Effect of battery longevity on costs and health outcomes associated with cardiac implantable electronic devices: a Markov model-based Monte Carlo simulation
Effect of battery longevity on costs and health outcomes associated with cardiac implantable electronic devices: a Markov model-based Monte Carlo simulation
Jordana K. Schmier 0 1 2 4
Edmund C. Lau 0 1 2 4
Jasmine D. Patel 0 1 2 4
Juergen A. Klenk 0 1 2 4
Arnold J. Greenspon 0 1 2 4
0 Exponent, Inc. , Philadelphia, PA , USA
1 Exponent, Inc. , Menlo Park, CA , USA
2 Exponent, Inc. , 1800 Diagonal Road, Suite 500, Alexandria, VA 22314 , USA
3 Jordana K. Schmier
4 Thomas Jefferson University , Philadelphia, PA , USA
Introduction The effects of device and patient characteristics on health and economic outcomes in patients with cardiac implantable electronic devices (CIEDs) are unclear. Modeling can estimate costs and outcomes for patients with CIEDs under a variety of scenarios, varying battery longevity, comorbidities, and care settings. The objective of this analysis was to compare changes in patient outcomes and payer costs attributable to increases in battery life of implantable cardiac defibrillators (ICDs) and cardiac resynchronization therapy defibrillators (CRT-D). Methods and results We developed a Monte Carlo Markov model simulation to follow patients through primary implant, postoperative maintenance, generator replacement, and revision states. Patients were simulated in 3-month increments for 15 years or until death. Key variables included Charlson Comorbidity Index, CIED type, legacy versus extended battery longevity, mortality rates (procedure and all-cause), infection and non-infectious complication rates, and care settings. Costs included procedure-related (facility and professional), maintenance, and infections and non-infectious complications, all derived from Medicare data (2004-2014, 5% sample). Outcomes included counts of battery replacements, revisions, infections and non-infectious complications, and discounted (3%) costs and life years. An increase in battery longevity in ICDs yielded reductions in numbers of revisions (by 23%), battery changes (by 44%), infections (by 23%), non-infectious complications (by 10%), and total costs per patient (by 9%). Analogous reductions for CRT-Ds were 23% (revisions), 32% (battery changes), 22% (infections), 8% (complications), and 10% (costs). Conclusion Based on modeling results, as battery longevity increases, patients experience fewer adverse outcomes and healthcare costs are reduced. Understanding the magnitude of the cost benefit of extended battery life can inform budgeting and planning decisions by healthcare providers and insurers.
Battery life; Cardiac resynchronization therapy devices; Costs and cost analysis; Device battery replacement; Device longevity
The use of cardiac implantable electronic devices (CIEDs) has
been increasing in recent decades in the USA and worldwide
]. Both implantable cardiac defibrillators (ICDs) and
cardiac resynchronization therapy defibrillators (CRT-Ds) have
been associated with decreased mortality in patients with
cardiomyopathy and heart failure associated with conduction
delays . In addition, patients with a history of life-threatening
ventricular arrhythmias have improved survival with CIED
implantation compared to antiarrhythmic drug therapy [
The effectiveness of CIED implantation is tempered by the
risks associated with the procedure. These risks include
infections and non-infectious complications from the initial
implantation or subsequent revision procedure [
Infection rates appear to be increasing faster than the rate of
], only some of which can be explained
by factors such as gender, comorbidities, or procedure
]. It is well-understood that repeat procedures
place the patient at higher risk for developing CIED infection
6, 9, 15
]. Therefore, strategies to decrease the number of
repeat CIED procedures are needed.
Device selection can be driven by many factors, including
hospital policies and technology adoption practices [
and patient characteristics . There are also studies directly
comparing product performance and claims (e.g., von Gunten
et al. [
], Zanon et al. [
]). A focus of several of these
studies is on the increased longevity of selected CIEDs and
the clinical and economic benefits associated with an extended
device lifespan [
]. It stands to reason that longer device
life would be associated with fewer pulse generator
replacements and therefore costs should be lower, both because of
fewer surgeries and the lowered risk and costs associated with
surgical complications. There is also a tradeoff between
battery longevity and maintaining the most contemporary
technology in a given patient. In addition, patients who are
candidates for ICD or CRT therapy typically have a number of
significant medical comorbidities that affect their projected
survival. A pulse generator with prolonged battery life would
have little clinical value in a patient who is unlikely to survive
until the time of elective pulse generator replacement. The
case is not always clear-cut; however, clinicians need tools
to help them with decision-making. They must balance the
relative advantages and disadvantages of various features,
including size, shape, lead technologies, and battery longevity
as well as patient preferences. Yet, we are unaware of any
studies that have explicitly modeled the impact of increased
battery longevity on clinical outcomes and costs. Such models
may be useful tools to guide physicians in making these
clinical decisions with their patients.
