Abbreviations:
CA catheter ablation
DP double potentials
FP fractionated potentials
LAVAs local abnormal ventricular activities
LP late potentials
LV left ventricular
RV right ventricular
SHD structural heart disease
VT ventricular tachycardia
The legendary story of John Henry is the classic human vs machine tale
set in 1800s in West Virginia, USA. John Henry worked on the railroad
construction for the Westward expansion of America, He was of legendary
size and strength, over 6 feet tall and able to drive a rail spike with
a single swing of his twenty-pound hammer. As legend goes, John Henry’s
prowess was measured in a race against the newly developed steam-powered
rock drilling machine. The race was set to see whether human or machine
made the most progress in drilling. On that day, John Henry emerged
victorious over the machine only to die in victory with hammer in
hand. This epic story, although not quite the same, carries some
parallels to the contemporary challenges that face ventricular
tachycardia (VT) ablation in the setting of structural heart disease
(SHD)-a disease which poses one of the greatest challenges in
electrophysiology, where the comparison is now made between automatic vs
manual annotation during substrate mapping.
SHD-related VT is predominantly re-entrant in mechanism, in or around
ischemic or non-ischemic scar. Catheter ablation (CA) is a class I
recommendation for the drug-refractory treatment.1Identification of VT isthmii is critical for the efficacy of CA.
Historically, activation mapping, followed by entrainment and
termination of VT with ablation has allowed identification of VT isthmii
and formed the basis for SHD related VT ablation.2However, unreliable inducibility, conversion of VT to another,
non-sustainability of VT to permit activation/entrainment mapping and
the hemodynamic instability during VT, meant that alternative mapping
strategies were desirable. This formed the basis for the evolution of
substrate mapping. Substrate mapping allows identification of putative
VT isthmii by using surrogate markers in stable sinus or paced rhythm
representing slow, discontinuous conduction in and around scar.
Substrate mapping has improved the feasibility of VT ablation by
avoiding the deleterious consequences of prolonged episodes of sustained
VT to permit activation/entrainment mapping.3
One such surrogate marker, termed local abnormal ventricular activities
(LAVAs)4, represent regions of delayed conduction and
are defined as sharp, high-frequency ventricular potentials, which are
distinct from the far-field electrogram and often display fractionated,
double or multiple components, separated by low-amplitude signals or an
isoelectric interval.4 Typically, these signals have
been annotated and tagged on the electroanatomic map manually, because
automated algorithms are generally limited in their ability to
differentiate the delayed multiple component signals within the scar
from the initial far-field component of the electrogram. Advances in
multi-electrode mapping catheters and electroanatomic mapping systems
now allow rapid collection of a large volume of intra-cardiac
electrograms, which poses a significant challenge to this real-time
manual electrogram annotation. The Lumipoint algorithm (Boston
Scientific, Marlborough, MA) can annotate signals throughout the entire
mapping window, as opposed to a peak signal and is therefore able to
identify LAVAs rapidly.5 It is plausible to suggest
that the automated annotation of LAVAs may improve procedural efficiency
and potentially, the consistency of point annotation and rapid
delineation of VT isthmii.
In this study, Nakatani et al.6 describe 100 patients
with SHD ([ischemic n=75, non-ischemic n=25]) related VT undergoing
electroanatomic mapping and CA, using the ultra-high-density Rhythmia
system (Boston Scientific). The study aimed to determine if the
Lumipoint algorithm is accurate in detecting LAVAs. The automatic
identification of LAVAs was performed retrospectively using the
algorithm features to detect late potentials (LP), fragmented potentials
(FP) and double potentials (DP), this was directly compared to the
real-time manual electrogram annotation of LAVAs and sites of ablation,
performed during each procedure. Mapping was performed using the
Intellamap OrionTM catheter (Boston Scientific) in the
left ventricle (83%), right ventricle (8%) and epicardium (9%). The
average number of points collected per map were 9976 ± 7477 over 31 ± 11
minutes. Overall, the 3 Lumipoint features complemented each other and
correctly identified LAVAs equally or better than manual annotation in
79% of patients (64% equally, 15% better). Conversely there were 18%
of patients in whom LAVAs were missed by Lumipoint but identified by
manual annotation. These discrepancies were predominantly due to
Lumipoint detecting very low amplitude LAVAs better than manual
annotation but missing fragmented signals with few activation components
(the FP feature was set to recognise a minimum of 7 activation
components). The impact of wavefront direction (sinus rhythm [n=34],
right ventricular [RV] pacing [n=63] or left ventricular
[LV] pacing [n=3]) and scar location on the accuracy of the 3
algorithm features (LP, FP, DP), was then assessed by manually checking
each automatically highlighted area to determine a percentage of LAVAs
within each region. This demonstrated that automatic FP annotation
accurately detected LAVAs irrespective of scar location or wavefront
direction (Figure). Automatic LP annotation accurately identified LAVAs
in sinus rhythm, but during RV pacing it less accurately detected LAVAs
in the LV septum, apex, and epicardium, compared to in the LV lateral
wall (Figure). Automatic annotation of DP was unaffected by wavefront
direction but was less accurate at identifying LAVAs in the RV and
epicardium (Figure). Hence it seems that overall, Lumipoint is better at
detecting LAVAs during sinus rhythm in areas of LV scar. With regards to
the correlation between LAVAs and VT isthmii, 19 patients underwent
activation/entrainment mapping and VT isthmus identification during
their procedure. Of these, LAVAs were identified in the VT isthmus in
all 19/19 (100%) patients by automatic annotation, and in 17/19 (89%)
patients by manual annotation. The identification of LP corresponded to
the VT isthmus in 18/19 (95%), FP in 6/19 (32%) and DP in 5/19 (26%)
of these cases.
