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JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
VOL. 65, NO. 24, 2015
ª 2015 BY THE AMERICAN COLLEGE OF CARDIOLOGY FOUNDATION
ISSN 0735-1097/$36.00
PUBLISHED BY ELSEVIER INC.
http://dx.doi.org/10.1016/j.jacc.2015.04.033
The Relationship Between Level of
Adherence to Automatic Wireless
Remote Monitoring and Survival in
Pacemaker and Defibrillator Patients
Niraj Varma, MD, PHD,* Jonathan P. Piccini, MD, MHSC,y Jeffery Snell, BA,z Avi Fischer, MD,z Nirav Dalal, MS,z
Suneet Mittal, MDx
ABSTRACT
BACKGROUND Remote monitoring (RM) technology embedded within cardiac rhythm devices permits continuous
monitoring, which may result in improved patient outcomes.
OBJECTIVES This study used “big data” to assess whether RM is associated with improved survival and whether this is
influenced by the type of cardiac device and/or its degree of use.
METHODS We studied 269,471 consecutive U.S. patients implanted between 2008 and 2011 with pacemakers
(PMs), implantable cardioverter-defibrillators (ICDs), or cardiac resynchronization therapy (CRT) with pacing capability
(CRT-P)/defibrillation capability (CRT-D) with wireless RM. We analyzed weekly use and all-cause survival for each device
type by the percentage of time in RM (%TRM) stratified by age. Socioeconomic influences on %TRM were assessed
using 8 census variables from 2012.
RESULTS The group had implanted PMs (n ¼ 115,076; 43%), ICDs (n ¼ 85,014; 32%), CRT-D (n ¼ 61,475; 23%),
and CRT-P (n ¼ 7,906; 3%). When considered together, 127,706 patients (47%) used RM, of whom 67,920 (53%)
had $75%TRM (high %TRM) and 59,786 (47%) <75%TRM (low %TRM); 141,765 (53%) never used RM (RM None).
RM use was not affected by age or sex, but demonstrated wide geographic and socioeconomic variability. Survival
was better in high %TRM versus RM None (hazard ratio [HR]: 2.10; p < 0.001), in high %TRM versus low %TRM
(HR: 1.32; p < 0.001), and also in low %TRM versus RM None (HR: 1.58; p < 0.001). The same relationship was observed
when assessed by individual device type.
CONCLUSIONS RM is associated with improved survival, irrespective of device type (including PMs), but demonstrates
a graded relationship with the level of adherence. The results support the increased application of RM to improve patient
outcomes. (J Am Coll Cardiol 2015;65:2601–10) © 2015 by the American College of Cardiology Foundation.
R
emote monitoring (RM) of patients with car-
a method for improving patient outcomes (2–8).
diac electronic implantable devices (CIEDs)
Newer technologies embedded in CIEDs permit daily
continues to evolve (1). Although originally
monitoring with automatic early notification of
devised to facilitate patient access and/or clinic
changes in patient’s clinical condition and device
efficiency by replacing the need for in-person
(mal)function (9). These notifications enable prompt
follow-up evaluations, RM is now being explored as
clinical
intervention,
irrespective
From the *Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio; yDuke Clinical Research Institute, Duke University
Medical Center, Duke University, Durham, North Carolina; zScientific Division, St. Jude Medical, Inc., Sylmar, California; and the
xDepartment of Electrophysiology, Valley Health System, New York, New York and Ridgewood, New Jersey. Dr. Varma has
received consulting fees/honoraria from St. Jude Medical, Boston Scientific, Sorin, Biotronik, and Medtronic. Dr. Piccini has
received research grants from ARCA Biopharma, Boston Scientific, Gilead, Janssen, ResMed, and St. Jude Medical; and is a
consultant for Bayer, ChanRx, JNJ, Medtronic, and Spectranetics. Drs. Fischer, Snell, and Dalal are employees of St. Jude Medical.
Dr. Mittal is a consultant for Boston Scientific, Medtronic, Sorin, and St. Jude Medical.
Listen to this manuscript’s audio summary by JACC Editor-in-Chief Dr. Valentin Fuster.
Manuscript received January 14, 2015; revised manuscript received March 23, 2015, accepted April 7, 2015.
of
follow-up
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Varma et al.