The objective of this analysis was to compare changes in
patient outcomes and payer costs attributable to increases in
ICD and CRT-D battery life. The model characterizes
complications and costs associated with CIED-related complications
after a primary implantation. The model also includes a number
of patient comorbidities, which affect overall survival. Analyses
are specific to a patient cohort, a device type and battery length;
comparisons reflect differences in costs and event rates
experienced by patients for whom that device/battery was implanted.
2.1 Model structure
A state-transition (Markov) model was developed to estimate
downstream health effects and payer-perspective costs
following implantation of ICD and CRT-D devices. Model
states represent defined phases of care (primary implantation,
postoperative maintenance, revision, battery replacement) and
an absorbing state (dead). Patients are simulated one at a time
(i.e., at the patient level); patients face device
procedurerelated risks and mortality risks and accrue costs over a
defined time horizon.
Figure 1 illustrates the ways in which patients flow through
different paths in the model. The Markov states are indicated
with letters; arrows indicate paths and transitions. Each
transition has a probability associated with it; sources for
probability values are presented in Table 1. These values were
derived from an analysis of the Medicare data, using methods
described in the BInput Data^ section. Table 1 summarizes the
high and low values for each of the scenarios. For example,
the analysis identified the median and range of institutional
and professional costs for the first quarter for patients who
received an implant for inpatient and outpatient setting, for
both device types, and for each of the three health categories
(levels of the Charlson Comorbidity Index, 0–1, 2–3, and 4+).
Each of these combinations was used in separate runs of the
model, so that the model could be run only with inpatients
with an ICD who had a Charlson Comorbidity score of 0–1
or patients who received a CRT-D as an outpatient and had a
Charlson Comorbidity score greater than 4, for example.
Some of these analyses are presented in detail in this
manuscript; others are summarized. All patients enter the model at
the primary implantation state (Markov state A). Following
the implant, most patients proceed to the postoperative
maintenance state (Markov state B); a small percentage requires a
revision (Markov state D) in the next cycle. Most patients who
enter the postoperative maintenance state remain there for
multiple cycles. Battery replacements (Markov state C) can
only happen after one or more cycles of postoperative
maintenance, reflecting real-world patterns. Following a battery
replacement, patients return to the postoperative maintenance
state. Patients can remain in the same state for multiple cycles,
indicated by loops, such as the loops shown on states B and D.
BDead^ (Markov state E) is a terminal/absorbing state,
meaning that there is no exit once patients have entered the state.
The model time horizon is fixed at 15 years and each
cycle’s duration is one calendar quarter. An annual discount
rate of 3% was applied to both total costs and life years to
account for the net present value of healthcare costs. The
model was designed and implemented using TreeAge Pro
2016 (TreeAge Software, Inc., Williamstown, MA) with an
Excel graphic user interface available to control selected
Simulating at the patient level allows for heterogeneity
among patients and introduces history to the Markov states.
Random variation across patients was introduced by
simulating individual patients (Monte Carlo simulations) for each set
of model parameters. The number of simulated patients per
model run was 20,000, using a fixed seed to reduce individual
2.2 Input data
The primary source for model input data was de-identified
administrative claims data (known as the Limited Data Set
(LDS)) associated with the 5% sample of Medicare
beneficiaries available from the Centers for Medicare and Medicaid
Services (CMS). This 5% sample is equivalent to about 2.5
million Medicare beneficiaries. The LDS data files contain the
healthcare service records from inpatient and outpatient
encounters generated by these 2.5 million individuals. The data
consist of seven components: institutional claims (inpatient,
outpatient facility, durable medical equipment, home care,
hospice, and skilled nursing facility) and professional claims
(Part B). Beneficiaries in the 5% sample were systematically
drawn and represent the broad spectrum of Medicare enrollees
across age, gender, race, and geographic region. Each
beneficiary was assigned a synthetic identification number, which
allows longitudinal tracking of subsequent infection, revision,
and other complications following the initial implant.
