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The Relationship Between Level ofAdherence to Automatic WirelessRemote Monitoring and Survival inPacemaker and Defibrillator Patients

Niraj Varma, MD, PHD,* Jonathan P. Piccini, MD, MHSC,y Jeffery Snell, BA,z Avi Fischer, MD,z Nirav Dalal, MS,zSuneet Mittal, MDx

ABSTRACT

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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-diac electronic implantable devices (CIEDs)continues to evolve (1). Although originally

devised to facilitate patient access and/or clinicefficiency by replacing the need for in-personfollow-up evaluations, RM is now being explored as

m the *Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio;

dical Center, Duke University, Durham, North Carolina; zScientific Divisio

epartment of Electrophysiology, Valley Health System, New York, New

eived consulting fees/honoraria from St. Jude Medical, Boston Scientifi

eived research grants from ARCA Biopharma, Boston Scientific, Gilead

sultant for Bayer, ChanRx, JNJ, Medtronic, and Spectranetics. Drs. Fische

. Mittal is a consultant for Boston Scientific, Medtronic, Sorin, and St. Jud

ten to this manuscript’s audio summary by JACC Editor-in-Chief Dr. Vale

nuscript received January 14, 2015; revised manuscript received March 2

a method for improving patient outcomes (2–8).Newer technologies embedded in CIEDs permit dailymonitoring with automatic early notification ofchanges in patient’s clinical condition and device(mal)function (9). These notifications enable promptclinical intervention, irrespective of follow-up

yDuke Clinical Research Institute, Duke University

n, St. Jude Medical, Inc., Sylmar, California; and the

York and Ridgewood, New Jersey. Dr. Varma has

c, Sorin, Biotronik, and Medtronic. Dr. Piccini has

, Janssen, ResMed, and St. Jude Medical; and is a

r, Snell, and Dalal are employees of St. Jude Medical.

e Medical.

ntin Fuster.

3, 2015, accepted April 7, 2015.

ABBR EV I A T I ON S

AND ACRONYMS

CI = confidence interval

CIED = cardiac electronic

implantable device

CRT = cardiac

resynchronization therapy

CRT-D = cardiac

resynchronization therapy with

defibrillation capability

CRT-P = cardiac

resynchronization therapy with

pacing capability

HR = hazard ratio

ICD = implantable

cardioverter-defibrillator

MIR = mortality incidence rate

MIRR = mortality incidence

rate ratio

PM = pacemaker

RM = remote monitoring

RM None = never used

remote monitoring

TRM = time in remote

monitoring

%TRM = percentage of time in

remote monitoring

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Adherence to Remote Monitoring and Survival J U N E 2 3 , 2 0 1 5 : 2 6 0 1 – 1 0

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schedule (4,6,10). However, whether theseactions have a tangible effect on patientoutcome remains an area of active investiga-tion. First reports from studies using high-voltage CIEDs indicated improved survivalamong patients assigned to remote manage-ment in both an observational cohort (ALTI-TUDE) (11) and the randomized IN-TIME(Influence of Home Monitoring on Mortalityand Morbidity in Heart Failure Patients withImpaired Left Ventricular Function) trial (5).Mechanisms remain unclear, but facilitationof ventricular arrhythmia/shock manage-ment has been proposed as one explanation.

SEE PAGE 2611

To better understand the influence of RMon outcomes, we hypothesized that survivalwould be better in patients with greater RMuse and should apply to all types of CIEDs:patients with pacemakers (PMs) who haveless cardiovascular risk as well as those withimplantable cardioverter-defibrillators (ICDs)and cardiac resynchronization therapy (CRT)with pacing/defibrillation capability (CRT-P/CRT-D). We tested this in a cohort of CIED

patients, all receiving automatic RM devices, byleveraging “big data” from a nationwide RM system-generated proprietary database, which collectscomprehensive longitudinal follow-up data in hun-dreds of thousands of patients.