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Adherence to Remote Monitoring and Survival
ABBREVIATIONS
schedule (4,6,10). However, whether these
Death Master File. Age, sex, device type, and follow-
AND ACRONYMS
actions have a tangible effect on patient
up duration were ascertained using manufacturer
outcome remains an area of active investiga-
device tracking data. Remote monitoring status was
tion. First reports from studies using high-
determined from the Merlin patient care network
voltage CIEDs indicated improved survival
(St. Jude Medical) and date of death from the U.S.
among patients assigned to remote manage-
Social Security Death Master File, with all death re-
ment in both an observational cohort (ALTI-
cords through November 30, 2013. We added death
TUDE) (11) and the randomized IN-TIME
reports through this date made directly to the device
CI = confidence interval
CIED = cardiac electronic
implantable device
CRT = cardiac
resynchronization therapy
CRT-D = cardiac
resynchronization therapy with
(Influence of Home Monitoring on Mortality
manufacturer’s U.S. tracking system by health care
defibrillation capability
and Morbidity in Heart Failure Patients with
providers or family members (this accounted for <1%
CRT-P = cardiac
resynchronization therapy with
pacing capability
HR = hazard ratio
Impaired Left Ventricular Function) trial (5).
of deaths). Socioeconomic data were gathered from
Mechanisms remain unclear, but facilitation
the 2012 U.S. Census Bureau American Community
of ventricular arrhythmia/shock manage-
Survey, 2008 to 2012, by individual ZIP code tabula-
ment has been proposed as one explanation.
ICD = implantable
cardioverter-defibrillator
SEE PAGE 2611
MIR = mortality incidence rate
tion area, specifically, 4-year college degree, median
income, below poverty level, telephone or cell phone
service, employment status, health care insurance,
To better understand the influence of RM
and total urban/rural classification of population
on outcomes, we hypothesized that survival
counts (12). The urban percentage for a region was
would be better in patients with greater RM
computed as the ratio of urban to total population
use and should apply to all types of CIEDs:
counts. We obtained data without patient identifiers
patients with pacemakers (PMs) who have
from implant registration records of devices manu-
less cardiovascular risk as well as those with
factured by St. Jude Medical, Inc. Data included date
implantable cardioverter-defibrillators (ICDs)
of implantation, age at implantation, sex, patient ZIP
monitoring
and cardiac resynchronization therapy (CRT)
code, site ZIP code, and device model numbers. For
%TRM = percentage of time in
with pacing/defibrillation capability (CRT-P/
patients enrolled in the Merlin patient care network
remote monitoring
CRT-D). We tested this in a cohort of CIED
remote monitoring, we obtained data without patient
MIRR = mortality incidence
rate ratio
PM = pacemaker
RM = remote monitoring
RM None = never used
remote monitoring
TRM = time in remote
patients, all receiving automatic RM devices, by
identifiers consisting of maintenance transmission
leveraging “big data” from a nationwide RM system-
dates linked to implant registration data.
collects
Among RM-capable patients, RM service use was
comprehensive longitudinal follow-up data in hun-
computed using weekly status data sent from each
dreds of thousands of patients.
user of Merlin to the central server. A multiple-retry
generated
proprietary
database,
which
algorithm ensured the status data were communi-
METHODS
cated when an attempt to send data to the server
failed. Those patients having had at least 1 trans-
STUDY
DESIGN
AND
PATIENT
SELECTION. This
mission ever were classed as RM Any. RM adherence
retrospective, national, observational cohort study
per patient was defined as the proportion of total
evaluated 371,217 consecutive patients receiving new
follow-up weeks having at least 1 status transmission
implants of market-released PMs, ICDs, CRT-Ps, and
or percentage of time in RM (%TRM). To determine
CRT-Ds (St. Jude Medical, Inc., Sylmar, California). To
whether %TRM affected outcome, RM-capable pa-
assess the impact of RM use on outcome, patients
tients were assigned to 1 of 3 groups based on extent of
whose implanted device did not support automatic
their RM use. Those with 0%TRM were designated as
daily monitoring were excluded (deemed not auto-
RM None. RM Any patients were further divided into
matic RM capable) (Figure 1). The remaining patients
high %TRM or low %TRM groups by a cut point of 75%
with ICD/CRT-D devices implanted between October
use (this value approximated median %TRM) Thus,
2008 and December 2011 and PM/CRT-P devices
low %TRM patients were those sending weekly main-
implanted between October 2009 and December 2011
tenance records to the server <75% (but >0%) of their
comprised the study cohort (automatic RM capable).
follow-up time in this study, whereas high %TRM pa-
Patients enrolled in another clinical trial or with
tients were those who sent weekly maintenances re-
follow-up
time
<90
days
also
were
excluded.
cord to the server $75% of their follow-up time.
