European Journal of Emergency Medicine最新文献

Background and importance: Ensuring prompt ambulance responses is complicated and costly. It is a general conception that short response times save lives, but the actual knowledge is limited.

Objective: To examine the association between the response times of ambulances with lights and sirens and 30-day mortality.

Design: A registry-based cohort study using data collected from 2014-2018.

Settings and participants: This study included 182 895 individuals who, during 2014-2018, were dispatched 266 265 ambulances in the Capital Region of Denmark.

Outcome measures and analysis: The primary outcome was 30-day mortality. Subgroup analyses were performed on out-of-hospital cardiac arrests, ambulance response priority subtypes, and caller-reported symptoms of chest pain, dyspnoea, unconsciousness, and traffic accidents. The relation between variables and 30-day mortality was examined with logistic regression.

Results: Unadjusted, short response times were associated with higher 30-day mortality rates across unadjusted response time quartiles (0-6.39 min: 9%; 6.40-8.60 min: 7.5%, 8.61-11.80 min: 6.6%, >11.80 min: 5.5%). This inverse relationship was consistent across subgroups, including chest pain, dyspnoea, unconsciousness, and response priority subtypes. For traffic accidents, no significant results were found. In the case of out-of-hospital cardiac arrests, longer response times of up to 10 min correlated with increased 30-day mortality rates (0-6.39 min: 84.1%; 6.40-8.60 min: 86.7%, 8.61-11.8 min: 87.7%, >11.80 min: 85.5%). Multivariable-adjusted logistic regression analysis showed that age, sex, Charlson comorbidity score, and call-related symptoms were associated with 30-day mortality, but response time was not (OR: 1.00 (95% CI [0.99-1.00])).

Conclusion: Longer ambulance response times were not associated with increased mortality, except for out-of-hospital cardiac arrests.

Background and importance: Racial bias is found in both physicians and medical students. Immigrants in many parts of the world face challenges similar to racial minorities. Identification of immigrants might however be more subtle than identification by race, and currently, no data are available on a possible bias against the large minority group of migrants in Europe.

Design: Randomized control trial.

Settings and participants: Second-year medical students were randomized into four groups to watch a video of either a male or female patient with pain, with or without immigrant status.

Intervention: Students were asked whether they would administer pain medication (primary outcome).

Outcome measures and analysis: Immigrant status, patient's gender, student's gender, age, and language skills were covariates in a logistic regression model. Secondary outcomes included pain medication potency and the student's rating of the patient's pain intensity.

Main results: We recruited 607 students [337 females (56%), 387 (64%) between 18 and 22 years old]. Analgesia was administered in 95% (n = 576). Immigrant status was not associated with the probability of receiving pain medication [95 vs. 95%, odds ratio (OR) 0.81, 95% confidence interval (CI) 0.39-1.70, P  = 0.58]. Immigrants received high-potency analgesia less often (26 vs. 33%, OR 0.69, 95% CI 0.50-0.96, P  = 0.03). Female students administered pain medication more frequently (96 vs. 93%, OR 2.29, 95% CI 1.05-5.02, P  = 0.04), and rated the patients' pain higher (mean numeric rating scale 7.7, SD 0.9 vs. 7.4, SD 1.0, OR 1.36, 95% CI 1.16-1.60, P  < 0.001).

Conclusion: Medical students showed no immigration bias with regard to administering pain medication but were less likely to choose high-potency analgesia in immigrants. We also found a gender difference in pain management. These results demonstrate the importance of including knowledge about immigration bias in medical training.

Background and importance: Although shoulder dislocation diagnosis is often solely based on clinical examination, physicians may order a radiograph to rule out a concomitant shoulder fracture before performing reduction. The Fresno-Québec decision rule aims to identify patients requiring a radiograph before reduction to avoid unnecessary systematic imaging. However, this novel approach needs further validation.

Objective: To evaluate the performance of the Fresno-Québec rule in identifying patients who do not require a prereduction radiograph and assess the variables associated with a clinically significant fracture.

Design, settings, and participants: A multicenter, retrospective cohort study from 2015 to 2021. Data were extracted from three ED university-affiliated tertiary-care centers. Patients aged ≥18 years with a final diagnosis of anterior glenohumeral dislocation were included.

