complementary to or better than the electrophysiologist?
Thomas Rostock, MD1, Alexander P. Benz, MD,
MSc1,2 and Raphael Spittler, MD,
MSc1
1 University Hospital Mainz, Center for Cardiology,
Department of Cardiology II / Electrophysiology, Mainz, Germany
2 Population Health Research Institute, McMaster
University, Hamilton, Ontario, Canada
Running title: AI-guided mapping of persistent AF
Word count: 1451
Corresponding author:
Thomas Rostock, MD
University Hospital Mainz
Center for Cardiology
Cardiology II / Electrophysiology
Langenbeckstr. 1
55131 Mainz, Germany
Email: throstock@gmail.com
Disclosures: None declared.
Funding: None.
Keywords: Atrial fibrillation, mapping, mechanisms, artificial
intelligence
Artificial intelligence (AI) has a fascinating history. It all started
in the 1940s and 1950s with foundational work by pioneers like Alan
Turing, who proposed the concept of a universal machine that could
simulate any computation. In 1956, the term ”AI” was coined at the
Dartmouth Conference. This marked the beginning of AI as a field. In the
1960s and 1970s, there was optimism about rapid progress in AI. Early
successes, like the creation of algorithms for problem-solving and the
development of the ELIZA program, which could simulate a
psychotherapist, fueled this enthusiasm. However, in the late 1970s and
1980s, AI hit a period of stagnation, known as the ”AI winter,” due to
the limitations of the technology of the time and reduced funding. The
1990s saw a revival with the advent of machine learning and the
internet. The focus shifted to data-driven approaches. This period also
saw the development of practical applications, such as IBM’s Deep Blue,
which defeated chess champion Garry Kasparov. The 21st century brought
even more advancements. The development of more sophisticated machine
learning techniques, like deep learning, and the availability of large
amounts of data and powerful computing resources, led to significant
breakthroughs. AI systems like IBM’s Watson winning Jeopardy! and the
development of autonomous vehicles are examples of recent
achievements.1
Using the prompt “please give us a brief history of AI”, the paragraph
above was created by ChatGPT1, an interactive software
based on a large language model. Some of the details provided by the
AI-based chatbot were unknown to the authors. Therefore, we verified the
validity of the facts presented by the software. We believe this
represents a simple example on how AI-based solutions can be
complementary to human labor.
Modern technology has long since found its way both into clinical
practice and medical research. In the field of cardiac
electrophysiology, automated processing of data from various sources
(e.g., a 12-lead ECG, cardiac imaging, three-dimensional high-density
mapping or intracardiac electrograms) may support the evaluation of
complex pathophysiological mechanisms or offer guidance for clinical
decision making. Recently, there has been a growing interest in the
evaluation of AI-based solutions such as deep learning. Examples include
the identification of suitable patients most likely to benefit from
cardiac resynchronization theraqpy2 or the
classification of complex intracardiac electrical patterns during
catheter ablation of atrial fibrillation (AF)3.
Machine learning may also be helpful for real-time, “live”
interpretation of data gathered by a three-dimensional mapping system
during catheter ablation. In a recent study, Seitz et al. introduced
Volta VX1, an AI-based, expert-trained, real-time software for the
mapping of persistent AF4. The Volta VX1 system is
based on a machine learning software aimed at the automated
identification of patient-specific electrogram patterns, i.e.,
multipolar electrogram dispersion.
In this issue of the Journal , Bahlke and colleagues report
procedural and clinical outcomes of 50 patients undergoing catheter
ablation of persistent AF, supported by the Volta VX1
software5. The majority of the patients included in
the study had long-standing persistent AF, and almost half had undergone
at least one prior ablation. During high-density mapping of the left and
the right atrium with a multi-electrode catheter and a conventional
three-dimensional mapping system, areas of electrogram dispersion were
automatically detected and tagged by the software. In addition to
standard pulmonary vein isolation, only areas identified by the Volta
software were targeted for ablation. The endpoint of successful ablation
was defined as termination of AF by conversion either to atrial
tachycardia or sinus rhythm. Subsequent ablation of consecutive atrial
tachycardia was performed to achieve sinus rhythm. All identified areas
of electrogram dispersion were targeted, even if conversion occurred
before completing the ablation protocol. When considered appropriate,
the ablated areas were connected with each other or with anatomical
barriers following a so-called “ablate and connect” strategy, in order
to avoid iatrogenic creation of arrhythmogenic substrate. Catheter
ablation was performed using a high-power short-duration protocol. The
mean procedure duration was approximately 3 hours, and the mean
radiofrequency ablation time was 29 minutes. The authors observed a mean
40-ms increase in AF cycle length during the ablation procedure (162 to
202 ms), and conversion of AF to atrial tachycardia or termination to
sinus rhythm was observed in 12 patients (24%). Accounting for a
blanking period of 6 weeks, approximately 40% of the patients were free
from arrhythmia recurrence at 1 year following the first Volta
VX1-supported catheter ablation (mean follow-up duration 1 ± 0.5 year).