Included patients were followed until death or device
STATISTICAL ANALYSIS. The primary endpoint of
replacement/removal through November 2013.
this study was all-cause mortality, which was deter-
Study data were obtained from 4 sources: device
mined using unadjusted mortality incidence rates
implant registration, device RM, postal (ZIP) code
(MIRs) and adjusted survival via Cox proportional
sociodemographic data, and the U.S. Social Security
hazards survival models. The MIR ratio (MIRR), RM
Varma et al.
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Adherence to Remote Monitoring and Survival
Any/RM None, and 95% confidence intervals (CIs) were
determined from the patient deaths, and the follow-up
F I G U R E 1 Study Design
duration determined for patients in each group. Allcause survival was compared for each device type
CIEDs
N = 371,217
among patients with high %TRM, low %TRM, and RM
None using multivariable Cox proportional hazards
Not Automatic RM
Capable
N = 101,746
Source
modeling with stratification based on age and covariates of sex plus the RM predictor census variables.
Automatic RM Capable
N = 269,471
The Cox proportional hazard ratio (HR) and 95% CI
were determined. Length of follow-up was calculated
for each patient as the time from device implantation
until device explantation, replacement, death, or end
Cohort
ICD/CRT-D Implants
PM/CRT-P Implants
Implant Oct 2008
to Nov 2011
Implant Oct 2009
to Nov 2011
of study surveillance. To assess socioeconomic influences on %TRM, the 8 census variables were evaluated between high %TRM and low %TRM using
logistic regression and stepwise backward elimination
for p values <0.2. These variables were then used for
Time
adjustment in the Cox survival regression.
All
statistical
analyses
were
performed
with
Follow-up to Nov 2013
N = 269,471
Revolution R Enterprise 7.1.0 (Revolution Analytics,
Mountain View, California). Patient demographics
were assessed as mean SD, median (interquartile
RM Any
(%TRM > 0)
range), or n (%). The Student t test was used to
determine the p value for mean comparison and the
chi-square test for the p value for count.
RM
Adherence
High %TRM
N = 67,920
25%
To assess the geographic distribution of RM use
across the United States, a 2-dimensional clustering
Low %TRM
N = 59,786
22%
RM None
N = 141,765
53%
of RM Any patients was performed based on latitude
and longitude from the 3-digit ZIP code. For lowdensity regions, additional aggregation was performed using a nearest-neighbor method to combine
Outcomes
Mortality
adjacent ZIP codes until a minimum of 100 patients
per geographic grouping was obtained. The groups
were then merged, and the center of the combined
Only patients receiving devices embedded with the ability for automatic daily remote
group was determined as the weighted average of the
monitoring (RM) were evaluated (Automatic RM Capable), categorized by the degree of RM
latitude and longitude for the combined group. All
use: high %TRM ($75%), low %TRM (0% to 75%), and those who never used RM (RM
None). (Patients with devices not equipped with radiofrequency antennae and those using
resulting geographic groups were divided into tertiles
wanded telemetry were excluded [Not Automatic RM Capable]). %TRM ¼ percentage of
based on the mean %TRM in each group.
time in remote monitoring; CIEDs ¼ cardiac electronic implantable devices; CRT-D, cardiac
resynchronization therapy with defibrillation capability; CRT-P, cardiac resynchronization
RESULTS
therapy with pacing capability; ICD ¼ implantable cardioverter-defibrillator; PM ¼
pacemaker.
From the initial 371,217 patients, 101,746 whose devices were not RM capable were excluded (Figure 1).
The study cohort consisted of 269,471 automatic RM
time. Dichotomization by a 75% use value (close to the
capable patients (age, 71.0 13.5 years; 64.8% male)
median) divided RM Any into relatively balanced pa-
with a mean follow-up of 2.9 1.0 years (Table 1).
tient populations of high %TRM (n ¼ 67,920 [53.1%])
Missing ZIP code data accounted for <0.1% (1,694) of
and low %TRM (n ¼ 59,786 [46.9%]). Thus, high %TRM
patients for whom missing values were imputed from
comprised 25.2% (67,920 of 269,471) of all automatic
the median value for the state of residence.
