3 to 87.0]). GEST had an receiver-operating characteristic - area under the curve (ROC-AUC) of 0.79 (0.77 to 0.81), whereas the GEST+SQ model had ROC-AUC of 0.80 (0.78 to 0.82). The GEST+SQ model had significantly improved expected calibration error of 0.01 (0.01 to 0.02) for GEST+SQ vs. 0.042 (0.03 to 0.05) for GEST alone. In a sequential screening pathway, as few as 5% of patients required SQ screening following GEST risk scoring.

CONCLUSION: GEST modestly outperformed the SQ for predicting 6-month mortality. A GEST+SQ collaborative model did not improve discrimination (ROC-AUC) over GEST alone, but improved calibration. Sequential screening using GEST and then the SQ for intermediate-risk patients could decrease physician screening burden by 95% relative to manual, SQ-only screening. Collaborative approaches integrating automated tools with targeted physician input may enhance ED mortality risk assessment while reducing clinician effort.

INTRODUCTION: The anterior medial temporal lobe (MTL), including the entorhinal cortex (ERC) and Brodmann area 35 (BA35), is among the earliest cortical sites of tau pathology in Alzheimer's disease (AD), yet conventional image segmentation methods poorly capture these regions.

METHODS: We applied an automated segmentation approach using an extended Automatic Segmentation of Hippocampal Subfields (ASHS) atlas, including anterior MTL subregions, in 448 Pennsylvania Alzheimer's Disease Research Center participants with magnetic resonance imaging, tau positron emission tomography (PET) (n = 199), and/or plasma phosphorylated tau 217 (p-tau217) (n = 377). Amyloid beta (Aβ) positivity was defined using PET or plasma.

RESULTS: Tau-PET showed an anterior-posterior gradient, with highest uptake in BA35, ERC, and anterior hippocampus. Increased MTL tau-PET uptake and plasma p-tau217 were associated with cortical thinning localized to BA35 and ERC, even in cognitively unimpaired Aβ-positive individuals.

CONCLUSIONS: Anterior MTL subregions, especially BA35, show early vulnerability to tau-related neurodegeneration. Extended anterior MTL parcellation improves localization of early tau-associated structural changes and may facilitate biological staging in preclinical AD.

Task-irrelevant features can impact formation of auditory objects and influence the effectiveness of selective attention, including the buildup of attention over time. Using a previously established paradigm exploring the effects of random interruptions on spatial selective attention, this study explores how the task-irrelevant feature of talker identity impacts the buildup of spatial attention and whether it alters the impact of interruptions. Participants performed a sequence recall task in which participants were presented with two competing syllable sequences coming from different spatial directions and were asked to report the syllable sequence coming from the target direction. On half of the trials, an unpredictable, novel interrupting sound occurred, disrupting attentional focus. Two experiments explored how talker identity influenced performance, specifically, whether 1) making the two streams come from different talkers facilitates task performance and reduces the impact of interruption compared to when the streams are spoken by the same talker, and 2) talker discontinuity interferes with attention buildup and harms syllable recall performance compared to when the talker is the same from one syllable to the next. Our results showed that distinct talker features, though task-irrelevant in this spatial task, significantly improved syllable recall performance and reduced the impact of interrupters. Further, irrelevant talker discontinuities damaged attention buildup and reduced syllable recall performance. Post hoc analysis also revealed that repeating syllables in sequence substantially improved recall performance, which should be accounted for in future studies using similar paradigms.

BACKGROUND: Ultrasound-guided nerve blocks (UGNBs) are increasingly incorporated into multi-modal analgesia in the Emergency Department (ED). Despite their growing adoption, there is no consensus defining when an Emergency Medicine (EM) clinician is competent to perform UGNBs. Training methods, assessment approaches, and credentialing standards remain highly variable across institutions. The objective of this study was to define competency in UGNBs for EM physicians through a modified Delphi method that included national experts in EM and Anesthesia.

