Wide Complex Tachycardia Differentiation: A Reappraisal of the State‐of‐the‐Art

Abstract

The primary goal of the initial ECG evaluation of every wide complex tachycardia is to determine whether the tachyarrhythmia has a ventricular or supraventricular origin. The answer to this question drives immediate patient care decisions, ensuing clinical workup, and long‐term management strategies. Thus, the importance of arriving at the correct diagnosis cannot be understated and has naturally spurred rigorous research, which has brought forth an ever‐expanding abundance of manually applied and automated methods to differentiate wide complex tachycardias. In this review, we provide an in‐depth analysis of traditional and more contemporary methods to differentiate ventricular tachycardia and supraventricular wide complex tachycardia. In doing so, we: (1) review hallmark wide complex tachycardia differentiation criteria, (2) examine the conceptual and structural design of standard wide complex tachycardia differentiation methods, (3) discuss practical limitations of manually applied ECG interpretation approaches, and (4) highlight recently formulated methods designed to differentiate ventricular tachycardia and supraventricular wide complex tachycardia automatically.

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Wide Complex Tachycardia Differentiation:
A Reappraisal of the State#of#the#Art
Anthony H. KashouPeter A. NoseworthyChristopher V.
DeSimoneAbhishek J. DeshmukhSamuel J. AsirvathamAdam M. May
Ahead of Print • DOI: 10.1161/JAHA.120.016598 • Publication Date (Web): 19 May 2020
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Journal of the American Heart Association
J Am Heart Assoc. 2020;9:e016598. DOI: 10.1161/JAHA.120.016598 1
MINI-REVIEW
Wide Complex Tachycardia Differentiation:
A Reappraisal of the State- of- the- Art
Anthony H. Kashou, MD; Peter A. Noseworthy, MD; Christopher V. DeSimone, MD, PhD;
Abhishek J. Deshmukh, MBBS; Samuel J. Asirvatham, MD; Adam M. May, MD
ABSTRACT: The primary goal of the initial ECG evaluation of every wide complex tachycardia is to determine whether the tach-
yarrhythmia has a ventricular or supraventricular origin. The answer to this question drives immediate patient care decisions,
ensuing clinical workup, and long- term management strategies. Thus, the importance of arriving at the correct diagnosis can-
not be understated and has naturally spurred rigorous research, which has brought forth an ever- expanding abundance of
manually applied and automated methods to differentiate wide complex tachycardias. In this review, we provide an in- depth
analysis of traditional and more contemporary methods to differentiate ventricular tachycardia and supraventricular wide com-
plex tachycardia. In doing so, we: (1) review hallmark wide complex tachycardia differentiation criteria, (2) examine the con-
ceptual and structural design of standard wide complex tachycardia differentiation methods, (3) discuss practical limitations
of manually applied ECG interpretation approaches, and (4) highlight recently formulated methods designed to differentiate
ventricular tachycardia and supraventricular wide complex tachycardia automatically.
Key Words: ECG ■ supraventricular tachycardia ■ ventricular tachycardia ■ wide complex tachycardia
Wide complex tachycardia (WCT) is a general
term that broadly denotes the presence of
ventricular tachycardia (VT) or supraventricu-
lar WCT (SWCT). As such, clinicians who encounter
patients with a WCT must consider a broad variety
of attributable causes including VT, SWCT with pre-
existing or functional aberrancy, SWCT developing
from impulse propagation using atrioventricular ac-
cessory pathways (ie, preexcitation), rapid ventricular
pacing, and tachyarrhythmias coinciding with toxic-
metabolic QRS duration widening (eg, hyperkalemia
or antiarrhythmic drug toxicity). Yet, without question,
the most critical task for the clinician is to determine
whether the tachyarrhythmia has a ventricular or su-
praventricular origin. Accurate discrimination of VT
and SWCT is incredibly vital as it impacts immediate
patient care decisions, ensuing clinical workup, and
long- term management strategies. Hence, proper pa-
tient management heavily relies on whether clinicians
are equipped with and appropriately apply effective
and reliable means to distinguish VT and SWCT.
