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  • Title: Prediction of maintenance of sinus rhythm after electrical cardioversion of atrial fibrillation by non-deterministic modelling.
    Author: Zohar P, Kovacic M, Brezocnik M, Podbregar M.
    Journal: Europace; 2005 Sep; 7(5):500-7. PubMed ID: 16087117.
    Abstract:
    AIMS: Atrial fibrillation (AF) is the most common rhythm disorder. Because of the high recurrence rate of AF after cardioversion and because of potential side effects of electrical cardioversion, it is clinically important to predict persistence of sinus rhythm after electrical cardioversion before it is attempted. The aim of our study was the development of a mathematical model by "genetic" programming (GP), a non-deterministic modelling technique, which would predict maintenance of sinus rhythm after electrical cardioversion of persistent AF. PATIENTS AND METHODS: Ninety-seven patients with persistent AF lasting more than 48h, undergoing the first attempt at transthoracic cardioversion were included in this prospective study. Persistence of AF before the cardioversion attempt, amiodarone treatment, left atrial dimension, mean, standard deviation and approximate entropy of ECG R-R intervals were collected. The data of 53 patients were randomly selected from the database and used for GP modelling; the other 44 data sets were used for model testing. RESULTS: In 23 patients sinus rhythm persisted at 3 months. In the other 21 patients sinus rhythm was not achieved or its duration was less than 3 months. The model developed by GP failed to predict maintenance of sinus rhythm at 3 months in one patient and in six patients falsely predicted maintenance of sinus rhythm. Positive and negative likelihood ratios of the model for testing data were 4.32 and 0.05, respectively. Using this model 15 of 21 (71.4%) cardioversions not resulting in sinus rhythm at 3 months would have been avoided, whereas 22 of 23 (95.6%) cardioversions resulting in sinus rhythm at 3 months would have been administered. CONCLUSION: This model developed by GP, including clinical data, ECG data from the time-domain and nonlinear dynamics can predict maintenance of sinus rhythm. Further research is needed to explore its utility in the present or an expanded form.
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