RM capable patients. The median (interquartile range)
Overall, 141,765 (53%) patients with automatic RM-
time to initiation of RM from device implantation was
capable devices never used RM (RM None). Among
12 (4 to 33) weeks for RM Any, 33 (11 to 74) weeks for
those patients using RM (RM Any; n ¼ 127,706), dis-
low %TRM, and 6 (3 to 15) weeks for high %TRM.
tribution of use was skewed (Central Illustration, top),
MORTALITY AND SURVIVAL RESULTS. Overall, sur-
but 90,087 patients (70.6%) used RM $50% of the
vival was greater in those patients with some %TRM
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Adherence to Remote Monitoring and Survival
Figure 2 with HR and p value. Overall, outcomes were
T A B L E 1 Patient Demographic Characteristics
Parameter
Follow-up, yrs
superior in high %TRM and low %TRM compared
All
(N ¼ 269,471)
RM None*
(n ¼ 141,765)
RM Any*
(n ¼ 127,706)
p Value
2.9 1.0
2.8 1.1
3.0 1.0
<0.001
<0.001
with RM None for all device types including PMs.
Outcomes were also better in high %TRM compared
to low %TRM, except for CRT-P, likely due to the much
71.0 13.5
70.8 14.0
71.1 12.9
174,553 (64.8)
92,103 (65.0)
82,450 (64.6)
0.028
ICD
85,014 (31.6)
45,232 (31.9)
39,782 (31.2)
<0.001
groups) (Table 2), we anticipate that a larger study
PM
115,076 (42.7)
60,494 (42.7)
54,582 (42.7)
<0.001
population and/or longer follow-up may reveal a
CRT†
69,381 (25.8)
36,039 (25.4)
33,342 (26.1)
<0.001
significant difference between these 2 categories for
RM use, %
NA
NA
78.1 (41.6–92.9)
First RM transmission, weeks
NA
NA
12 (4–33)
SOCIOECONOMIC ANALYSIS. All 8 socioeconomic
variables linked by ZIP code to the patients in this
Age, yrs
Male
Remote monitoring
Last RM transmission, weeks
CRT-P.
NA
NA
1 (1–10)
Bachelor’s degree
26.2 15.1
26.1 15.1
26.3 15.1
0.023
Median income
54.6 21.8
54.3 22.1
54.9 21.4
<0.001
Below poverty line
14.1 8.4
14.6 8.8
13.4 7.8
<0.001
Have telephone
97.5 2.3
97.4 2.5
97.6 2.1
<0.001
1.1 1.1
1.2 1.1
1.1 1.1
<0.001
study were found to be statistically significant in
ZIP code–linked data‡
Receive SNAP
smaller number of patients studied. Because the trend
was directionally consistent with CRT-D (and other
Device type
predicting degree of RM use (high %TRM or low
%TRM), but the magnitude of the associations was
insubstantial. A landline phone or cell phone in the
home and completion of at least 4 years of college
Uninsured
14.6 7.5
15.2 7.9
13.9 6.9
<0.001
were positive predictors of RM use. Living below the
Residence: urban
76.3 33.4
79.4 21.5
72.4 35.1
<0.001
poverty line, lacking health insurance, unemployed,
Not in labor force
37.4 8.9
37.5 8.9
37.4 9.0
<0.001
not in the work force, lower median income, and
9.7 4.4
10.1 4.5
9.3 4.2
<0.001
living in an urban neighborhood predicted less
Unemployed
Values are mean SD, n (%), or median (interquartile range). *For some parameters, comparison between RM
Any and RM None yields differences that are very small in magnitude but statistically significant. This is due to
the huge number of patients in each group, for whom even a small difference between largely similar populations
becomes significant statistically. †CRT included CRT-D (n ¼ 61,475; 23% total) and CRT-P (n ¼ 7,906; 3% total)
devices. ‡All parameters in this section were measured as % in ZIP code except median income, which was
thousands of dollars in ZIP code.
CRT ¼ cardiac resynchronization therapy; CRT-D ¼ cardiac resynchronization therapy with defibrillation
capability; CRT-P ¼ cardiac resynchronization therapy with pacing capability; ICD ¼ implantable cardioverterdefibrillator; NA ¼ not available; PM ¼ pacemaker; RM ¼ remote monitoring; RM Any ¼ remote monitoring
used at least once; RM None ¼ no remote monitoring use; SNAP ¼ Supplemental Nutrition Assistance Program.
RM use (all p < 0.001). Neither age nor sex affected
RM use substantially (RM None vs. RM Any: 70.8 vs.
71.1 years; 65.0% female vs. 64.6% male). (Note that
the economic status and education of the specific
patients were not known: this was simply an analysis
of ZIP code–associated data).
The geographic distribution of %TRM is shown in
tertiles of use (Figure 3). The apparent scarcity of
patients in the High Plains and Intermountain West is
compared with those who failed to use RM at all
due to aggregation of data to maintain patient pri-
(Table 2). This relationship existed in all CIED cate-
vacy. There are fewer patients in that region, but they
gories, including PMs. The MIRR for RM Any versus
are more dispersed than suggested by this projection.
