“Do the best you can until you know better. Then when you know
better, do better”—Maya Angelou
There was a time when defibrillators were not programmable. As a
last-ditch effort to provide protection for a patient with recurrent
ventricular tachycardia, you called your industry partner, prescribed an
implantable cardioverter defibrillator (ICD) with a particular therapy
rate, and after receiving a device prescribed for your particular
patient, saw to its implantation. While individualized for a particular
patient, our options were limited to choosing a rate for detection.
Certainly, the emphasis of ICD treatment was the sensitivity to detect a
life-threatening arrhythmia. In addition, the lack of stored
electrograms made it difficult to accurately adjudicate appropriate
versus inappropriate therapy. As ICD therapy matured, we began to
appreciate that inappropriate shocks occur, and they are associated with
poor outcomes including the frightening realization that they are
adversely associated with mortality1.
As such, measures such as empiric anti-tachycardia pacing (ATP), higher
detection rates, and prolonged detection times have led to decreased
rates of inappropriate therapy. In addition, currently available ICDs
are increasingly programmable. The 2015 HRS/EHRA/APHRS/SOLAECE expert
consensus statement on optimal implantable cardioverter-defibrillator
programming and testing provides straightforward guidelines on ideal
device programming.2 This document provides general
information regarding the use of multiple zones, different methods of
supraventricular tachycardia (SVT) versus ventricular tachycardia (VT)
discriminators, including atria-ventricular (A-V) relationship, sudden
onset, stability, and morphology. Given the differences between
manufacturers, the 2019 HRS/EHRA/APHRS/LAHRS focused update to 2015
expert consensus statement on optimal implantable
cardioverter-defibrillator programming and testing provide some
manufacturer specific recommendations for
programming3.
However, it is important to note that there is a paucity of data
comparing different devices and their respective discriminators. Given
fundamental differences in algorithms and device-specific programming
parameters, it is difficult to compare clinical results across
defibrillator brands. Moreover, while all device companies have changed
default programming values to increase detection times and rate
detection parameters, “out of the box” programming may not be ideal
for al ICD recipients. While the decrease in inappropriate therapy
employing these programming-strategies is well
demonstrated4, there are still many (if not most)
devices that remain in initial factory settings. This suggests that most
of us feel that this is simply good enough. For the photography
enthusiasts out there—when was the last time you adjusted the exposure
time on your camera? Or changed a lens? Most of us are content enough to
click away with our smartphone because it’s already pretty darn good.
But this begs the question—can we or should we do better?
Of all the potential programming optimization possible in the current
ICD, morphology discrimination might be the most taken for granted
(though I suspect defibrillation waveforms might be a very close
second). Different devices collect morphology templates in different
ways (all proprietary), creating a “black box” for the practicing
clinician. What does percentage match refer to? Can I program the
percentage match to an acquired template? What are the number of beats
that fulfil criteria and out of how many? How much variation in an
acquired template is seen normally? How much does aberrant conduction
affect this? More importantly, does it matter? One would assume
(correctly) that device manufacturers make seemingly Herculean efforts
to validate their respective technologies. However, this type of
evaluation does limit the type of comparisons that can be made across
different devices using agnostic and standardized parameters that are
analyzing actual arrhythmic events—data which is more relevant to the
clinician electrophysiologist.
In this issue of the journal, Frontera and colleagues5present a detailed evaluation of morphology discriminators for three
device manufacturers, comparing the Far Field MD™ (Abbott Medical,
Abbott, Il, USA), RhythmID™ (Boston Scientific, Nattick, MA, USA) and
Wavelet™ (Medtronic, Minneapolis, MN, USA) algorithms. They compared SVT
and VT episodes from ICDs with an atrial lead placed obtained from their
respective remote monitoring platforms. After adjudicating the episodes
by independent review of intracardiac electrograms using the atrial
channel from dual chamber and cardiac resynchronization defibrillators,
they looked solely at the morphology discriminator algorithms and
determined the sensitivity and specificity for a range of template match
percentage values after constructing receiving operating characteristics
(ROC) curves. They found some statistically significant differences
between manufacturers in both sensitivity and specificity results at
default programming, most notably poor specificity for the RhythmID™ and
Wavelet™ algorithms compared to the Far Field™ morphology
discriminators. Perhaps more importantly, “optimal” settings
corresponding to the highest number of correctly classified episodes
could be achieved with programming changes. The Abbott algorithm default
is essentially already set to the optimal setting in this analysis, and
while further optimization could be achieved it falls outside of allowed
programming parameters. Significant improvements could be made with
programmable changes for the Boston Scientific and Medtronic devices
from default settings. The authors do report that these findings are
somewhat different from prior reports that are possibly related to study
design6-8. However, this study does amount to the
first investigation of morphology discriminators with real-world
episodes for three different vendors.
