Repolarization gradients can provide the substrate for reentry. Limitations have been reported in clinical identification of abnormal substrates using electrocardiographic techniques.
Determine the discriminant criteria associated with arrhythmogenesis in an experimental model of repolarization gradients, and evaluate noninvasive electrical imaging (ECGI) to detect them.
Langendorff perfused pig hearts (n=8) and a human donor heart were suspended in a human shaped torso tank. Repolarization gradients were created through perfusion of dofetilide and pinacidil into adjacent perfusion beds. Arrhythmic inducibility was tested using an S1S2 protocol. 108 epicardial electrograms (EGM) and 256 body surface ECG were recorded simultaneously. EGMs were also reconstructed using ECGI. Repolarization times (RT) were calculated as the steepest upstroke of the EGM T-wave. Receiver operator characteristic (ROC) curve analysis was used to assess the diagnostic ability of different epicardial RT parameters (mean RT, RT dispersion [RTD], max RT gradient [ΔRT]) for VF risk. ECGI reconstruction of these RT parameters were validated against direct recordings.
The area under the ROC curve (AUC) demonstrated that the recorded RT parameters were good predictors of VF inducibility (AUC=0.77-0.82). Their ratios (RTD/meanRT and ΔRT/meanRT) were the most discriminant (AUC>0.98). ECGI accurately reconstructed EGM T-waves (CC=0.85±0.25) and RT maps (CC=0.73±0.11). ECGI-reconstructed mean RT, RTD and ΔRT were strongly correlated to those directly measured (R2=0.98, 0.89 and 0.75 respectively). Although the ECGI-derived RT parameters were slightly less discriminant than those recorded (AUC=0.64-0.81), ECGI computed RTD/meanRT and ΔRT /meanRT were more efficient in predicting VF risk (AUC>0.90) than the 12-lead ECG measured QTc (AUC=0.71).
Repolarization gradients (RTD or ΔRT) and the global recovery of the tissue (meanRT) are critical factors in arrhythmia induction. Their relationship is more predictive of arrhythmia than either factor individually. ECGI can be used to accurately non-invasively reconstruct these critical features, having the potential to improve risk-stratification in patients.
© 2021 Published by Elsevier Inc.