Atrial tachycardia was the most common type of recurrent arrhythmia.
The study by Bahlke and colleagues adds to the existing literature on
algorithm-based mapping and catheter ablation of persistent AF. Previous
studies, including those investigating the Volta VX1 software, have
largely excluded patients with a maximum episode duration exceeding 1
year. However, a patient-specific, individualized approach to catheter
ablation targeted at atrial substrate modification may have its greatest
potential in patients with more advanced disease.
We believe the following aspects should be considered when interpreting
the data presented by Bahlke and colleagues:
1) Long-standing persistent AF is commonly associated with severe
alterations of atrial electrical and structural substrate
characteristics. It is uncertain whether i) Therefore, the key questions
are: do repetitive organized activation patterns still exist in this
complex context and ii) whether and how they could reliably be
identified. Even though AF termination was observed in approximately 1
in 4 patients in the study, the authors did not report the corresponding
proportion of patients with long-standing persistent AF in this group.
The patient characteristics suggest a wide distribution of the baseline
duration of AF (mean 50 ± 54 months), ranging from 1 month to more than
16 years. It may therefore be intuitive to assume that AF conversion or
termination during ablation predominantly occurred in patients with
shorter baseline AF duration.
2) In consideration of the complex electrical and structural alterations
associated with long-standing persistent AF, there may be endpoints
other than AF termination that are potentially better suited to define
the procedural success of catheter ablation. Modification of the atrial
substrate(s) in the absence of a clearly identified arrhythmia mechanism
may rather aim at i) facilitating previously unsuccessful electrical
cardioversion, ii) the prevention of early re-initiation, iii) the
maintenance of sinus rhythm to enable reverse atrial remodeling or iv)
the prevention of subsequent atrial tachycardia. With the Volta VX1
approach used in this study (as with other electrogram-guided
approaches), the majority of observed arrhythmia recurrence was due to
atrial tachycardia. Subsequent atrial tachycardia frequently occurs
after electrogram-guided ablation due to uncommon, non-macroreentrant
mechanisms and often prompts an additional procedure. Alternatively,
anatomical approaches (e.g., line-based ablation), with or without the
consideration of individual structural alterations such as low voltage
areas, may prove beneficial and potentially avoid atrial tachycardia
subsequent to ablation of AF.
3) Some studies have associated procedural AF termination with a
beneficial long-term outcome following catheter
ablation6. In the era of more extensive ablation, it
was not uncommon to accept significant damage to large areas of the
atrium. On the other hand, a significant proportion of patients without
termination does not experience arrhythmia recurrence. This particularly
interesting population has not been well characterized thus far. Until
today, there is no alternative procedural endpoint directly related to
the arrhythmia other than AF termination. Furthermore, it remains
uncertain whether or not to aim at more extensive ablation in the
absence of termination. Complete ablation of all targets identified by a
machine learning-guided software such as Volta VX1 may represent a
suitable endpoint that deserves further investigation. Alternatively,
specific electrophysiological patterns in response to ablation like
prolongation of AF cycle length, changes in local activation gradients
and arrhythmia organization may contain valuable information. It is
conceivable that AI-guided interpretation of the entirety of such
parameters may identify other candidate endpoints for catheter ablation
of more advanced forms of persistent AF.
4) A potential advantage of the Volta VX14 system is
the automated and reproducible identification of dispersion patterns
during AF that, per se , can be replicated by an
electrophysiologist. With other software-based AF simulation and mapping
tools (e.g., the CardioInsight7 and
FIRM8 mapping system), the operator is forced to rely
on the software output, without the opportunity to reassess or verify
individual parameters. We believe that in our field, AI may currently
have its greatest potential in being complementary to AF mapping
interpretation of the electrophysiologist.
Finally, the optimal approach to catheter ablation of long-standing
persistent AF remains unknown. AI-guided solutions such as the Volta VX1
system may specifically contribute to a better understanding of the
pathophysiology of more advanced forms of AF. An individualized approach
to catheter ablation that is targeted at the underlying mechanism(s) may
ultimately move the field forward. Adequately designed, randomized
clinical trials are needed to demonstrate that technological
advancements improve patient-important outcomes.