Cycle length statistics during human atrial fibrillation reveal refractory properties of the underlying substrate: a combined in silico and clinical test of concept study

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Characterization of cardiovascular profile of anti-influenza drug peramivir: A reverse-translational study using the isoflurane-anesthetized dog

An injectable anti-influenza drug peramivir has been reported to induce QT-interval prolongation in some phase III studies, although its thorough QT/QTc study was negative. We investigated the discrepancy among those clinical studies using isoflurane-anesthetized beagle dogs (n = 4). Peramivir in doses of 1 mg/kg/10 min (sub-therapeutic dose) followed by 10 mg/kg/10 min (clinically-relevant dose) was intravenously administered. Peramivir prolonged QT interval/QTcV and Tpeak-Tend, and tended to delay ventricular repolarization in a reverse-frequency dependent manner, indicating IKr inhibition in vivo. Meanwhile, peramivir did not alter P-wave duration, PR interval or QRS width, indicating a lack of impact on cardiac conduction via Na+ or Ca2+ channel inhibition in vivo. Peramivir prolonged Tpeak-Tend and tended to prolong terminal repolarization period, which would develop substrates for initiating and maintaining spiral reentry, respectively. Meanwhile, peramivir did not prolong J-Tpeakc, which could not induce early afterdepolarization, a trigger inducing torsade de pointes. Thus, our results support that clinical dose exposure of peramivir can delay the ventricular repolarization in influenza patients. Peramivir has only a small potential to induce torsade de pointes in patients with the intact hearts, but caution should be paid on its use for patients formerly having the trigger for torsade de pointes.

Personalized ablation vs. conventional ablation strategies to terminate atrial fibrillation and prevent recurrence

AIMS

The long-term success rate of ablation therapy is still sub-optimal in patients with persistent atrial fibrillation (AF), mostly due to arrhythmia recurrence originating from arrhythmogenic sites outside the pulmonary veins. Computational modelling provides a framework to integrate and augment clinical data, potentially enabling the patient-specific identification of AF mechanisms and of the optimal ablation sites. We developed a technology to tailor ablations in anatomical and functional digital atrial twins of patients with persistent AF aiming to identify the most successful ablation strategy.

METHODS AND RESULTS

Twenty-nine patient-specific computational models integrating clinical information from tomographic imaging and electro-anatomical activation time and voltage maps were generated. Areas sustaining AF were identified by a personalized induction protocol at multiple locations. State-of-the-art anatomical and substrate ablation strategies were compared with our proposed Personalized Ablation Lines (PersonAL) plan, which consists of iteratively targeting emergent high dominant frequency (HDF) regions, to identify the optimal ablation strategy. Localized ablations were connected to the closest non-conductive barrier to prevent recurrence of AF or atrial tachycardia. The first application of the HDF strategy had a success of >98% and isolated only 5-6% of the left atrial myocardium. In contrast, conventional ablation strategies targeting anatomical or structural substrate resulted in isolation of up to 20% of left atrial myocardium. After a second iteration of the HDF strategy, no further arrhythmia episode could be induced in any of the patient-specific models.

CONCLUSION

The novel PersonAL in silico technology allows to unveil all AF-perpetuating areas and personalize ablation by leveraging atrial digital twins.

Cycle Length Evaluation in Persistent Atrial Fibrillation Using Kernel Density Estimation to Identify Transient and Stable Rapid Atrial Activity

Purpose

Left atrial (LA) rapid AF activity has been shown to co-localise with areas of successful atrial fibrillation termination by catheter ablation. We describe a technique that identifies rapid and regular activity.

Methods

Eight-second AF electrograms were recorded from LA regions during ablation for psAF. Local activation was annotated manually on bipolar signals and where these were of poor quality, we inspected unipolar signals. Dominant cycle length (DCL) was calculated from annotation pairs representing a single activation interval, using a probability density function (PDF) with kernel density estimation. Cumulative annotation duration compared to total segment length defined electrogram quality. DCL results were compared to dominant frequency (DF) and averaging.

Results

In total 507 8 s AF segments were analysed from 7 patients. Spearman’s correlation coefficient was 0.758 between independent annotators (P < 0.001), 0.837–0.94 between 8 s and ≥ 4 s segments (P < 0.001), 0.541 between DCL and DF (P < 0.001), and 0.79 between DCL and averaging (P < 0.001). Poorer segment organization gave greater errors between DCL and DF.

Conclusion

DCL identifies rapid atrial activity that may represent psAF drivers. This study uses DCL as a tool to evaluate the dynamic, patient specific properties of psAF by identifying rapid and regular activity. If automated, this technique could rapidly identify areas for ablation in psAF.