Assessment of real-time electrocardiogram effects on interpretation quality by emergency physicians.

BACKGROUND: Electrocardiogram (ECG) is one of the most commonly performed examinations in emergency medicine. The literature suggests that one-third of ECG interpretations contain errors and can lead to clinical adverse outcomes. The purpose of this study was to assess the quality of real-time ECG interpretation by senior emergency physicians compared to cardiologists and an ECG expert. METHODS: This was a prospective study in two university emergency departments and one emergency medical service. All ECGs were performed and interpreted over five weeks by a senior emergency physician (EP) and then by a cardiologist using the same questionnaire. In case of mismatch between EP and the cardiologist our expert had the final word. The ratio of agreement between both interpretations and the kappa (k) coefficient characterizing the identification of major abnormalities defined the reading ability of the emergency physicians. RESULTS: A total of 905 ECGs were analyzed, of which 705 (78%) resulted in a similar interpretation between emergency physicians and cardiologists/expert. However, the interpretations of emergency physicians and cardiologists for the identification of major abnormalities coincided in only 66% (k: 0.59 (95% confidence interval (CI): 0.54-0.65); P-value = 1.64e-92). ECGs were correctly classified by emergency physicians according to their emergency level in 82% of cases (k: 0.73 (95% CI: 0.70-0.77); P-value ≈ 0). Emergency physicians correctly recognized normal ECGs (sensitivity = 0.91). CONCLUSION: Our study suggested gaps in the identification of major abnormalities among emergency physicians. The initial and ongoing training of emergency physicians in ECG reading deserves to be improved.

Assessment of Predictor Factors Associated with Multiple Emergency Department Attendance with Asthma Attack: A Qualitative and Multicentric Prospective Observational Study.

Purpose: Identified factors associated with multiple emergency department (ED) visits (≥) for asthma, which is associated with death. Patients and Methods: We first conducted a qualitative study. We invited French-speaking adults (≥18 years old) with a diagnosis of asthma for more than 6 months. The identified concepts were transcribed into items. A Delphi method allowed for selecting items for a self-reported questionnaire. In an observational multicentric cross-sectional quantitative study, the resulting 20-item questionnaire and 12-item General Health Questionnaire, exploring psychological distress, were administered to adults visiting an ED for asthma exacerbation. Multivariable logistic regression was used to assess factors associated with ED visits. Results: Data saturation was obtained after 8 patient interviews. Patients who came to the ED seemed unfamiliar with their illness or treatments but were concerned by the disease. The questionnaire was administered to 182 patients. On multivariable logistic regression, multiple and systematic ED visits were associated with asthma exacerbation (adjusted odds ratio (aOR) = 6.89, 95% confidence interval [CI]: 2.25-21.09), asthma perceived as a handicap (aOR=3.19, 95% CI: 1.55-6.57) and reported atopy (OR=2.09, 95% CI: 1.03-4.26). High educational level and lack of maintenance inhaled corticosteroids were protective for multiple ED visits. Conclusion: Inadequate medical care is frequent in patients attending the ED for an asthma exacerbation, associated with strong psychological impact. Questioning the reasons for consulting the ED may help quickly identify patients requiring asthma education and improve their referral.

Ensemble bias correction of climate simulations: preserving internal variability.

Climate simulations often need to be adjusted (i.e., corrected) before any climate change impacts studies. However usual bias correction approaches do not differentiate the bias from the different uncertainties of the climate simulations: scenario uncertainty, model uncertainty and internal variability. In particular, in the case of a multi-run ensemble of simulations (i.e., multiple runs of one model), correcting, as usual, each member separately, would mix up the model biases with its internal variability. In this study, two ensemble bias correction approaches preserving the internal variability of the initial ensemble are proposed. These "Ensemble bias correction" (EnsBC) approaches are assessed and compared to the approach where each ensemble member is corrected separately, using precipitation and temperature series at two locations in North America from a multi-member regional climate ensemble. The preservation of the internal variability is assessed in terms of monthly mean and hourly quantiles. Besides, the preservation of the internal variability in a changing climate is evaluated. Results show that, contrary to the usual approach, the proposed ensemble bias correction approaches adequately preserve the internal variability even in changing climate. Moreover, the climate change signal given by the original ensemble is also conserved by both approaches.