After decades of rigorous research, the quest for
an effective, simplified, and practical means to non-
invasively differentiate WCTs has brought forth an
ever- expanding plethora of manually applied ECG
interpretation methods.1 –10 While manual methods
have proven their value in research settings, and can
be readily adopted by clinicians, arriving at correct
and timely VT or SWCT diagnoses remains a prob-
lematic undertaking—even among experienced elec-
trocardiographers. Recently, research has shown
that accurate WCT differentiation can even be ac-
complished by automated approaches implemented
by computerized ECG interpretation (CEI) software
programs.11,12
In this review, we provide an in- depth analysis of
traditional and contemporary methods to differenti-
ate WCTs. In doing so, we: (1) review hallmark ECG
characteristics used for VT and SWCT differentiation,
(2) examine the conceptual and structural design
of standard WCT differentiation methods, (3) high-
light practical limitations of manually applied ECG
Correspondence to: Adam M. May, MD, 660 South Euclid Avenue, CB 8086, St. Louis, MO 63110. E-mail: may.adam@wustl.edu
For Sources of Funding and Disclosures, see page 9.
© 2020 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the Creative
Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and
is not used for commercial purposes.
JAHA is available at: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution
and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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J Am Heart Assoc. 2020;9:e016598. DOI: 10.1161/JAHA.120.016598 2
Kashou etal Wide Complex Tachycardia Differentiation
interpretation approaches, and (4) discuss recently
devised methods designed to differentiate WCTs
automatically.
HALLMARK ECG CRITERIA
In general, WCT differentiation methods comprise one
or more ECG criteria that embody distinctive elec-
trophysiologic properties of VT and SWCT. Available
methods utilize ECG interpretation criteria that examine
the: (1) relationship of atrial and ventricular depolariza-
tion, (2) morphological configuration of QRS complexes
in specific ECG leads (ie, V1–V2 and V6), (3) WCT QRS
duration, (4) chest lead concordance, (5) mean electri-
cal axis (ie, QRS axis), (6) differences in ventricular ac-
tivation velocity, and (7) dissimilarities compared with
the baseline ECG. While all have proven their value in
distinguishing VT and SWCT, no single criterion or col-
lection of criteria promises diagnostic certainty.
Atrioventricular Dissociation
Wellens and colleagues1 highlighted the importance
of atrioventricular dissociation in 1978, which later ma-
tured into one of the most trusted ECG criteria to secure
VT diagnoses. As a general rule, VT may be confirmed
once atrioventricular dissociation is assuredly identi-
fied, especially when the ventricular rate exceeds the
atrial rate. Unsurprisingly, several WCT differentiation
methods include atrioventricular dissociation as a key
VT diagnostic criterion.2,3,8,9 However, although atrio-
ventricular dissociation may be quite valuable in estab-
lishing VT diagnoses, its absence does not rule out VT
since it is often not electrocardiographically apparent,
even among patients with known VT.
By definition, atrioventricular dissociation is present
when a self- governing ventricular rhythm autonomously
subsists the atrial rhythm. Classically, atrioventric-
ular dissociation is characterized by a series of QRS
complexes uncoupled from “dissociated” P waves
(Figure 1). When interpreting a 12- lead ECG display-
ing VT, atrioventricular dissociation may be recognized
as interspersed P waves nestled between or hidden
amidst overlapping QRS complexes and T waves.
Less commonly, atrioventricular dissociation manifests
as “capture” or “fusion” beats—each of which depict
varying degrees to which a supraventricular impulse
contributes to ventricular depolarization. In the case
of a capture beat, an ideally timed supraventricular
impulse seizes ventricular depolarization entirely and
produces a single QRS complex resembling the pa-
tient’s baseline rhythm. In the case of a fusion beat,
ventricular depolarization wavefronts emanating from
supraventricular and ventricular sources collide and
create a hybrid QRS complex that shares the ventric-
ular depolarization characteristics of the VT and base-
line rhythm.
Historically, the identification of atrioventricular
dissociation can be quite challenging. In general,
atrioventricular dissociation may be recognized in
roughly one fifth of VTs recorded by 12- lead ECG.
For many cases, VT will coexist with an atrial arrhyth-
mia (eg, atrial fibrillation) that lacks organized atrial
depolarization (ie, P waves). On other occasions,
atrioventricular dissociation simply cannot be rec-
ognized because of overlying QRS complexes and
T waves that obscure dissociated P wave activity.
Furthermore, it is essential to recognize that up to
approximately half of VTs will demonstrate retrograde
ventriculoatrial conduction,1 wherein ventricular im-
pulses conduct retrograde through the His- Purkinje
system to depolarize the atria. In such cases, VTs will
not exhibit atrioventricular dissociation; instead, they
demonstrate a regular (eg, 1:1 ventriculoatrial con-
duction) or an erratic (eg, ventriculoatrial conduction
with variable block) relationship.