METHODS: A comprehensive librarian-assisted literature review informed the development of a 123-item questionnaire covering four domains: defining competency, teaching methods, assessment methods, and ongoing professional practice evaluation. Twenty-seven experts (23 EM, 4 anesthesiology) representing 24 institutions participated in two rounds of electronic voting and discussion. Consensus was defined a priori as 80% agreement.

RESULTS: All 27 panelists (100%) completed both rounds. Of 123 items, 61 achieved consensus: 33 items related to defining competency, 14 to teaching methods, 8 to assessment methods, and 6 to ongoing professional practice evaluation related to UGNBs. There was significant debate regarding the minimum number of UGNBs to determine competency and whether UGNBs should be included as a core ultrasound privilege.

CONCLUSION: This multidisciplinary modified Delphi provides the first national consensus defining competency in UGNBs for both practicing and EM physicians in training. The 61 consensus items offer a structured framework for residency curricula, faculty development, clinical privileging, and quality assurance. These recommendations may help guide forthcoming ACGME requirements and support safe, effective integration of UGNBs into emergency medicine training.

Lower extremity (LE) reconstruction presents unique challenges due to complex wound environments, compromised vascularity, and high functional demands. Compared with other anatomic regions, free flap reconstruction in the LE demonstrates higher complication and failure rates. Early microsurgical intervention improves outcomes, though negative pressure wound therapy can bridge to delayed coverage. In oncologic patients, radiation and chemotherapy impair tissue quality and recipient vessels, necessitating meticulous planning and vessel selection outside the zone of injury. Anatomic location influences reconstructive options, ranging from local flaps to free tissue transfer. Risk stratification models, such as the A-Sarc score, support evidence-based reconstructive decision-making.

INTRODUCTION: In Alzheimer's disease (AD), tau-neurodegeneration (T-N) mismatch has been proposed to reflect non-AD processes such as transactive response DNA binding protein 43 kDa and vascular disease. We aimed to characterize the spatiotemporal trajectories of T-N mismatch that may reflect non-AD progression.

METHODS: We performed T-N regression on 710 Alzheimer's Disease Neuroimaging Initiative participants using cortical thickness and 18F-flortaucipir uptake across 20 cortical regions. SuStaIn, a data-driven phenotype discovery and staging algorithm, was applied to standardized T-N residuals in canonical (N∼T) and vulnerable (N > T) cases.

RESULTS: SuStaIn identified three vulnerable subtypes with distinct N > T progression patterns. The posterior and anterior subtypes displayed different, but progressively diffuse mismatch patterns, while the limbic subtype exhibited temporal-limbic progression. Subtypes and SuStaIn stages were associated with distinct clinical features. Their longitudinal trajectories aligned with SuStaIn inferred progression.

DISCUSSION: Findings support that T-N mismatch progression captures specific co-pathological processes.

AIMS: The integration of point-of-care ultrasound (POCUS) by non-specialists and the shortage of trained sonographers highlights the need for scalable training approaches. This study aimed to evaluate the learning curve of novice operators performing artificial intelligence (AI)-guided limited transthoracic echocardiography (TTE) and to assess whether acquired images were sufficient for diagnostic interpretation of structural cardiac disease.

METHODS AND RESULTS: In this multicentre, prospective secondary analysis, nine novice operators performed limited TTE scans on 159 patients using a handheld device with AI-based acquisition guidance. Following eight hours of standardized training, novices independently obtained six standard TTE views. Three blinded expert reviewers graded image quality on a 1-5 scale and assessed diagnostic adequacy. Image scores were used to generate learning curves, and subgroup analyses examined the influence of patient characteristics. Of 954 novice-acquired images, 97.7% met the diagnostic threshold (score ≥3). After training, all operators achieved mean scores ≥3 across patients. AI-guidance consistently enabled high-quality imaging across all views, with minimal impact from sex, age, or pathology. Body mass index (BMI) showed a significant effect (P = 0.0029), though all subgroups exceeded diagnostic thresholds: 4.44 ± 0.17 (BMI <18), 4.40 ± 0.04 (18-24), 4.12 ± 0.12 (25-29), and 4.07 ± 0.07 (>30). Experts reliably ruled out left ventricular dysfunction (99.4%) and hypertrophy (98.7%); agreement was lower for wall motion abnormalities (80.7%) and atrial dilation (86.6%).

