Academic Emergency Medicine最新文献

Objectives: The aim of this study was to describe the incidence of brief resolved unexplained events (BRUEs) and compare the impact of a national clinical practice guideline (CPG) on admission and diagnostic testing practices between general and pediatric emergency departments (EDs).

Methods: Using the Nationwide Emergency Department Sample for 2012-2019, we conducted a cross-sectional study of children <1 year of age with an International Classification of Diseases diagnostic code for BRUE. Population incidence rate was estimated using Centers for Disease Control and Prevention birth data. ED incidence rate was estimated for all ED encounters. We used interrupted time series to evaluate the associated impact of the CPG publication on the outcomes of ED disposition (discharge, admission, and transfer) and electrocardiogram (ECG) use.

Results: Of 133,972 encounters for BRUE, 80.0% occurred in general EDs. BRUE population incidence was 4.28 per 1000 live births and the annual incidence remained stable (p = 0.19). BRUE ED incidence was 5.06 per 1000 infant ED encounters (p = 0.14). The impact of the BRUE CPG on admission rates was limited to pediatric EDs (level shift -23.3%, p = 0.002). Transfers from general EDs did not change with the CPG (level shift 2.2%, p = 0.17). After the CPG was published, ECGs increased by 13.7% in pediatric EDs (p = 0.005) but did not change in general EDs (level shift -0.2%, p = 0.82).

Conclusions: BRUEs remain a common pediatric problem at a population level and in EDs. Although a disproportionate number of infants present to general EDs, there is differential uptake of the CPG recommendations between pediatric and general EDs. These findings may support quality improvement opportunities aimed at improving care for these infants and decreasing unnecessary hospital admissions or transfers.

Objectives: Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting.

Methods: We systematically searched publications related to EM research and NLP across databases including MEDLINE, Embase, Scopus, CENTRAL, and ProQuest Dissertations & Theses Global. Independent reviewers screened, reviewed, and evaluated article quality and bias. NLP usage was categorized into syndromic surveillance, radiologic interpretation, and identification of specific diseases/events/syndromes, with respective sensitivity analysis reported. Performance metrics for NLP usage were calculated and the overall area under the summary of receiver operating characteristic curve (SROC) was determined.

Results: A total of 27 studies underwent meta-analysis. Findings indicated an overall mean sensitivity (recall) of 82%-87%, specificity of 95%, with the area under the SROC at 0.96 (95% CI 0.94-0.98). Optimal performance using NLP was observed in radiologic interpretation, demonstrating an overall mean sensitivity of 93% and specificity of 96%.

Conclusions: Our analysis revealed a generally favorable performance accuracy in using NLP within EM research, particularly in the realm of radiologic interpretation. Consequently, we advocate for the adoption of NLP-based research to augment EM health care management.

Background: Antimicrobial resistance among Enterobacterales continues to be a growing problem, particularly in those with urinary infections. Previous studies have demonstrated safety and efficacy with the use of single-dose aminoglycosides in uncomplicated cystitis. However, data in complicated infections are limited. Single-dose aminoglycosides may provide a convenient alternative for those with or at risk for resistant pathogens causing complicated urinary infections, especially when oral options are unavailable due to resistance, allergy, intolerance, or interactions with other medications. This study evaluated the safety and effectiveness of single-dose aminoglycosides in treatment of complicated cystitis in the emergency department (ED).

Methods: This was a multicenter, prospective study performed between July 2022 and March 2023 of patients who met criteria for complicated cystitis and were otherwise stable for discharge at an academic ED. Primary outcomes were clinical or microbiologic failure within 14 days of treatment. Safety was assessed by review of adverse events. Descriptive statistics were used.

Results: Thirteen patients were included. Complicating factors were male sex (n = 4), kidney stone (n = 2), urinary catheter (n = 6), recent urologic procedure (n = 1), urinary hardware (n = 1), antibiotic allergy precluding use of alternate oral options (n = 4), immunocompromised status (n = 2), and <1-year history of multidrug-resistant organisms on urine culture (n = 8). Eleven patients (85%) had positive urine cultures in the preceding 12 months with no oral antimicrobial option. Eight patients (62%) received amikacin (median dose 15 mg/kg), four patients (31%) received gentamicin (median dose 5 mg/kg), and one patient (8%) received tobramycin (5 mg/kg) for treatment. Ten patients (77%) reported resolved urinary symptoms after treatment and 11 patients (85%) reported no new urinary symptoms since discharge. No patient required hospital admission for treatment failure, and no adverse events were noted.

Conclusions: Single-dose aminoglycosides appear to be a reasonably effective and safe treatment for complicated cystitis, which avoided hospital admission in this cohort.

Background: Cervical spine computed tomography (CSCT) scans are frequently performed in older emergency department (ED) trauma patients based on the 65-year-old high-risk criterion of the Canadian Cervical Spine Rule (CCR). We sought to determine the positivity rate of CSCT scans in symptomatic and asymptomatic patients to assess the current applicability of age in the CCR.

Methods: We reviewed CSCT ED reports from two institutional hospitals from 2018 to 2023. The primary variable was age; however, we also recorded fracture types and sites and type of treatments. Patients were separated into symptomatic and asymptomatic cohorts. We used a Fisher's exact test to compare variables between the asymptomatic and symptomatic groups and chi-square tests for comparison between age groups.

Results: Of 9455 CSCTs performed in patients ≥ 65 years, 192 (2.0%) fractures were identified (113 females); 28 (0.30%) were in asymptomatic patients. The rates of fractures (1.6%) and asymptomatic fractures (0.18%) were lowest in the 65- to 70-year age group. There were no distinguishing features as to the level or part of the vertebra fractured or surgical treatment rate between asymptomatic and symptomatic patients.

Conclusions: Cervical spine fractures in posttrauma patients ≥ 65 years are uncommon, with the lowest incidence in those 65 to 70 years old. Excluding asymptomatic individuals aged 65-70 from routine CSCT presents a minimal risk of missed fractures (0.18%). This prompts consideration for refining age-based screening and integrating shared decision making into the clinical protocol for this demographic, reflecting the low incidence of fractures and the changing health profile of the aging population.

Background: Precision health is a burgeoning scientific discipline that aims to incorporate individual variability in biological, behavioral, and social factors to develop personalized health solutions. To date, emergency medicine has not deeply engaged in the precision health movement. However, rapid advances in health technology, data science, and medical informatics offer new opportunities for emergency medicine to realize the promises of precision health.

Methods: In this article, we conceptualize precision emergency medicine as an emerging paradigm and identify key drivers of its implementation into current and future clinical practice. We acknowledge important obstacles to the specialty-wide adoption of precision emergency medicine and offer solutions that conceive a successful path forward.

Results: Precision emergency medicine is defined as the use of information and technology to deliver acute care effectively, efficiently, and authentically to individual patients and their communities. Key drivers and opportunities include leveraging human data, capitalizing on technology and digital tools, providing deliberate access to care, advancing population health, and reimagining provider education and roles. Overcoming challenges in equity, privacy, and cost is essential for success. We close with a call to action to proactively incorporate precision health into the clinical practice of emergency medicine, the training of future emergency physicians, and the research agenda of the specialty.

Conclusions: Precision emergency medicine leverages new technology and data-driven artificial intelligence to advance diagnostic testing, individualize patient care plans and therapeutics, and strategically refine the convergence of the health system and the community.