Key result
A genetic programming model using clinical and ECG data predicted maintenance of sinus rhythm at 3 months with positive and negative likelihood ratios of 4.32 and 0.05, respectively.
Why the study?
Can a mathematical model developed by genetic programming predict the maintenance of sinus rhythm at 3 months after electrical cardioversion in patients with persistent AF?
Observational (n=97)
Can a mathematical model developed by genetic programming predict the maintenance of sinus rhythm at 3 months after electrical cardioversion in patients with persistent AF?
Effect estimate: Positive LR 4.32; Negative LR 0.05
A non-deterministic mathematical model using genetic programming and clinical/ECG data can accurately predict the maintenance of sinus rhythm 3 months after electrical cardioversion for persistent AF.
Should not yet alter cardioversion decisions in persistent AF; leaves open the role of genetic programming models pending validation.
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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Žohar et al. (2005) conducted an observational in Atrial fibrillation (n=97). Genetic programming (GP) mathematical model was evaluated on Maintenance of sinus rhythm at 3 months (Positive LR 4.32; Negative LR 0.05). A genetic programming model using clinical and ECG data predicted maintenance of sinus rhythm at 3 months with positive and negative likelihood ratios of 4.32 and 0.05, respectively.
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