Morphological Criteria
Meticulous examination of QRS configurations recorded
in particular ECG leads (ie, V1–V2 and V6) may provide
essential clues as to whether a WCT has a ventricular or
supraventricular origin. The pioneering works put forth
by Sandler and Marriott,13 Wellens etal1, and Kindwall
etal14—collectively known as the “classical morphologi-
cal criteria”—have added considerable value towards
the diagnostic evaluation of WCTs (Figure1).
In general, the primary purpose of using the mor-
phological criteria is to identify QRS configurations that
are consistent or inconsistent with aberrant conduction.
If a WCT demonstrates a QRS configuration incompat-
ible with typical right or left bundle branch block pat-
terns, VT is the most likely diagnosis. For example, VT
would be the most likely diagnosis for a WCT demon-
strating atypical right bundle block characteristics (eg,
Nonstandard Abbreviations and Acronyms
CEI computerized ECG interpretation
LR likelihood ratio
RWPT R wave peak time
SWCT supraventricular wide complex
tachycardia
Vi voltage excursion during the initial 40 ms
of the QRS complex
Vt voltage excursion during the terminal
40 ms of the QRS complex
VT ventricular tachycardia
WCT wide complex tachycardia
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J Am Heart Assoc. 2020;9:e016598. DOI: 10.1161/JAHA.120.016598 3
Kashou etal Wide Complex Tachycardia Differentiation
monophasic R wave in V1 or V2 and QS pattern in
V6). Conversely, if a WCT displays QRS configurations
representative of typical right and left bundle aber-
rancy, SWCT is the most likely diagnosis. For example,
SWCT would be the most probable diagnosis for WCTs
demonstrating a classic left bundle branch block pat-
tern (eg, r wave onset to S wave nadir <60ms in V1 or
V2 and notched monophasic R wave in V6). There are
only a few notable exceptions to this concept, including
bundle branch reentry or fascicular VTs—each of which
rapidly engage the His- Purkinje network and can result
in fairly typical “aberrant” morphologies.
QRS Duration
Ordinarily, VT primarily relies on an inefficient
means to depolarize the ventricular myocardium (ie,
cardiomyocyte- to- cardiomyocyte conduction). As a
result, VT commonly expresses longer QRS dura-
tions than SWCT. This distinction was verified ini-
tially by Wellens and colleagues,1 and later spurred
interest in proposed WCT QRS duration cutoffs
to define VT diagnoses: QRS >140ms for WCTs
with right bundle branch block pattern and QRS
>160 ms for WCTs with left bundle branch block
pattern.15 However, since VT and SWCT occupy
broad and overlapping QRS duration ranges, the
sole use of WCT QRS duration cutoffs to differen-
tiate WCTs is unsatisfactory. A substantial propor-
tion of SWCTs will display QRS durations >160ms,
especially among patients with ongoing antiarrhyth-
mic drug use, electrolyte disturbances, dramatic
conduction delays, or severe underlying structural
heart disease or cardiomyopathies. On the contrary,
many patients demonstrating idiopathic VT variants
or VTs that arise from within or rapidly engage the
His- Purkinje system demonstrate QRS durations
<140ms (Figure 1). In rarer cases, VTs may dem-
onstrate substantial impulse propagation within the
conduction system and express QRS durations
<120ms (eg, fascicular VT), thereby not fulfilling the
technical definition of WCT (ie, heart rate ≥100 beats
per minute and QRS duration ≥120ms).
Figure1. Hallmark ECG features of ventricular tachycardia (V T).
AV indicates atrioventricular; LAD, left axis deviation; LBBB, left bundle branch block; NW, northwest; RAD, right axis deviation;
RBBB, right bundle branch block; RWPT, R wave peak time; and WCT, wide complex tachycardia.
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J Am Heart Assoc. 2020;9:e016598. DOI: 10.1161/JAHA.120.016598 4
Kashou etal Wide Complex Tachycardia Differentiation
Chest Lead Concordance
Following the keen observations described originally
by Marriott,16 chest lead concordance has endured
as a strong distinguishing feature of VT. According to
its strict definition, concordance is present when QRS
complexes in all 6 precordial leads (V1–V6) uniformly
display a monophasic pattern having the same polarity
(ie, “R” for positive concordance and “QS” for nega-
tive concordance) (Figure1). In general, WCTs demon-
strating positive concordance most often arise from VT
originating from the posterobasal left ventricle. On the
other hand, WCTs demonstrating negative concord-
ance are practically diagnostic for VT originating for the
anteroapical left ventricle.
