Introduction:

Atrial fibrillation is the most common recurrent arrhythmia in clinical practice. Its prevalence is multiplying in the current population and has different pathophysiological causes that make it a global pandemic.

Objectives:

To design a predictive model for failure of electrical therapy in patients with paroxysmal atrial fibrillation.

Methods:

A case-control study was carried out with 33 cases, and 66 controls. Predictor variables: age, ejection fraction ≤ 40%, left atrial volume ≥ 34 mL/m2. From logistic regression, a model was obtained in which the positive predictive value, negative predictive value, sensitivity and specificity were included.

Results:

The predictive risk factors were: age ≥ 55 years (p= 0.013; odds ratio (OR)= 3.58; 95% confidence interval -CI-: 1.33-9.67); left ventricular ejection fraction (LVEF) ≤ 40% was observed in 20 patients (22.7%) (p= 0.004; OR= 4.45; 95% CI: 1.54-12.8); elevated left atrial pressure, elevated left atrial volume (p= 0.004; OR= 3.11; 95% CI: 1.24-8.77), according to the logistic regression model. Internal validation was carried out by data division; It was confirmed that the model predicts very well those who will be successful in the therapeutic result.

Conclusions:

The predictive model developed is composed of the predictors age > 55 years, LVEF; left atrial volume; It presents a good fit and discriminating power, especially positive predictive value.

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