Firstly, the authors should be congratulated on their elegant work,
allowing the reader to gain real insight into morphology discrimination
and programming options for improving accuracy. Perhaps more
importantly, it brings to the light the importance of programming
considerations for the type of device and clinical setting. It is
important to realize that the results of this study are only applied to
the morphology discriminators and does not reflect real time therapy
using all potential components of any full device detection algorithms.
The authors themselves state that it would make sense to employ
different thresholds for morphology detection depending whether or not
the algorithm was functioning as a sole determinant to withhold therapy
in a single chamber ICD as compared to part of a decision tree as is the
case in dual chamber and cardiac resynchronization devices.
In order to strive for doing better, we should not be satisfied with
simply implanting another widget with no further thought given to what
we are doing when we prescribe this therapy. Sure, we are decreasing the
risk of sudden cardiac death by implanting a defibrillator for accepted
indications, but I think the relative ease of doing so and our relative
success demonstrated in our landmark clinical trials has allowed us to
overlook the dangers of inappropriate therapy and strive for what we can
do better. When it comes to avoiding inappropriate therapy, I would
remind everyone the first tenet of medicine: “primum non
nocere”.
References:
- Daubert JP, Zareba W, Cannom DS, McNitt S, Rosero SZ, Wang P, Schuger
C, Steinberg JS, Higgins SL, Wilber DJ, Klein H, Andrews ML, Hall WJ,
Moss AJ. Inappropriate Implantable Cardioverter-Defibrillator Shocks
in MADIT II. J. Am. Coll. Cardiol. 2008;51:1357–1365.
- Wilkoff BL, Fauchier L, Stiles MK, Morillo CA, Al-Khatib SM, Almendral
J, Aguinaga L, Berger RD, Cuesta A, Daubert JP, Dubner S, Ellenbogen
KA, Mark Estes NA, Fenelon G, Garcia FC, Gasparini M, Haines DE,
Healey JS, Hurtwitz JL, Keegan R, Kolb C, Kuck K-H, Marinskis G,
Martinelli M, McGuire M, Molina LG, Okumura K, Proclemer A, Russo AM,
Singh JP, Swerdlow CD, Teo WS, Uribe W, Viskin S, Wang C-C, Zhang S.
2015. HRS/EHRA/APHRS/SOLAECE expert consensus statement on optimal
implantable cardioverter-defibrillator programming and testing. Heart
Rhythm 2016;13:e50-86.
- Stiles MK, Fauchier L, Morillo CA, Wilkoff BL.
2019
HRS/EHRA/APHRS/LAHRS focused update to 2015 expert consensus statement
on optimal implantable cardioverter-defibrillator programming and
testing. J Arrhythm. 2019 May 14;35(3):485-493. doi:
10.1002/joa3.12178. eCollection 2019 Jun.PMID: 31293697.
- Sedláček K, Ruwald AC, Kutyifa V, McNitt S, Thomsen PEB, Klein H,
Stockburger M, Wichterle D, Merkely B, DE LA Concha JF, Swissa M,
Zareba W, Moss AJ, Kautzner J, Ruwald MH; MADIT-RIT Investigators. The
Effect of ICD Programming on Inappropriate and Appropriate ICD
Therapies in Ischemic and Nonischemic Cardiomyopathy: The MADIT-RIT
Trial. J Cardiovasc Electrophysiol, Vol. 26, pp. 424-433, April 2015)
- Frontera A, Strik M, Eschalier R, Biffi M, Pereira B, Weite N, Chauvel
R, Mondoly P, Laborderie J, Bernis JP, Clementy N, Reuter S, Garrigue
S, Deplagne A, Vernooy K, Pillois X, Haissaguerre M, Dubois R, Ritter
P, Bordachar P, Ploux S. Electrogram Morphology Discriminators in
Implantable Cardioverter Defibrillators: a comparative evaluation. J
of Cardiovasc Electrophysiol in press .
- Klein GJ, Gillberg JM, Tang A, Inbar S, Sharma A, Unterberg-Buchwald
C, Dorian P, Moore H, Duru F, Rooney E, Becker D, Schaaf K, Benditt D,
Worldwide Wave Investigators. Improving SVT discrimination in
single-chamber ICDs: a new electrogram morphology-based algorithm. J.
Cardiovasc. Electrophysiol. 2006;17:1310–1319.
- Theuns DAMJ, Rivero-Ayerza M, Goedhart DM, van der Perk R, Jordaens
LJ. Evaluation of morphology discrimination for ventricular
tachycardia diagnosis in implantable cardioverter-defibrillators.
Heart Rhythm 2006;3:1332–1338.
- Gold MR, Ahmad S, Browne K, Berg KC, Thackeray L, Berger RD.
Prospective comparison of discrimination algorithms to prevent
inappropriate ICD therapy: primary results of theRhythm ID Going Head
to Head Trial. Heart Rhythm 2012;9:370–377.