Outcomes measure and analysis: Accuracy metrics [sensitivity (Se), specificity (Sp), positive (PPV), negative predictive value (NPV), positive likelihood ratio (PLR) and negative likelihood ratio (NLR)] of the Fresno-Québec rule were measured. Multivariable logistic regression model was used to identify variables associated with the presence of a concomitant clinically significant fracture.

Main results: A total of 2129 patients were included, among whom 9.7% had a concomitant fracture. The performance metrics of the Fresno-Québec rule were as follows: Se 0.96 95% confidence interval (0.92-0.98), Sp 0.36 (0.34-0.38), PPV 0.14 (0.12-0.16), NPV 0.99 (0.98-0.99), PLR 1.49 (1.42-1.55) and NLR 0.12 (0.06-0.23). A total of 678 radiographs could have been avoided, corresponding to a reduction of 35.2%. Age ≥40 years, first dislocation episode [odds ratio (OR) = 3.18 (1.95-5.38); P  < 0.001], the following mechanisms: road collision [OR = 6.26 (2.65-16.1)], low-level fall [OR = 3.49 (1.66-8.28)], high-level fall [OR = 3.95 (1.62-10.4)], and seizure/electric shock [OR = 10.6 (4.09-29.2)] were associated with the presence of a concomitant fracture.

Conclusion: In this study, the Fresno-Québec rule has excellent Se in identifying concomitant clinically significant fractures in patients with an anterior glenohumeral dislocation. The use of this clinical decision rule may be associated with a reduction of approximately a third of unnecessary prereduction radiographs.

Background: The assessment of acute heart failure (AHF) prognosis is primordial in emergency setting. Although AHF management is exhaustively codified using mortality predictors, there is currently no recommended scoring system for assessing prognosis. The European Society of Cardiology (ESC) recommends a comprehensive assessment of global AHF prognosis, considering in-hospital mortality, early rehospitalization rates and the length of hospital stay.

Objective: We aimed to prospectively evaluate the performance of the Multiple Estimation of risk based on the Emergency department Spanish Score In patients with AHF (MEESSI-AHF) score in estimating short prognosis according to the ESC guidelines.

Design, settings and patients: A multicenter study was conducted between November 2020, and June 2021. Adult patients who presented to eleven French hospitals for AHF were prospectively included.

Outcome measures and analysis: According to MEESSI-AHF score, patients were stratified in four categories corresponding to mortality risk: low-, intermediate-, high- and very high-risk groups. The primary outcome was the number of days alive and out of the hospital during the 30-day period following admission to the Emergency Department (ED).

Results: In total, 390 patients were included. The number of days alive and out of the hospital decreased significatively with increasing MEESSI-AHF risk groups, ranging from 21.2 days (20.3-22.3 days) for the low-risk, 20 days (19.3-20.5 days) for intermediate risk,18.6 days (17.6-19.6 days) for the high-risk and 17.9 days (16.9-18.9 days) very high-risk category.

Conclusion: Among patients admitted to ED for an episode of AHF, the MEESSI-AHF score estimates with good performance the number of days alive and out of the hospital.

Aims: Patient admission is a decision relying on sparsely available data. This study aims to provide prediction models for discharge versus admission for ward observation or intensive care, and 30 day-mortality for patients triaged with the Manchester Triage System.

Methods: This is a single-centre, observational, retrospective cohort study from data within ten minutes of patient presentation at the interdisciplinary emergency department of the Kepler University Hospital, Linz, Austria. We trained machine learning models including Random Forests and Neural Networks individually to predict discharge versus ward observation or intensive care admission, and 30 day-mortality. For analysis of the features' relevance, we used permutation feature importance.

Results: A total of 58323 adult patients between 1 December 2015 and 31 August 2020 were included. Neural Networks and Random Forests predicted admission to ward observation with an AUC-ROC of 0.842 ± 0.00 with the most important features being age and chief complaint. For admission to intensive care, the models had an AUC-ROC of 0.819 ± 0.002 with the most important features being the Manchester Triage category and heart rate, and for the outcome 30 day-mortality an AUC-ROC of 0.925 ± 0.001. The most important features for the prediction of 30 day-mortality were age and general ward admission.

Conclusion: Machine learning can provide prediction on discharge versus admission to general wards and intensive care and inform about risk on 30 day-mortality for patients in the emergency department.