Annals of emergency medicine
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Multicenter Study
Analysis of current management of atrial fibrillation in the acute setting: GEFAUR-1 study.
Limited information relative to the management of atrial fibrillation in the emergency department (ED) daily practice is available. This study evaluates current management of atrial fibrillation in this setting to identify areas for practice improvement. ⋯ In our ED population, rate-control effectiveness is poor and rhythm control is not attempted in most recent-onset episodes. Methods to improve rate-control effectiveness, the selection of patients for rhythm control, and the use of electrocardioversion appear warranted.
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Multicenter Study Comparative Study
Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation.
Clinical and ECG data from presentation are highly discriminatory for diagnosis of acute coronary syndromes, whereas definitive diagnosis from serial ECG and cardiac marker protein measurements is usually not available for several hours. Artificial neural networks are computer programs adept at pattern recognition tasks and have been used to analyze data from chest pain patients with a view to developing diagnostic algorithms that might improve triage practices in the emergency department. The aim of this study is to develop and optimize artificial neural network models for diagnosis of acute coronary syndrome, to test these models on data collected prospectively from different centers, and to establish whether the performance of these models was superior to that of models derived using a standard statistical technique, logistic regression. ⋯ This study confirms that artificial neural networks can offer a useful approach for developing diagnostic algorithms for chest pain patients; however, the exceptional performance and simplicity of the logistic model militates in favor of logistic regression for the present task. Our artificial neural network models were well calibrated and performed well on unseen data from different centers. These issues have not been addressed in previous studies. However, and unlike in previous studies, we did not find the performance of artificial neural network models to be significantly different from that of suitably optimized logistic regression models.