For practical use, chest lead concordance is a
highly specific (specificity >90%) but rather insensitive
(sensitivity <20%) diagnostic determinant for VT. Thus,
VT may be confirmed with near certainty if concor-
dance is present; however, if concordance is absent,
VT cannot be ruled out. Furthermore, it is worth noting
that SWCT may demonstrate concordance patterns in
a variety of rare circumstances. For example, SWCTs
with positive concordance may occur in the setting of
patients demonstrating preexcitation from left poste-
rior or left lateral accessory pathways. Alternatively,
although VT is nearly always responsible for a WCT
having negative concordance, unusual exceptions in-
clude rare SWCTs arising from extranodal accessory
pathways (ie, Mahaim connections) or those develop-
ing among patients with flecainide toxicity or chest wall
deformities.17
QRS Axis
Occasionally, WCT QRS axis offers an effective
means to distinguish VT from SWCT. To illustrate,
we must acknowledge that many of the dissimilari-
ties between VT and SWCT relate to the site of origin
and the summated direction of impulse propagation.
This difference is often responsible for substantial
differences in the resultant mean electrical vector,
including its frontal plane orientation (ie, QRS axis).
In general, most forms of SWCT with aberrancy (eg,
left bundle branch block and right bundle branch
block) produce a constrained range of mean elec-
trical vectors permitted by their representative con-
duction abnormalities. On the other hand, VT may
demonstrate a nearly limitless variety of mean electri-
cal vectors, many of which residing outside of the ex-
pected range for SWCT. For example, a scar- related
VT mapped to the anterolateral wall of the left ven-
tricle may produce a WCT having an atypical right
bundle branch block pattern and rightward and su-
perior QRS axis—a mean electrical vector orientation
not ordinarily observed for SWCTs with right bundle
branch block aberrancy.
In 1988, Akhtar and colleagues15 verified that
a rightward superior QRS axis (ie, northwest axis)
between −90° and −180° is highly predictive of VT
(Figure 1). Subsequently, several manually applied
WCT differentiation methods, including Vereckei aVR
algorithm,6 Jastrzebski VT score,8 and the limb lead
algorithm,10 have knowingly incorporated an ECG
criterion (ie, dominant R wave in lead aVR) that es-
sentially employs QRS axis as a key diagnostic de-
terminant. Several authors have also shown that the
coexistence of left- or right- axis deviation with right
or left bundle branch block, respectively, to be quite
specific for VT.1,15,18
Differences in Ventricular Activation
Velocity
Careful inspection of the first components of the QRS
complex, along with its comparison to its terminal
segments, as a means to distinguish VT and SWCT,
has been adopted by a wide variety of WCT differen-
tiation criteria and algorithms.2,5 – 7,14,19 The basis for
this examination stems from the fact that SWCT and
VT ordinarily demonstrate marked differences in the
manner to which they commandeer or engage the
His- Purkinje network. For example, an SWCT with
left bundle branch block aberrancy will commonly
display rapid initial QRS deflections (eg, r wave dura-
tion <30ms in V1 or V2, or an RS interval <100ms
for QRS complexes in the precordial leads [V1–V6])
that arise from rapidly depolarized myocardial seg-
ments stimulated by preserved components of the
His- Purkinje network (ie, right bundle branch).2,14
Conversely, a VT wavefront that propagates and
spreads from a site of origin remote from specialized
conduction tissue, and thereby must utilize slower
cardiomyocyte- to- cardiomyocyte conduction, is ex-
pected to demonstrate delayed or “slurred” initial
components of the QRS complex (eg, R wave peak
time [RWPT] in lead II ≥50ms, or RS interval ≥100ms
in any of the precordial leads [V1–V6]).2,7 However,
once the VT impulse engages the conduction sys-
tem, and swiftly activates the remainder of the ven-
tricular myocardium, the terminal components of
the QRS complex will correspondingly demonstrate
more rapid or “sharper” deflections compared with
what was observed at the beginning of the QRS
complex (eg, ratio of the voltage excursion during the
initial [Vi] and terminal [Vt] 40ms of the QRS complex
<1) (Figure1).5,6
Comparison to the Baseline ECG
The value of comparing a patient’s WCT and baseline
ECG should not be underestimated. In 1985, Dongas
et al20 confirmed that WCTs with unchanged QRS
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J Am Heart Assoc. 2020;9:e016598. DOI: 10.1161/JAHA.120.016598 5
Kashou etal Wide Complex Tachycardia Differentiation
configurations in leads V1, II, and III compared with the
preexisting bundle branch block during sinus rhythm
were nearly always SWCT, while WCTs with noticeably
different QRS configurations were usually VT. Later,
in 1991, the multivariate analysis put forth by Griffith
and colleagues21 verified that substantial deviation in
QRS axis (ie, QRS axis change ≥40°) compared with
the baseline ECG was one of the most predictive ECG
features to diagnose VT. More recently, Pachon etal9
utilized comparisons of QRS morphology between the
WCT and the baseline ECG as one of the weighty diag-
nostic determinants within their point- based algorithm.