CONCLUSION: Novices with no prior POCUS experience achieved diagnostic-quality TTE images after one day of AI-guided training. AI may supplement conventional echocardiography training, and future research should evaluate its integration into routine clinical workflows.

Organophosphate flame retardants (OPFRs) are ubiquitous flame-retardant additives with endocrine-disrupting properties. Despite increasing evidence that OPFRs impact neurodevelopment, their effects on the neuroendocrine stress response remain poorly understood. To examine their long-term impact on stress regulation, we treated pregnant C57Bl/6J dams to a mixture of tris(1,3-dichloro-2-propyl) phosphate (TDCPP), triphenyl phosphate (TPP), and tricresyl phosphate (TCP; 1 mg/kg each) from gestational day (GD) 7 through postnatal day (PND) 14. Adult offspring (8-9 weeks of age) were then challenged with acute stressors, including 1 h restraint or a 6-day acute variable stress (AVS) paradigm. Perinatal OPFR exposure produced persistent, sex-specific alterations in the hypothalamic-pituitary-adrenal (HPA) axis and stress-related neurocircuitry. Following 1 h restraint, OPFR-treated females showed heightened serum corticosterone. In addition, gene expression analysis revealed sex-dependent disruptions in key stress-regulatory pathways after OPFR treatment and 1 h restraint in the hypothalamus (Crhr1, Crhr2, Ptpn5) and pituitary (Crhr1, Pomc, Nr3c1). Females demonstrated more differences in adrenal gene expression related to steroidogenesis (Mc2r, Cyp11b2) and catecholamine biosynthesis (Dbh, Pnmt), with OPFR-treated groups having blunted responses. OPFR AVS females displayed reduced corticosterone and Crh mRNA in the hypothalamus, and downregulated Pacap/Pac1r expression in the bed nucleus of the stria terminalis (BNST), accompanied by increased behavioral avoidance and immobility. In males, OPFR exposure led to increased BNST Pacap and Pac1r, expression, along with hyperactivity and avoidance behaviors. Together, these findings demonstrate that early-life OPFR exposure induces lasting, sex-specific dysregulation of the HPA axis and associated stress circuits, highlighting OPFRs as developmental neuroendocrine disruptors with implications for mood and stress-related disorders.

BACKGROUND: Clinical Informatics is wide-ranging field that engages with nearly every aspect of clinical care that is documented in the electronic health record (EHR). While studies from the informatics literature had been gradually introducing more sophisticated machine learning and artificial intelligence (AI) techniques into clinical settings, the explosive growth of Large Language Models (LLMs) has enticed both entrepreneurs and clinicians to rapidly introduce LLMs into the Emergency Department.

DISCUSSION: Clinical Informaticists possess a deep understanding of both the clinical significance and underlying architecture of clinical data. Misunderstanding how data is represented can pose significant hazards for clinical care, research, and AI systems. Despite the seemingly high performance of LLMs on some clinical measures, evidence for their ability to reason clinically is lacking, and they often provide confident, false answers. Emergency Physicians (EPs) who are board-certified in Clinical Informatics could be a natural constituency to help to integrate these technologies safely into the ED. However, there are very few EPs with this board-certification, due to high demand, few training programs, and a lack of visibility of the subspecialty.

CONCLUSIONS: LLMs and other AI systems are likely to play a growing role within the ED as technology improves and hospitals partner with commercial vendors. Working EPs need to have a strong understanding of the potential benefits and limitations of these technologies, and EPs with training in Informatics will play an essential role. Increasing exposure to Clinical Informatics within Emergency Medicine residencies and supporting EPs to go into Informatics fellowships is paramount.