RM None was #0.55 across all CIED devices, demon-
There is wide geographic and socioeconomic vari-
strating that patients using RM have substantially
ability in the degree of RM use nationally, with a
decreased mortality. The Central Illustration shows
small but statistically significant bias toward rural
that for all devices, patients with high %TRM had a
residence for high %TRM patients.
lower MIR (MIR: 3,083 of 6,330 deaths per 100,000
patient-years; MIRR: 0.49) and greater survival than
DISCUSSION
RM None (adjusted HR: 2.10; 95% CI: 2.04 to 2.16;
p < 0.001). Significantly, patients with low %TRM also
In this nationwide comparative effectiveness study of
had lower mortality (MIR: 3,865 of 6,330; MIRR: 0.61)
RM use in more than 269,000 patients with implanted
and greater survival than patients with RM None
CIEDs, there are 3 main findings. First, RM use was
(adjusted HR: 1.58; 95% CI: 1.54 to 1.62; p < 0.001).
associated with improved survival. Second, the de-
Patients with high %TRM had lower mortality than
gree of adherence to remote management correlated
those with low %TRM (adjusted HR: 1.32; 95% CI: 1.27
strikingly with the magnitude of survival gain, sug-
to 1.36; p < 0.001). (These differences remained un-
gesting a gradient of effect. Thus, patients with high
changed whether 75% use or median [78.1%] was used
%TRM ($75%) exhibited the best survival, but those
as a cut point to split RM Any into high %TRM and low
with low %TRM still had markedly better survival
%TRM groups.) These observations indicate a gradient
compared with patients not using RM at all. For all
between the relationship of RM use and outcome.
devices, the MIR (per 100,000 patient-years) was
These relationships were explored further accord-
3,083 for high %TRM, 3,865 for low %TRM, and 6,330
ing to individual device types. Results are depicted in
for RM None (p < 0.001). Finally, the association
Varma et al.
JACC VOL. 65, NO. 24, 2015
JUNE 23, 2015:2601–10
CENTRAL I LLU ST RAT ION
Adherence to Remote Monitoring and Survival
Remote Monitoring Use and Impact
Varma, N. et al. J Am Coll Cardiol. 2015; 65(24):2601–10.
Remote monitoring (RM) technology embedded in cardiac rhythm devices enables continuous monitoring, but the degree of automaticity (i.e.,
requirement for active patient participation in using this service) varies. In this study, RM was not used in 53% of patients (RM None) (top).
Among those patients using RM at least once (RM Any), median RM use was 78.1% (range, 41.6 to 92.9). In this group, the number of patients
according to adherence level (%) was >0% to <25%, 20,796; $25% to <50%,16,823; $50% to <75%, 22,167; and $75% to #100%,
67,920. RM use was divided by a 75% cut point into high %TRM ($75% use) and low %TRM (<75% use). Greater RM use demonstrably
improved patient survival for all devices (bottom). In summary, patients who never used RM (RM None) and low %TRM accounted for 74.7%
of all patients; hence, only one-fourth of the U.S. population receiving an automatic RM-capable device maximize its usefulness.
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Adherence to Remote Monitoring and Survival
T A B L E 2 Mortality, MIR, and MIRR
MIR
(per 100,000 pt-yr)
n
Deaths
RM Any (%)
RM None
(95% CI)
RM Any
(95% CI)
All
269,471
38,130 (4.9)
47.4
6,329.9 (6,252.0–6,408.8)
3,457.0 (3,398.2–3,516.7)
0.55
PM
115,076
13,256 (4.2)
47.4
5,364.5 (5,252.8–5,478.6)
3,016.7 (2,930.7–3,105.3)
0.56
Device
CRT-P
MIRR
7,906
1,345 (6.6)
45.9
8,612.0 (8,070.1–9,190.9)
4,501.7 (4,099.3–4,944.2)
0.52
ICD
85,014
11,652 (4.5)
46.8
5,816.9 (5,689.5–5,947.1)
3,019.7 (2,925.4–3,117.00)
0.52
CRT-D
61,475
11,877 (6.6)
48.3
8,592.8 (8,402.3–8,787.7)
4,698.4 (4,559.1–4,842.0)
0.55
Values are n (% per pt-yr) unless otherwise indicated.