Recently, we introduced novel WCT differen-
tiation methods,11,12 , 22 which leverage the magni-
tude of change between the WCT and baseline
ECG as a means to effectively distinguish VT and
SWCT (Figure 1). We described how universally
available computerized measurements derived
from CEI software may be used to precisely quan-
tify specific changes between the WCT and base-
line rhythms.11,12, 2 2 For example, the so- called WCT
Formula uses quantifiable QRS amplitude changes
(eg, frontal and horizontal percent amplitude change)
between paired WCT and baseline ECGs to estab-
lish an estimated VT probability.11 Similarly, the VT
prediction model utilizes measurable changes in the
QRS axis, T axis, and QRS duration between paired
WCT and baseline ECGs to determine VT likelihood.12
Such methods may be readily embedded into auto-
mated ECG interpretation software systems to re-
duce the time necessary for an accurate diagnosis.
However, it must be acknowledged that a distinct
disadvantage of these novel approaches is that they
require a baseline ECG (ie, an ECG recorded before
or after the WCT event) for their implementation.
STRATEGIC BLUEPRINTS FOR
TRADITIONAL METHODS
Decades of clinical research has brought forth a wide
variety of thoughtfully designed methods to differenti-
ate VT and SWCT. Separate from choosing the ideal
Figure2. Various wide complex tachycardia (WCT) differentiation algorithmic designs and algorithms.
AF indicates atrial fibrillation; AV, atrioventricular; LBBB, left bundle branch block; LR, likelihood ratio; RBBB, right bundle branch
block; RWPT, R wave peak time; SWCT, supraventricular wide complex tachycardia; and V T, ventricular tachycardia.
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J Am Heart Assoc. 2020;9:e016598. DOI: 10.1161/JAHA.120.016598 6
Kashou etal Wide Complex Tachycardia Differentiation
electrophysiological determinants to secure accurate
WCT differentiation, algorithm creators were account-
able for devising the organizational structure and op-
erative mechanics that will enable their algorithm’s
generalized use. In the following sections, we: (1) re-
view the most common algorithm designs, (2) discuss
the overarching rationale behind their formulation, and
(3) examine the unique advantages and limitations for
each diagnostic approach.
Multistep Algorithms
Without question, the most commonly utilized ap-
proaches to differentiate WCTs are the multistep
decision- tree algorithms, including the Brugada,2
Vereckei aVR,6 and limb lead algorithms10 (Figure2).
In general, multistep algorithms prompt users to ad-
dress a series of sequentially applied inquiries, with
each step requesting the ECG interpreter to deter-
mine whether a highly specific attribute of VT is pre-
sent or absent. If an affirmative response is rendered
at any particular algorithm step, the algorithm’s ap-
plication is complete and a VT diagnosis is secured.
On the other hand, before SWCT is diagnosed, the
ECG interpreter must navigate through the entire al-
gorithm and confirm that each step warrants a nega-
tive response. In other words, SWCT diagnoses may
only be reached once all highly specific attributes for
VT, examined by the particular multistep algorithm,
are absent.
In the early 1990s, Brugada and colleagues2 were
the first to conceptualize, organize, and then introduce
a multistep decision- tree algorithm design for WCT
differentiation. Their seminal work provided clinicians
with clear and straightforward steps to reach a defin-
itive diagnosis. The authors hoped that the multistep
decision- tree design would help resolve more ambigu-
ous cases in which the WCT shares features support-
ive of SWCT and VT.
Since their inception, multistep algorithms have
served as an excellent means for clinicians to wholly
commit to VT or SWCT diagnosis with reasonably
good diagnostic accuracy. Nevertheless, there are
notable limitations worth acknowledging. For exam-
ple, one common problem is that clinicians are ordi-
narily left unapprised of the likelihood that their VT
or SWCT diagnoses are accurate. Unless clinicians
(1) are sufficiently informed of the performance met-
rics (eg, positive likelihood ratio [LR]) afforded by the
algorithm step responsible for the diagnosis, and (2)
accurately gauge the patient’s pretest probability for
VT or SWCT, they will not have a precise determi-
nation of whether their diagnosis is, in fact, correct.
