TY - JOUR
T1 - Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence following Pulmonary Vein Isolation
AU - Shade, Julie K.
AU - Ali, Rheeda L.
AU - Basile, Dante
AU - Popescu, Dan
AU - Akhtar, Tauseef
AU - Marine, Joseph E.
AU - Spragg, David D.
AU - Calkins, Hugh
AU - Trayanova, Natalia A.
N1 - Funding Information:
This work was supported by the National Institutes of Health grant (U01-HL141074) to Dr Trayanova, a grant from the Lowenstein Foundation to Dr Trayanova, a National Science Foundation Graduate Research Fellowship (DGE-1746891) to J.K. Shade, and a research fellowship from the Johns Hopkins University to Dr Ali.
Publisher Copyright:
© 2020 Lippincott Williams and Wilkins. All rights reserved.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - Background: Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a methodology that combines machine learning (ML) and personalized computational modeling to predict, before PVI, which patients are most likely to experience AF recurrence after PVI. Methods: This single-center retrospective proof-of-concept study included 32 patients with documented paroxysmal AF who underwent PVI and had preprocedural late gadolinium enhanced magnetic resonance imaging. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing. Features were derived from pre-PVI late gadolinium enhanced magnetic resonance images and from results of simulations of AF induction. The most predictive features were used as input to a quadratic discriminant analysis ML classifier, which was trained, optimized, and evaluated with 10-fold nested cross-validation to predict the probability of AF recurrence post-PVI. Results: In our cohort, the ML classifier predicted probability of AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation area under the curve of 0.82. Dissecting the relative contributions of simulations of AF induction and raw images to the predictive capability of the ML classifier, we found that when only features from simulations of AF induction were used to train the ML classifier, its performance remained similar (validation area under the curve, 0.81). However, when only features extracted from raw images were used for training, the validation area under the curve significantly decreased (0.47). Conclusions: ML and personalized computational modeling can be used together to accurately predict, using only pre-PVI late gadolinium enhanced magnetic resonance imaging scans as input, whether a patient is likely to experience AF recurrence following PVI, even when the patient cohort is small.
AB - Background: Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a methodology that combines machine learning (ML) and personalized computational modeling to predict, before PVI, which patients are most likely to experience AF recurrence after PVI. Methods: This single-center retrospective proof-of-concept study included 32 patients with documented paroxysmal AF who underwent PVI and had preprocedural late gadolinium enhanced magnetic resonance imaging. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing. Features were derived from pre-PVI late gadolinium enhanced magnetic resonance images and from results of simulations of AF induction. The most predictive features were used as input to a quadratic discriminant analysis ML classifier, which was trained, optimized, and evaluated with 10-fold nested cross-validation to predict the probability of AF recurrence post-PVI. Results: In our cohort, the ML classifier predicted probability of AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation area under the curve of 0.82. Dissecting the relative contributions of simulations of AF induction and raw images to the predictive capability of the ML classifier, we found that when only features from simulations of AF induction were used to train the ML classifier, its performance remained similar (validation area under the curve, 0.81). However, when only features extracted from raw images were used for training, the validation area under the curve significantly decreased (0.47). Conclusions: ML and personalized computational modeling can be used together to accurately predict, using only pre-PVI late gadolinium enhanced magnetic resonance imaging scans as input, whether a patient is likely to experience AF recurrence following PVI, even when the patient cohort is small.
KW - atrial fibrillation
KW - computational biology
KW - heart atria
KW - machine learning
KW - magnetic resonance imaging
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U2 - 10.1161/CIRCEP.119.008213
DO - 10.1161/CIRCEP.119.008213
M3 - Article
C2 - 32536204
AN - SCOPUS:85088607496
SN - 1941-3149
VL - 13
SP - 617
EP - 627
JO - Circulation: Arrhythmia and Electrophysiology
JF - Circulation: Arrhythmia and Electrophysiology
IS - 7
ER -