CI ¼ confidence interval; MIR ¼ mortality incidence rate; MIRR ¼ mortality incidence rate ratio; pt-yr ¼ patient-year; other abbreviations as in Table 1.
between RM and use persisted across the spectrum of
analysis exhibited the best survival among the CIED
patients receiving CIEDs, including CRT-D, ICD,
groups tested, with or without RM (Table 2). This
and, importantly, PMs. These associations were not
suggests that RM has advantages in patients regard-
altered substantively by age, sex, or socioeconomic
less of their susceptibility to ventricular arrhythmias
variations. Remarkably, only one-fourth of all patients
and/or shock therapies, which were considered factors
receiving automatic RM–capable CIEDs in this nation-
contributing to results in the ALTITUDE study. Iden-
wide analysis were in the high %TRM category, indi-
tification of atrial arrhythmias, high-rate episodes,
cating that the vast majority of recipients do not use
and changes in pacing and lead parameters may all
the full capabilities of their implantable devices.
represent potential actionable findings in patients
The ALTITUDE observational study in patients with
with PMs (9). Earlier intervention for these problems
ICDs and CRT-Ds reported improved survival in pa-
may lead to improved outcomes. This hypothesis was
tients assigned to remote management compared with
supported by the results of the COMPAS (Comparative
those without (11). Our results are important for not
Follow-up Schedule with Home Monitoring) random-
only confirming this association in a larger patient
ized clinical trial, although the study was not suffi-
cohort and with a separate proprietary remote tech-
ciently powered to evaluate survival (15).
nology, but also for extending this to analysis of PMs
The current results illustrate the critical impact of
and to testing the effect of differing levels of RM use.
adherence. To benefit from RM, patients (and pro-
Furthermore, an ALTITUDE substudy analysis recog-
viders) must use it. Earlier activation and then main-
nized that physician and hospital factors determined a
tenance of consistent transmissions were associated
lack of patient enrollment in RM. In these patients,
with the best outcomes. The demonstration of a
other practice constraints and lower adherence to
graded effect of RM use on outcome extends the value
other recommended treatments possibly may have
of an observational analysis beyond that of previous
contributed to poorer patient outcome (13). (A review
work that simply compared effects of RM on or off
of CIED follow-up practice among U.S. Medicare bene-
(12,13). In support of a direct RM effect, our results for
ficiaries from 2005 to 2008 revealed that patient
high %TRM parallel the degree of survival benefit
survival
with
noted among heart failure patients treated with ICDs
infrequent post-CIED follow-up [14]). Unlike the
and CRT randomized to RM with a different pro-
ALTITUDE study, we restricted our analysis to only
prietary technology (Home Monitoring, Biotronik,
patients who received devices capable of automatic
Berlin, Germany) when a consistently high (85%) level
RM, thereby eliminating at least 1 level of selection
of connection was ensured (5). Nevertheless, here we
was
diminished
among
patients
bias. Hence, our results showing the correlation of RM
demonstrated that patients maintaining some level
with survival gain and its modulation by degree of use
of connectivity, although gaining less benefit, still
are unlikely to be due to systematic differences in RM
derived some survival advantage compared with those
availability.
not using RM at all. This relationship is analogous to
An important discovery in our study is that the
the achievement of therapeutic anticoagulation in
strong association between RM and improved survival
patients with atrial fibrillation: %TRM may be as
extended to patients with PMs who typically do not
important for device patients as the time in thera-
have left ventricular dysfunction or heart failure.
peutic range is for patients with atrial fibrillation
Consistent with this “lower risk,” PM patients in our
taking warfarin.
Varma et al.
