Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation?

Abstract

ObjectiveElectrical cardioversion is frequently performed to restore sinus rhythm in patients with persistent atrial fibrillation (AF). However, AF recurs in many patients and identifying the patients who benefit from electrical cardioversion is difficult. The objective was to develop sex-specific prediction models for successful electrical cardioversion and assess the potential of machine learning methods in comparison with traditional logistic regression.MethodsIn a retrospective cohort study, we examined several candidate predictors, including comorbidities, biochemistry, echocardiographic data, and medication. The outcome was successful cardioversion, defined as normal sinus rhythm immediately after the electrical cardioversion and no documented recurrence of AF within 3 months after. We used random forest and logistic regression models for sex-specific prediction.ResultsThe cohort comprised 332 female and 790 male patients with persistent AF who underwent electrical cardioversion. Cardioversion was successful in 44.9% of the women and 49.9% of the men. The prediction errors of the models were high for both women (41.0% for machine learning and 48.8% for logistic regression) and men (46.0% for machine learning and 44.8% for logistic regression). Discrimination was modest for both machine learning (0.59 for women and 0.56 for men) and logistic regression models (0.60 for women and 0.59 for men), although the models were well calibrated.ConclusionsSex-specific machine learning and logistic regression models showed modest predictive performance for successful electrical cardioversion. Identifying patients who will benefit from cardioversion remains challenging in clinical practice. The high recurrence rate calls for thoroughly informed shared decision-making for electrical cardioversion.

Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation

“…42 Several factors, including comorbidities, echocardiogram information and medications, were included in the model (n=332 women and n=790 men); however, each analysis demonstrated only modest predictive values, with an AUC between 0.56 and 0.6 for both women and men. 42 A separate effort validated an ML model to predict cardioversion success from patients referred for electrical cardioversion (n=429), and compared the algorithm predictions with the CHA2DS2-VASc and HATCH scores, which have both been shown to be predictive of AF recurrence following cardioversion in a few studies. [43][44][45] The results from this study were mixed.…”

Erkennung, Vorhersage und Behandlung von Vorhofflimmern mithilfe künstlicher Intelligenz

“…Signal analysis using machine learning may help identify responders to treatment since the complexity of the signals may be better handled by a neural network than human explicit signal processing. So far, in two studies in patients, machine learning did not aid in identifying responders to direct current cardioversion [56] or pulmonary vein isolation [57], respectively; however, both studies had enrolled too few patients for algorithm development. The AI-PAFA Trial will prospectively randomize 340 AF patients to be evaluated for catheter ablation using an AI algorithm or conventional guideline-based rules [58].…”

Is machine learning the future for atrial fibrillation screening?

“…This makes ML invaluable for automated detection of pathology or conditions, for risk prediction of future conditions, and for prediction of those who would benefit from specific treatment strategies. Some examples of the uses of ML for AF include prediction of recurrence of AF following AF ablation, 21 improving cardiac activation mapping for AF, 22 prediction of successful electrical cardioversion of AF, 23 and AF screening. Thus far, supervised and unsupervised ML have been used for AF screening.…”