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Predicting Transmural Lesion Formation and Steam-Pop Occurrence During Bipolar Ablation-Ex Vivo Porcine Model
Hisaki Makimoto1, Masashi Kamioka1, Tomonori Watanabe1
1Division of Cardiovascular Medicine, Department of Medicine Jichi Medical University School of Medicine Shimotsuke Tochigi Japan.
Background:
Bipolar radiofrequency (RF) ablation can create deeper myocardial lesions than unipolar ablation, yet its optimal settings remain undefined.
Objective:
To develop and validate predictive models for lesion transmurality and steam-pop occurrence during bipolar ablation.
Methods:
Ex vivo bipolar ablation was applied to porcine myocardium (5-20 mm thickness) with catheters placed bilaterally at 45-degree and 10-g contact-force. RF power (20-50 W) and duration (20-180 s) were systematically varied. Generalized linear models (GLM) were trained on 194 applications to predict transmurality and steam-pop from RF energy, tissue thickness, initial bipolar impedance, 5-s impedance drop (absolute and percentage [PercentImpDrop5]), and RF duration; 111 independent applications served as the validation.
Results:
Training yielded 95 transmural lesions (49%) and 11 steam-pops (5.7%). For transmurality, the model incorporating RF energy, RF duration, initial impedance, and tissue thickness achieved an area under the receiver-operating characteristics curve (AUC) of 0.95 (95% CI 0.91-0.99) with 88% sensitivity and 100% specificity. Omitting tissue thickness markedly degraded performance (AUC 0.68; DeLong, p = 0.003). For steam-pop, the model combining RF energy and PercentImpDrop5 showed the best discrimination (AUC 0.90 [0.82-0.97], sensitivity 84%, specificity 90%); notably, PercentImpDrop5 alone achieved comparable accuracy (AUC 0.89).
Conclusion:
Tissue thickness is the dominant determinant of transmural lesion formation, whereas early impedance drop serves as a reliable real-time indicator of steam-pop risk during bipolar RF ablation. These algorithms may help standardize bipolar ablation protocols by enabling prospective titration of energy delivery based on tissue characteristics and intraprocedural impedance monitoring.