JACC VOL. 65, NO. 24, 2015
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Adherence to Remote Monitoring and Survival
F I G U R E 2 Kaplan-Meier Survival Curves
A
B
Pacemaker
1.00
Proportion Surviving
Proportion Surviving
1.00
ICD
0.95
0.90
0.85
0.80
0.75
0.70
High %TRM
Low %TRM
RM None
0.65
0.95
0.90
0.85
0.80
0.75
0.70
High %TRM
Low %TRM
RM None
0.65
0.60
0.60
0
1
4
2
3
Years from Implant
5
0
1
- - - Number at Risk - - High %TRM 31,652 30,843 28,227 12,170
Low %TRM 22,930 21,988 20,164 10,197
RM None 60,494 55,934 50,463 24,026
High %TRM 19,427 18,913 17,454 9,971 4,067
Low %TRM 20,355 19,530 18,094 12,057 5,761
RM None 45,232 41,196 36,847 23,050 10,140
D
CRT-P
CRT-D
1.00
Proportion Surviving
Proportion Surviving
354
709
1,211
- - - Cox Survival - - High %TRM vs. RM None HR: 2.24 [2.13–2.36], p<0.001
Low %TRM vs. RM None HR: 1.78 [1.69–1.87], p<0.001
High %TRM vs. Low %TRM HR: 1.26 [1.18–1.34], p<0.001
Mean follow-up: 3.07 (1.15) years
1.00
0.95
0.90
0.85
0.80
0.75
0.70
High %TRM
Low %TRM
RM None
0.65
0.95
0.90
0.85
0.80
0.75
0.70
High %TRM
Low %TRM
RM None
0.65
0.60
0.60
0
1
2
3
Years from Implant
4
5
- - - Number at Risk - - High %TRM
Low %TRM
RM None
5
- - - Number at Risk - - -
1,101
1,152
2,183
- - - Cox Survival - - High %TRM vs. RM None HR: 1.93 [1.84–2.02], p<0.001
HR: 1.45 [1.38–1.51], p<0.001
Low %TRM vs. RM None
High %TRM vs. Low %TRM HR: 1.31 [1.24–1.39], p<0.001
Mean follow-up: 2.73 (0.85) years
C
4
2
3
Years from Implant
1,991
1,634
4,281
1,918
1,552
3,776
1,710
1,398
3,288
631
611
1,244
47
45
101
- - - Cox Survival - - High %TRM vs. RM None HR: 1.82 [1.58–2.11], p<0.001
Low %TRM vs. RM None
HR: 1.79 [1.54–2.09], p<0.001
High %TRM vs. Low %TRM HR: 1.01 [0.83–1.22], p<0.929
Mean follow-up: 2.56 (0.89) years
0
1
4
2
3
Years from Implant
5
- - - Number at Risk - - High %TRM 14,850 14,423
Low %TRM 14,867 14,151
RM None 31,758 28,231
13,128 7,040
12,817 7,854
24,632 14,400
2,511
3,279
5,599
179
333
542
- - - Cox Survival - - High %TRM vs. RM None HR: 2.11 [2.00–2.22], p<0.001
Low %TRM vs. RM None HR: 1.64 [1.57–1.72], p<0.001
High %TRM vs. Low %TRM HR: 1.28 [1.20–1.36], p<0.001
Mean follow-up: 2.91 (1.14) years
High %TRM patients (orange line) consistently have higher survival curves compared with low %TRM (green line) and RM None patients
(blue line) for pacemakers (A), ICDs (B), CRT-P devices (C), and CRT-D devices (D). HR ¼ hazard ratio; other abbreviations as in Figure 1.
RM in isolation is not a treatment but a mechanism
responses are unavailable given the nature of the
for accessing important data regarding device func-
current study. Several interdependent cardiovascular
tion or incipient clinical conditions. Alerts that drive
factors (e.g., arrhythmias, shifts in right ventricular/
urgent in-person evaluation carry a high probability
biventricular pacing burden, vagal withdrawal, de-
of actionability for reprogramming and/or changes
creased patient activity) may change several days to
in drug therapy, either of which has the potential
weeks before clinical deterioration (16,17), permitting
to improve outcome (4,10). However, physician
provider intervention based on RM data upstream
2607
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Varma et al.
JACC VOL. 65, NO. 24, 2015
JUNE 23, 2015:2601–10
Adherence to Remote Monitoring and Survival
F I G U R E 3 U.S. Geographic RM Distribution
Geographic Distribution of Remote Monitoring
Continental United States
Alaska
Legend
Low Mean %TRM
Moderate Mean %TRM
Hawaii
High Mean %TRM
100 - 199
200 - 599
600 - 999
1,000 - 1,999
A clear geographic pattern emerges with regard to RM in the United States as seen both in tertiles of %TRM: high (green) (mean %TRM $72%),
moderate (yellow) (mean %TRM 21% to <72%), and low (red) (mean %TRM <21%), and according to the density of the RM population, from
high density (large circles ¼ population $2,000) to low density (small circles ¼ population <200). Abbreviations as in Figure 1.
of clinical symptoms. Optimized management of clin-
In this regard, our observations in low-voltage
ical conditions and/or device function may underlie
CIED categories are salient: a similar increment in
the survival advantage among remotely managed
survival gained by high adherence in both PM and
patients (5,16,17).