Medicare claims were queried to identify beneficiaries with
primary ICD or CRT-D implantations between January 1,
2004, and December 31, 2014. Primary ICD and CRT-D
implantation was identified f rom Current Procedural
Terminology (CPT) 33249. The concurrent presence of the
CPT code 33225 suggested a CRT-D was implanted.
A full list of diagnosis and procedure codes that were used
to identify primary implantation, revisions, battery
replacements, and complications is provided in Supplemental
Table 1. Model inputs for the reference and sensitivity
analyses are provided in Table 1. Costs, transition rates between
states (e.g., operative and other cause mortality rates), and
event rates (e.g., rates of lead infection or dislodgement) were
derived from the 5% sample. The time period from which data
were derived varies. Transition rates and costs were derived
using the entire period for which data were available (i.e.,
2004–2014), although costs were inflated to January 2016
dollars. To reflect current trends in setting of care, the
distribution of inpatient versus outpatient settings was derived from
2012 to 2014 data.
2.2.1 Patient and claim identification
In the retrospective Medicare claims analysis from which data
were derived, beneficiaries entered the cohort continuously
during the study period, starting on January 1, 2004, and were
followed until the end of the study period (December 31,
2014), until they withdrew from Medicare or switched to a
Medicare fee-for-service plan or until death. Beneficiaries
were required to have been enrolled from January 1, 2003,
with no study procedures during that year, to increase the
likelihood that the first study procedure from 2004 forward
was the patient’s initial CIED implant procedure. The
beneficiary’s status was tracked using the matching 2004–
2014 Medicare enrollment files, which provide annual age,
resident state, entitlement status, date of death, and other
enrollment information. Beneficiaries younger than 65 years old,
those residing outside of the 50 US states, or those enrolled in
Medicare-HMO programs whose claims were not submitted
to CMS were excluded from this study. For each beneficiary
in the cohort, healthcare service claims starting from the initial
implant were extracted from the Medicare claims data,
including claims for hospital stays, outpatient clinic visits, physician
service, skilled nursing care, home health services, and
hospice services. The overall health status of the beneficiary at
time of the initial installation was characterized by the
Charlson Comorbidity Index (CCI), a tool that uses
comorbidities to describe wellness and to predict mortality and
future healthcare resource use. Diagnoses and surgical
procedures performed during this one-year pre-implant period were
compiled for the calculation of the CCI.
Beneficiaries with CIED-related infections, either local
pocket infection or systemic endovascular infection, were
identified by either the ICD-9-CM diagnosis code 996.61 or
any diagnosis code for cardiac device infection, fever,
bacteremia, endocarditis, cellulitis, or sepsis. These diagnoses were
required to be accompanied by a generator removal, system
revision, lead revision, or pocket revision procedure on the
same claim record in order to be identified as a device
procedure-related infection. In addition to infection, other
non-infection complications of interest include cardiac
perforation, pneumothorax, cardiac arrest, pulmonary embolus, and
hematoma. Supplemental Table 1 includes a full list of codes
used to identify each procedure and complication. We
calculated the quarterly risk of infection and non-infection
complications following the primary installation. In addition, we also
calculated the risk of infection and non-infection complication
following a battery revision and following other types of
revisions. Duration of battery life was classified as either
CCI Charlson Comorbidity Index, CMS Centers for Medicare and Medicaid Services, CRT-D cardiac resynchronization therapy implantable cardioverter
defibrillator, ICD implantable cardioverter defibrillator, N/A not applicable
a Stratification categories: devices (ICD or CRT-D); Charlson category (0–2, 3–4, or 5+); setting (inpatient or outpatient); component (institutional or
professional); surgical type (primary implant, generator change, or revision)
Blegacy,^ which used data derived from the historical CMS
claims data, or Bextended,^ for which data from a US-based
registry from a manufacturer of an extended battery longevity
CIED was used [
]. Although the model uses data for each
quarter directly, the time until 50% of ICDs required revision
was 30 quarters in Medicare data versus 52 quarters in the
LATITUDE registry and 22 quarters versus 40 quarters for
CRT-Ds in the Medicare and registry data, respectively. This
distinction allowed us to analyze the impact of device
2.2.2 Cost analysis
We adopted a third-party payer perspective for this study. Cost
is represented by the amount paid by the Medicare trust to
hospitals, physicians, and other institutions for the care of
these elderly cardiac patients. We calculated the institutional
costs (hospitals, clinics, nursing facilities, and hospice) as well
as professional costs from physicians and other medical
professionals. Quarterly costs, aggregated into two categories
(institutional and professional), were calculated for patients with
a non-zero cost. All costs were adjusted to the January 2016
level by the Consumer Price Index for medical care services
published monthly by the US Bureau of Labor Statistics. Part
D (drug costs) and costs that were the patient’s responsibility
to pay out of pocket were unavailable and were excluded. We
calculated cost of the primary installation, the cost associated
with infection and non-infection complications, as well as
quarterly Bmaintenance^ cost (CPT: 93289, 93282, 93283,
93284, 93295, 93296, 93287) associated with the monitoring
and periodic service of the CIED devices.