Another limitation is that multistep algorithms pur-
posely examine a narrower scope of ECG attributes.
Although restricting the number of criteria evaluated
by an algorithm helps ensure that it is readily recalled
and easily implemented, this strategy ultimately in-
creases the risk for overlooking other relevant di-
agnostic ECG findings. For instance, clinicians who
choose to exclusively use the Vereckei aVR algo-
rithm6 may paradoxically threaten near- certain VT
diagnoses for WCTs demonstrating clear atrioven-
tricular dissociation.
VT as Default Diagnosis
In 1994, Griffith etal3 introduced an alternative WCT
differentiation method (ie, Griffith algorithm). For this
algorithm, the authors devised a reversed strategy:
VT is the default diagnosis, and SWCT diagnoses
may be reached only when the classical criteria of
typical left or right bundle branch block is present
(Figure2). According to their algorithm, an SWCT di-
agnosis may be made for WCTs displaying findings
consistent with typical left bundle branch block (ie,
rS or QS wave in leads V1 and V2, r wave onset to S
wave nadir <70ms in leads V1 and V2, and mono-
phasic R wave without a q wave in lead V6) or right
bundle branch block (ie, rSR’ morphology in lead
V1, RS complex in lead V6, and R wave amplitude
greater than S wave amplitude in lead V6). Thus, if
a WCT does not demonstrate QRS configurations
classic for SWCT because of aberrancy, VT is the
elected diagnosis. Hence, instead of relying on highly
specific ECG criteria to rule in VT, highly specific ECG
criteria are used to rule in SWCT.
The distinct advantage gained by using the Griffith
algorithm is that the majority of VTs will be correctly
identified. However, although this reversed approach
ensures strong diagnostic sensitivity for VT, it does
so at the expense of its diagnostic specificity. In other
words, since the Griffith algorithm deliberately limits
the means to how an SWCT diagnosis is reached, a
substantial number of SWCTs may be misclassified as
VT—especially those that demonstrate nonclassical
aberrancy or preexcitation.
Bayesian Approach
In 2000, Lau etal4 introduced a novel WCT differentia-
tion method centered around the use of LRs to distin-
guish VT and SWCT. The so- called Bayesian algorithm
couples a predetermined “pretest odds of VT” with the
predictive indices (ie, LRs) of a wide assortment of ECG
criteria to secure a “posttest odds of VT.” For practical
use, the Bayesian algorithm assumes a pretest odds
(ie, positive LR of 4) and multiplies this value by a com-
pilation of other LRs, each denoting the presence or
absence of specific ECG criterion (eg, positive LR of
50 for a monophasic QS in lead V6) (Figure2). Once
the serial multiplication of LRs is complete, the posttest
odds of VT (ie, LR) is established. If the final LR is ≥1,
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J Am Heart Assoc. 2020;9:e016598. DOI: 10.1161/JAHA.120.016598 7
Kashou etal Wide Complex Tachycardia Differentiation
VT is the diagnosis; if the final LR is <1, SWCT is the
diagnosis.
By conducting this mathematical procedure for
a wide variety ECG features, the Bayesian algorithm
deliberately evades the 2 significant limitations that
commonly thwart hierarchal multistep algorithms: (1)
imperfect ascertainment (ie, presence or absence of
certain ECG criteria cannot be confirmed), and (2) in-
complete consideration of all relevant ECG features
(eg, outright VT diagnosis reached after just one algo-
rithm step). However, this method mandates that the
interpreter engage in an intricate series of mathemat-
ical computations, which may be quite challenging to
accomplish while under duress. Additionally, because
the Bayesian algorithm considers each ECG criterion
to be an independent variable, the assigned LRs for
individual variables are most likely overvalued. As a re-
sult, the final LR rendered by the Bayesian algorithm
may not accurately reflect the true likelihood for VT or
SWCT diagnoses.
Single Criterion Method
In 2008, Pava and colleagues7 proposed that a sin-
gle, stand- alone criterion may distinguish VT and
SWCT accurately. In their analysis, they described
the procedure of measuring the RWPT in lead II as
a simple- to- use, highly specific, and highly sensitive
means to discriminate VT from SWCT. As described
by the authors, the RWPT represents the time
elapsed between the QRS complex onset and peak
of the first positive or negative deflection. According
to the algorithm’s design, if a WCT demonstrates
an RWPT ≥50ms, VT is diagnosed; alternatively, if
a WCT demonstrates an RWPT <50 ms, SWCT is
diagnosed (Figure2).