CRT-D patients supports an effect of RM use that is
Our observational study cannot confirm a di-
independent of the gravity of underlying cardiac
rect cause-and-effect relationship between RM and
disease and associated comorbidities. The similar
survival, although alignment of the described mor-
gain among different CIED categories also indicates
tality effect with that in a smaller numbers of patients
that the RM effect is independent of the degree of
managed remotely in randomized trials may point
programming versatility or therapeutic potential of
to such an effect (5). Association may be attributed
the CIED itself (greatest in CRT-D, least in PMs).
to a “healthy-user effect,” that is, patients who use
Notably,
RM more are less sick and more compliant in general
Routine Office Device Follow-up) trial demonstrated
and/or have physicians who are more up to date with
that use of RM itself facilitated patient compliance
recommended treatment. An ALTITUDE subanalysis
because randomization to RM promoted patient
indicated that both implantation of an RM-capable
engagement with follow-up services (18). A similar
device and patient activation were diminished in
effect was observed in follow-up clinics: randomiza-
patients with disadvantaged socioeconomic status
tion to RM improved patient retention to long-term
and/or greater comorbidities (13). However, account-
follow-up (18,19). Collectively, these actions (to
the
TRUST
(Lumos-T
Safely
RedUceS
ing for 17 such factors generated a modest area under
“induce” a positive behavioral change) may improve
the curve of only 0.62, meaning this “risk-treatment
initiation and maintenance of recommended treat-
paradox” was an incomplete explanation of the RM
ments (e.g., medications), extending effects beyond
effect.
device management. These actions may account for
Varma et al.
JACC VOL. 65, NO. 24, 2015
JUNE 23, 2015:2601–10
Adherence to Remote Monitoring and Survival
the graded relationship between RM use and survival
CONCLUSIONS
noted here. Clearly, the benefit of RM is multifactorial. This may explain why no single intervention
RM of patients with cardiovascular disease receiving
leading to a clear-cut mortality benefit was isolated
all types of CIEDs (including PMs) is associated with
in the IN-TIME trial. In this regard, our nationwide
improved all-cause survival, but maximal gain de-
“big data” analysis is more likely to discover and
pends on earlier implementation and consistent
confirm the total result of interconnected factors
adherence. Although our observational study cannot
than a randomized trial with a narrow field of view
determine a cause-and-effect relationship, the re-
(20,21).
striction of our analysis to only patients receiving
STUDY LIMITATIONS. Our results apply to implant-
wireless RM, the correlation with survival to the de-
able units enabled with automatic remote trans-
gree of use, and similar gains irrespective of device
mission technology and cannot be extended to other
type among patients with differing gravity of under-
remote management systems. In particular, non-
lying disease provide strong indirect evidence of an
implantable RM systems (characterized by modest
independent influence of RM on patient outcome.
adherence) have failed to improve patient outcomes
Our findings endorse recommendations advocating
(22). Causes of discontinuation or lost transmission in
the importance of post-implantation CIED follow-up,
the current study cannot be ascertained. Although
but also support extension of function from a peri-
socioeconomic factors significantly affected connec-
odic remote interrogation mechanism to a daily
tivity, the magnitude of this association was slight
monitoring system enabling improved outcome (2,7).
and insufficient to affect outcomes. Inclusion of
This result has a potential impact on millions of in-
earlier versions of RM technology demanding greater
dividuals with implanted devices worldwide.
patient participation are more vulnerable to trans-
ACKNOWLEDGMENT Data were provided by St. Jude
mission loss (23). Change of residence may account
Medical.
for some attrition (19). Our study period commenced
after publication of recommendations for CIED
REPRINT REQUESTS AND CORRESPONDENCE: Dr.
follow-up describing the role of RM as an adjunctive
Niraj Varma, Heart and Vascular Institute, J2-2,
mechanism to in-person evaluation, without advo-
Cleveland Clinic, 9500 Euclid Avenue, Cleveland,
cating for continuous monitoring functions (2). This
Ohio 44195. E-mail: [email protected].
may have contributed to variable connectivity among
our patients. Clinical profiles beyond age and sex
were unavailable. Demographic characteristics, med-
PERSPECTIVES
ications, etiology of heart failure and left ventricular
function, comorbidities, heart failure hospitaliza-
COMPETENCY IN PATIENT CARE: RM technology embedded
tions, and, importantly, individual responses to
in cardiac rhythm devices enables close follow-up of patients
remotely acquired data may all affect mortality. This
after implantation and is associated with improved clinical
study was not a randomized clinical trial and there-
outcomes proportionate to adherence to periodic data
fore cannot comment on efficacy. However, although
transmissions.
lacking access to detailed clinical data, this analysis
reports outcomes from consecutive patients in a
nationwide clinical practice, and, as such, the data
are generalizable as opposed to the highly controlled,
selected, and relatively small populations studied in
TRANSLATIONAL OUTLOOK: Understanding the mechanisms by which RM confers survival benefit and the factors
responsible for patient nonadherence to RM are important
objectives for future investigations.
clinical trials (20,21).
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