To capture the typical skew in costs and avoid assignment
of negative costs, we assumed costs (with some exceptions;
see Table 1) followed a lognormal distribution. Separate
professional and institutional costs (with some exceptions; see
Table 1) stratified by device and CCI category were drawn
every cycle and accrued to patients experiencing events.
2.3 Model assumptions
The complexity of the model required a number of
assumptions. These are provided in more detail in Supplemental
2.4 Scenarios and analyses
Setup of the model begins with specifying scenario variables.
The reference analysis requires the following parameter
Device type: ICD or CRT-D devices (run separately)
Battery lifetime: lifetimes based on CMS data (Blegacy^)
versus an Bextended^ lifetime (run separately)
Charlson Comorbidity Index: distribution of CCI based on
Mortality rates from procedures: Default values based on
Procedure settings: The inpatient/outpatient ratio for
primary implantation and revision based on CMS datasets
Cost data: based on CMS datasets, inflated to January
Sensitivity analyses examined include a sicker (average
higher CCI) population and an average of 20% more
outpatient procedures as well as discounting costs and outcomes at
rates of 6% and 0% annually to address the lack of consensus
on appropriate discounting rates for health economic studies
2.5 Model outputs
Patient-level event counts, costs, and life years are generated
for each model run, specific to a device and battery length.
Key outputs of interest include decreases in complications and
costs with increasing battery life and numbers of patients
requiring generator changes or revisions over the 15-year time
horizon (or remaining lifespan).
Among the ICD and CRT-D recipients identified in the CMS
database, mean age was 74.6 (standard deviation 6.1 years)
and 73.3% were male. Across all beneficiaries, approximately
one quarter had a CCI score of 2 or less and more than half had
a CCI of less than 5. There were no significant differences in
age, sex, or CCI score between ICD and CRT-D patients.
Patients receiving ICDs with a longer battery life were
more likely to have no generator changes (82.2 versus
68.1%), no infections (94.3 versus 92.6%), no non-infection
complications (91.2 versus 90.2%), and no revisions (94.1
versus 92.3%, Table 2) during the modeled period. The
relative reduction in each outcome is shown in Fig. 2. Patients
receiving CRT-Ds with a longer battery life experienced
similar additional benefits (Table 2). As expected, patients with
additional procedures incurred greater costs, as shown for the
ICD legacy scenario (Fig. 3); the same pattern was shown for
other devices and longevity options. A longer battery life was
associated with a decrease in total costs for patients with both
ICDs and CRT-Ds (Table 2) and practically equivalent life
years. Small increases in life years were observed with longer
battery life, but as they were consistently less than 1 month of
the 15-year model horizon and therefore less than one model
cycle, they are treated as equivalent. Patients receiving ICDs
with an extended battery had total costs reduced by
approximately $5288 (9%) compared to the legacy battery, while
patients receiving CRT-Ds with an extended battery had total
costs reduced by roughly $5981 (10%). Cost-effectiveness
ratios are not defined for extended battery strategies because
they are cost saving, that is, extended batteries are associated
with better (lower) costs and better (nominally increased) life
Sensitivity analyses both confirmed the model’s behavior
and provided additional insight. The following examples
reflect patients with ICDs, although similar patterns were
evident among CRT-D patients. For example, patients who were
sicker, operationalized as shifting the Charlson Comorbidity
Index, showed the same pattern, with costs for the existing
battery life greater than an extended battery life, but with
lower costs for each scenario to reflect earlier death. The total
discounted life years accrued were almost a full year less than
in the base case scenario. Patients who were healthier and had
lower CCI scores tended to live longer, but repeat procedure
rates were similar to the reference CCI distribution. This is
likely because living longer creates more opportunities for
the need for battery revisions. Sensitivity analyses also
explored the impact of having 20% more implant and revision
procedures performed in outpatient settings. (All battery
changes were assumed to be performed in outpatient settings.)