Unlike using a sequential series or compilation
of ECG criteria to differentiate WCTs, the principal
advantage of using a stand- alone criterion is that it
may be readily recalled and promptly implemented
by clinicians wishing to secure rapid VT or SWCT di-
agnoses. However, notwithstanding the impressive
diagnostic performance first reported for the RWPT
criterion, it is now abundantly clear that solely relying
upon highly specific but nonsensitive criteria to differ-
entiate WCTs will substantially jeopardize clinicians’
ability to recognize VT.8,10,23 It should be noted that
similar diagnostic limitations would be readily ob-
served for other criteria having exceptionally strong
specificity but limited sensitivity for VT (eg, atrioven-
tricular dissociation).
Point- Based Scoring Methods
In many cases, VT and SWCT cannot be confi-
dently distinguished using 12- lead ECG interpretation
alone. Occasionally, standard criteria to establish VT
diagnoses may not be unequivocally present or absent
(eg, “Are those small deflections dissociated P waves
or ECG artifact?”), and manual measurements essen-
tial for establishing the correct diagnosis may be at the
margin of predefined thresholds (eg, “Is the RS interval
convincingly <100ms or ≥100ms?”). Additionally, there
are occasions where criteria tend to be quite vulner-
able to human error and imprecision (eg, measurement
of Vi/Vt for minuscule QRS complexes in lead aVR).
Furthermore, it is not rare for WCT to simultaneously
possess ECG characteristics consistent with both VT
and SWCT. Finally, we must also not overlook that
many diagnostically challenging VT subtypes (eg, fas-
cicular VT or bundle branch reentry) routinely escape
ECG criteria emphasized by standard WCT differentia-
tion methods.
As a result of the aforementioned diagnostic chal-
lenges, it is easy to see why subscribing to one or
more WCT methods that wholly commit to an absolute
VT or SWCT diagnosis is problematic. Consequently,
several authors chose to devise an alternative ap-
proach to differentiating WCTs (ie, point- based algo-
rithms) (Figure2).8,9 Rather than absolutely committing
to a definite SWCT or VT diagnosis for every WCT,
point- based scoring methods purposely aim to iden-
tify WCTs with near- certain VT or SWCT diagnoses.
For example, the point- based algorithm put forth by
Jastrzebski etal8 (ie, the VT score) has demonstrated
the capacity to confirm VT with near certainty for a
substantial proportion of WCTs. According to their
method’s design, if a WCT possesses several highly
specific criteria that summate into a high VT score, VT
may be assuredly diagnosed (eg, positive predictive
value of 100% for a VT score ≥4). A similar approach
is used for the point- based algorithm described by
Pachón and colleagues.9 According to their algo-
rithm, a near- definite confirmation for VT (ie, positive
predictive value of 100% for a score ≥2) or SWCT
(ie, positive predictive value of 98% for a score −1)
may be established for more than half of evaluated
WCTs.9
PRACTICAL LIMITATIONS OF
TRADITIONAL METHODS
The value of any diagnostic tool is dependent on
the context in which it is used. Although individual
WCT differentiation methods demonstrate their own
unique shortcomings, the most emblematic weak-
ness is that they wholly rely upon the ECG interpreter
for their proper execution. In general, traditional
ECG interpretation methods require clinicians to: (1)
scrupulously examine patients’ 12- lead ECG, and
(2) carefully apply specific ECG criteria to establish
a correct VT or SWCT diagnosis. Thus, manually
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J Am Heart Assoc. 2020;9:e016598. DOI: 10.1161/JAHA.120.016598 8
Kashou etal Wide Complex Tachycardia Differentiation
applied interpretation approaches are entirely de-
pendent on the competency of the ECG interpreter,
and therefore are quite vulnerable to improper ap-
plication or abstained use. As a consequence, the
generalized usage of manual ECG interpretation
methods is unsurprisingly problematic—particularly
for clinicians who must promptly diagnose and man-
age high- acuity patients.24–27
Another relevant, but often overlooked, limita-
tion stems from the fact that WCT differentiation
methods were uniformly derived2,5–8,21 and inde-
pendently validated8,23,25,26,28 using select investi-
gational groups (ie, only patients who undergo an
electrophysiology procedure) and controlled exper-
imental conditions (ie, ECG interpretation performed
by heart rhythm experts separated from the actual
clinical settings in which the WCT presented). In fact,
to date, only one validation study has assessed di-
agnostic performance using a broader collection of
WCTs expected to be encountered in “real- life” clin-
ical practice (ie, evaluating WCTs from patients with
and without an accompanying electrophysiology
study).27 Consequently, it remains largely unknown
whether the diagnostic performance of standard
WCT differentiation algorithms or criteria would be
sufficiently preserved when they are implemented in
actual clinical practice. Unfortunately, a clear under-
standing of the overall practical value of conventional
WCT differentiation methods will likely never be real-
ized, as it would not be feasible to prospectively test
their diagnostic performance within genuine clinical
circumstances.