The authors ought to be congratulated on their important contribution to
substrate mapping using high density mapping and a highly sophisticated
electroanatomic mapping algorithm that attempts to automate the vast
amount of substrate mapping data detected. The study raises several
interesting points of discussion. The correct annotation of electrograms
collected during substrate mapping is dependent on multiple factors,
including the catheter electrode size and spacing, catheter contact with
the myocardium, the frequency of the signal/ability of the signal to be
seen, location of the mapping catheter and the direction of the
intrinsic or paced wavefront. All procedures were performed using the
Intellamap OrionTM catheter, which has 64 electrodes,
0.4 mm2 in size, with 2.5 mm inter-electrode spacing.
This large number of small electrodes allows a high sensitivity to
near-field signals and the creation of ultra-high-density
electroanatomic maps.7 Noise was filtered using a 0.02
mV cut-off but there was no disclosure regarding the gain used for
manual electrogram interpretation during the procedure. This is
particularly important because the findings suggest Lumipoint may be
able to detect LAVAs better than manual annotation when very low
amplitude signals were present. It is possible that these missed low
amplitude signals might have been more apparent if an increased gain was
used during manual annotation. In contrast, the Lumipoint algorithm
might have recognised more FP with few activation components if the
algorithm had been set to recognise a lower number of activation
components (eg. 5 instead of 7). The scar location and wavefront
direction were shown to impact the accuracy in the automatic detection
of LAVAs, in keeping with previous work by Tung et
al.8, whereby differing wavefront directions affected
electrogram amplitude and scar area. With regards to the detection of
LAVAs, automatic annotation relies on the abnormal signal falling within
the specified window of interest. However, if an area is activated
relatively early compared to this window, as can happen if a closely
adjacent site is paced (eg. RV pacing for septal scar), then delayed
conduction may appear earlier than the algorithm is programmed to accept
and therefore not identified as LAVAs.
The vast number of points collected per map, over a very short time,
highlights the considerable challenges faced when attempting to annotate
electrograms manually in real-time whilst performing ultra-high-density
mapping. The authors did not disclose overall procedure times and it
seems likely that manual annotation of electrograms continued after
substrate mapping or even at the end of the procedure. In addition, the
inclusion of ablated areas as regions of manually annotated LAVAs,
potentially affected the validity of the results, as such regions may
have been identified through either activation or pace mapping, without
the need for manual annotation of LAVAs. Nevertheless, these ablated
regions likely represented VT isthmii, and the purpose of this study was
primarily to determine the ability of Lumipoint to identify these
regions, therefore this approach seems reasonable. It should be noted
however, that complete reliance on the automatic annotation of LAVAs is
unable to guarantee the localisation of the critical VT isthmus, as the
algorithm will only identify regions where delayed/abnormal conduction
generates signals that fall within a specified window of interest,
potentially in an outer loop of the VT circuit or the scar borderzone.
Entrainment mapping is therefore required to determine the site of the
critical isthmus.
Previously, Martin et al.5, performed a similar study
on a smaller number of patients. The Lumipoint algorithm was applied
retrospectively to the electroanatomic maps of 27 patients who had
undergone CA of either ischemic (n=22) or dilated cardiomyopathy (n=5)
related VT. VT isthmii were accurately identified in 25/27 patients, as
seen by the manually annotated map, entrainment, and response to
ablation. The current study by Nakatani et al. builds on this prior
work, by including a larger patient population and describes the ability
of Lumipoint to identify the various components of LAVAs (LP, DP, and
FP) according to the cardiac rhythm/wavefront direction and site of scar
location. Both studies focused predominantly on patients with ischemic
cardiomyopathy, where regions of delayed conduction tend to be more
common, but the findings do suggest efficacy in detecting these regions
in non-ischemic patients, albeit in a small population.
In summary, the study by Nakatani et al.6 offers an
important insight into the potential utilisation of the automatic
Lumipoint algorithm, for identifying regions of delayed conduction in
the context of SHD related VT. Their findings suggest the retrospective
use of Lumipoint can achieve an overall equivalence to the real time
manual electrogram annotation of LAVAs. However, its accuracy is
affected by wavefront direction and scar location, performing best
during sinus rhythm for LV scar annotation. At present it remains likely
to be utilised as a supportive tool, with manual annotation still
required to check automatically annotated LAVAs. A future prospective,
randomised study would further validate these findings. As the legend of
John Henry taught us, although machines can automate a large part of
work in substrate mapping, human effort and knowledge is critically
necessary in working together to achieve cure for one of the most
challenging diseases of our era, that is SHD-related VT.