There was no difference in life years accumulated, as the
model did not assign different mortality rates based on
procedure setting, but costs for the legacy and extended battery
scenarios were approximately $2600 less than the base case
setting distribution. Figure 4 shows costs when using the
observed proportion of outpatient implant and revision
procedures for the legacy and extended battery scenarios for
CRTD devices using CMS data on care setting compared to
increased use of outpatient care (increasing, for example, the
percent of initial ICD implants performed in outpatient
settings from 56 to 76%). This more accurately reflects recent
trends in care settings. Increasing the ratio of outpatient to
inpatient procedures lowered costs by 4–5% over the modeled
Other sensitivity analyses show the effect of changing input
variables. Figure 5 demonstrates how changing the discount
rate affects total costs with extended longevity ICD devices.
As shown, there is a small but expected decrease with a higher
discount rate and an increase with no discount rate. It is not
surprising that the discount rate only changes costs slightly;
most of the cost for each patient is typically in the first cycle,
with the initial implant.
This analysis found that increases in battery life substantially
reduce costs and certain unwanted events (infections,
generator changes, revisions) and produce equivalent or minimal
gains in life expectancy. Extended battery life was the
dominant strategy for all examined scenarios. While this finding is
consistent with expectations, we believe that presenting
empiric evidence of the value of increased battery life can serve
multiple purposes. First, the typical evidence stream from
randomized controlled trials does not exist for battery
components, yet some alternative evidence is often required to
demonstrate value. Second, understanding the shift in costs that
would be associated with extended battery life can be used for
budgeting and planning. Finally, while understanding cost
savings associated with longer battery life is valuable for
estimating budget impact of CIED use for insurers, estimating
repeat hospitalizations can also have value to facilities and
healthcare systems in planning inpatient care. For these
reasons, understanding the magnitude of the cost benefit of
extended battery life warrants analysis rather than assumptions.
The UK’s National Institute for Health and Care Excellence
(NICE) has already considered this question; it developed and
issued guidance on its support for a CRT-D with extended
battery life based on published evidence [
]. The USA
currently employs a fee-for-service approach to healthcare, which
predominantly rewards for volume of care. As the USA
explores alternative payment models with greater emphasis on
Table 2 Clinical and cost results,
percentages of patients with no
adverse events or repeat
procedures, costs, and life years
Costs and life years discounted at 3% annually
CRT-D cardiac resynchronization therapy implantable cardioverter defibrillator, ICD implantable cardioverter
defibrillator, SD standard deviation
quality outcomes and value of care, it will be particularly
important to provide evidence on the economic value of
medical therapies and their differentiating properties.
Pulse generator replacements due to battery depletion have
typically occurred between 4 and 6 years after ICD
]. The problem of battery longevity is not new; over
the years, multiple factors have contributed to the
longstanding observation that battery life is insufficient for the
post-implantation survival [
] including trends toward
miniaturization, earlier use, and broadening of indications [
Further complicating these factors is the pace of innovation.
ICD patients may be upgraded to different or more advanced
devices before batteries fail or without the presence of a
complication. Patients who received ICD upgrades rather than
revisions or generator replacements were not excluded from
the present analysis. Later review determined that they
comprised less than 4% of ICD Brevision^ procedures and no
adjustments were made. Dual-chamber devices may be
associated with higher short-term complication rates [
and it is possible that patients who received upgrades may
be clinically different than others, but there were insufficient
patients to test in this population.
Repeated revision procedures have also been identified as a
risk factor for CIED complications, in particular, infection [
]. Unfortunately, the rate of CIED infection appears to be
increasing out of proportion to overall device utilization
]. Infection rates may be influenced by multiple factors,
including patient comorbidities, but also by many other
factors that are not detectable with a claims database [
refinement of the model could permit incorporating some of
the factors, although there are practical limits to what factors
can be considered based on the data set and counts of patients
in relevant categories. While reducing the need for revisions
would not eliminate all infections, as a primary driver of
infection, any reduction in revisions would be noteworthy.
From a clinical perspective, longer battery life with consistent
performance seems like an obvious step forward. Payers must
weigh clinical benefits against budget implications and are
increasingly cognizant of cost-effectiveness of treatment and
longterm patient outcomes. Hospitals have even more of a stake in
patient success including reducing readmissions than in previous
years, as competition for demonstrating quality through
improved patient outcomes converges with penalties for
unnecessary readmissions. Studies like the model presented here can
provide value to multiple audiences. They can help third-party
payers consider coverage, assist hospitals in predicting caseload
(revisions versus implantations), and potentially help patients
who may have a co-pay evaluate how they may benefit from
any increased cost to which they may be subject based on
battery and device type. Still, it is not logical to cover a device that
will long outlast a patient’s lifetime; models like this one can
help explore in what scenarios increased battery longevity has
the greatest impact on different populations.