NOVEL METHODS AND FUTURE
DIRECTIONS
Ideally, reliable WCT differentiation would occur imme-
diately upon 12- lead ECG acquisition. Unfortunately,
currently available CEI software programs have not yet
achieved sufficient diagnostic accuracy for complex
heart rhythms,29 including WCT differentiation. As a re-
sult, clinicians must rely primarily on traditional manu-
ally applied ECG interpretation methods to render an
accurate VT and SWCT diagnosis.
However, our recent work has challenged this
limitation with several novel automated methods to
distinguish VT and SWCT accurately.11,1 2 Through
the use of readily available ECG data routinely pro-
cessed by CEI software, well- established and math-
ematically formulated VT predictors (eg, frontal and
horizontal percent amplitude change) may be used
to yield accurate VT and SWCT predictions automat-
ically. A central feature of these methods is that they
provide clinicians an impartial estimation of VT like-
lihood (ie, 0.00% to 99.99% VT probability) through
the use of logistic regression modeling—a procedure
that may operate independently of clinicians’ ECG
interpretation competency. Prospective and forth-
coming methods will similarly deliver unambiguous
estimations of VT probability using machine learning
modeling techniques (eg, artificial neural networks
or random forests). By these means, clinicians will
be able to integrate estimated VT probabilities with:
(1) diagnoses reached by other WCT differentiation
methods (eg, Brugada algorithm or the VT score),
and (2) other particularly important diagnostic de-
terminants (eg, history of structural heart disease or
myocardial infarction). Once incorporated in CEI soft-
ware platforms, automated methods may substan-
tially help clinicians accurately distinguish VT and
SWCT.
As we progress further into an era that will be
dominated by automation and machine learning,
the prospect of integrating sophisticated and highly
accurate processes into computerized software to
accurately differentiate WCTs is not far away. By
solely analyzing 12- lead ECG recordings, machine
learning techniques have already shown the ability
to predict age and sex, as well as detect left ven-
tricular systolic dysfunction and hypertrophic car-
diomyopathy.30–33 Thus, it seems increasingly likely
that automated processes that leverage the power
of machine learning will one day help escape the
limitations that plague traditional WCT differentiation
approaches and enable highly accurate and timely
WCT differentiation. It is through the development,
refinement, and eventual integration of sophisticated
automated approaches into CEI software we can
hope to transform WCT differentiation into an anti-
quated diagnostic dilemma.
CONCLUSIONS
Decades of research have produced a rich literature
base and an expanding myriad of diagnostic ap-
proaches to help clinicians accurately differentiate
WCTs. Traditional manually applied WCT differentia-
tion methods have proven their value in distinguish-
ing the majority of WCTs; however, they uniformly
depend on the ECG interpreter for their implementa-
tion, rendering them particularly susceptible to their
improper execution or refrained utilization. Promising
automated WCT differentiation methods that make
use of CEI software programs are beginning to
emerge, signaling the eventual introduction of novel
alternative solutions to effectively distinguish VT and
SWCT.
ARTICLE INFORMATION
Received March 19, 2020; accepted April 13, 2020.
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J Am Heart Assoc. 2020;9:e016598. DOI: 10.1161/JAHA.120.016598 9
Kashou etal Wide Complex Tachycardia Differentiation
Affiliations
From the Departments of Medicine (A.H.K.), and Cardiovascular Diseases
(P.A.N., C.V.D., A.J.D., S.J.A.), Mayo Clinic, Rochester, MN; Cardiovascular
Division, Washington University School of Medicine, St. Louis, MO
(A.M.M.).
Sources of Funding
This work was supported by the Depar tment of Cardiovascular Diseases at
Mayo Clinic in Rochester, MN.
Disclosures
Adam May, Chris DeSimone, and Abhishek Deshmukh are obliged to dis-
close that they are “would- be” beneficiaries of intellectual property that is
briefly discussed in the article. The remaining authors have no disclosures
to report.
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