This model was designed to allow the user to account for
changes in the setting of implant and revision procedures,
which can have a substantial impact on costs, and across levels
of patient health, as measured by the Charlson Comorbidity
Index. The Charlson Comorbidity Index used for stratifying
patients did not explicitly account for patient age, but alternative
comorbidity indices could be explored, which would permit
correlation of patient age to event rates and mortality risks, as
well as costs. In particular, while the age of patients is
constrained by the use of the Medicare claims data, there are
other clinical factors for which we cannot account using an
administrative claims database. Specifically, we do not know
about the frequency of pacing or ICD shocks, which could offer
substantial insight into battery performance. The administrative
nature of the database means that our ability to identify
comorbidities and other health-related characteristics is limited to
those for which a claim and diagnosis code are present.
Although claims have been adjudicated by the time the dataset
is available for analysis, the coding may not always be
complete. For example, it is possible that there are codes that are not
listed because there is no additional reimbursement for them.
This analysis, consistent with most claims analyses, assumes
that all relevant codes have been submitted as part of each
claim. Other factors that were not included in this model may
affect outcomes. For example, patient demographic and clinical
characteristics, activity levels, battery consumption, and device
characteristics may affect outcomes but are not available in a
claims database. The model can be modified to include
additional parameters as more information becomes available.
Similarly, although the model only attributes infections that
occur in the first year following a generator change, the model’s
framework could be used to consider infections that occur over
shorter or longer follow-up periods. The one-year window has
been used in other CIED claims analyses [
]; longer time
periods create greater uncertainty about attribution.
Another important limitation to consider is that this model
used data from Medicare beneficiaries age 65 and older. In fact,
the Medicare population from whom model input parameters
were drawn averaged 74 years old. Selecting only the youngest
Medicare beneficiaries with CIEDs would not have provided
sufficient counts. Older patients may have fewer years in
which to benefit from the CIED, so limiting the input
parameters to data from older patients might constrain variation in
life expectancy and thus minimize benefits. Recent analyses
suggest that the average age at implant of an ICD in the USA is
67 ± 13 years, with one fourth of new implants in patients
59 years or younger [
]. Data from younger patients, who
could also have fewer comorbidities, could reasonably
demonstrate a greater benefit from the use of devices with longer
battery lives. Alternatively, these patients receiving CIEDs
earlier might be less healthy than those who do not require a
device until they are older. Younger patients might be more
likely to be in the workforce and have reduced work
productivity associated with negative outcomes related to battery life.
Another concern about the use of the Medicare population
for the input parameters and Blegacy^ battery longevity is that
the population in the registry that supplied the Bextended^
longevity data may differ. It is likely that the Medicare
population is more heterogeneous, but the possibility of younger
patients in the registry may also affect heterogeneity. It is
unclear how much overlap there may be between the
Medicare population and the sponsor-initiated registry, as well
as whether there are other differences between patients who
participate in registries and those who do not.
The model is conservative in estimating the impact of
implantations and revisions. It does not include either quality of
life or societal costs or incorporate patient preferences, which
suggest that patients value longevity over size [
Caregiving associated with implantable cardiac devices has
been shown to have an impact on the well-being of patients’
partners that could translate to an increased health care burden
among caregivers [
]. Further, loss of work or other types
of indirect costs associated with battery replacements and
related adverse events can only increase total economic burden.
Based on modeling results, as battery longevity increases,
patients experience fewer adverse outcomes and healthcare
costs are reduced.
Acknowledgements We would like to thank Pamela McMahon, Ph.D.,
for her work in developing and implementing the initial model.
Author contributions All authors participated in developing the study
concept and design. EL conducted much of the data analysis, with JS
incorporating the data into the decision tree that had been designed by
JK. JS, JP, and EL drafted the manuscript, with JK and AG providing
critical revision. All authors provided final review and approval. Funding
for the work was secured by JP and JS.FundingBoston Scientific
Corporation funded this study. Boston Scientific did not participate in
the design and conduct of the study, in the collection, analysis, and
interpretation of the data, with the exception of making available registry data
on battery longevity that have only been partially published, and in the
preparation, review, or approval of the manuscript, with the exception of
non-binding comments for style and a legal review.
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