METHODS

STUDY DESIGN AND PATIENT SELECTION. Thisretrospective, national, observational cohort studyevaluated 371,217 consecutive patients receiving newimplants of market-released PMs, ICDs, CRT-Ps, andCRT-Ds (St. Jude Medical, Inc., Sylmar, California). Toassess the impact of RM use on outcome, patientswhose implanted device did not support automaticdaily monitoring were excluded (deemed not auto-matic RM capable) (Figure 1). The remaining patientswith ICD/CRT-D devices implanted between October2008 and December 2011 and PM/CRT-P devicesimplanted between October 2009 and December 2011comprised the study cohort (automatic RM capable).Patients enrolled in another clinical trial or withfollow-up time <90 days also were excluded.Included patients were followed until death or devicereplacement/removal through November 2013.

Study data were obtained from 4 sources: deviceimplant registration, device RM, postal (ZIP) codesociodemographic data, and the U.S. Social Security

Death Master File. Age, sex, device type, and follow-up duration were ascertained using manufacturerdevice tracking data. Remote monitoring status wasdetermined from the Merlin patient care network(St. Jude Medical) and date of death from the U.S.Social Security Death Master File, with all death re-cords through November 30, 2013. We added deathreports through this date made directly to the devicemanufacturer’s U.S. tracking system by health careproviders or family members (this accounted for <1%of deaths). Socioeconomic data were gathered fromthe 2012 U.S. Census Bureau American CommunitySurvey, 2008 to 2012, by individual ZIP code tabula-tion area, specifically, 4-year college degree, medianincome, below poverty level, telephone or cell phoneservice, employment status, health care insurance,and total urban/rural classification of populationcounts (12). The urban percentage for a region wascomputed as the ratio of urban to total populationcounts. We obtained data without patient identifiersfrom implant registration records of devices manu-factured by St. Jude Medical, Inc. Data included dateof implantation, age at implantation, sex, patient ZIPcode, site ZIP code, and device model numbers. Forpatients enrolled in the Merlin patient care networkremote monitoring, we obtained data without patientidentifiers consisting of maintenance transmissiondates linked to implant registration data.

Among RM-capable patients, RM service use wascomputed using weekly status data sent from eachuser of Merlin to the central server. A multiple-retryalgorithm ensured the status data were communi-cated when an attempt to send data to the serverfailed. Those patients having had at least 1 trans-mission ever were classed as RM Any. RM adherenceper patient was defined as the proportion of totalfollow-up weeks having at least 1 status transmissionor percentage of time in RM (%TRM). To determinewhether %TRM affected outcome, RM-capable pa-tients were assigned to 1 of 3 groups based on extent oftheir RM use. Those with 0%TRM were designated asRM None. RM Any patients were further divided intohigh %TRM or low %TRM groups by a cut point of 75%use (this value approximated median %TRM) Thus,low %TRM patients were those sending weekly main-tenance records to the server <75% (but >0%) of theirfollow-up time in this study, whereas high %TRM pa-tients were those who sent weekly maintenances re-cord to the server $75% of their follow-up time.STATISTICAL ANALYSIS. The primary endpoint ofthis study was all-cause mortality, which was deter-mined using unadjusted mortality incidence rates(MIRs) and adjusted survival via Cox proportionalhazards survival models. The MIR ratio (MIRR), RM

FIGURE 1 Study Design

CIEDsN = 371,217

Source

Cohort

Time

RMAdherence

Outcomes

Automatic RM CapableN = 269,471

ICD/CRT-D Implants

Implant Oct 2008to Nov 2011

Follow-up to Nov 2013N = 269,471

RM Any(%TRM > 0)

High %TRMN = 67,920

25%

Low %TRMN = 59,786

22%

Mortality

RM NoneN = 141,765

53%

PM/CRT-P Implants

Implant Oct 2009to Nov 2011

Not Automatic RMCapable

N = 101,746

Only patients receiving devices embedded with the ability for automatic daily remote

monitoring (RM) were evaluated (Automatic RM Capable), categorized by the degree of RM

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

wanded telemetry were excluded [Not Automatic RM Capable]). %TRM ¼ percentage of

time in remote monitoring; CIEDs ¼ cardiac electronic implantable devices; CRT-D, cardiac

resynchronization therapy with defibrillation capability; CRT-P, cardiac resynchronization

therapy with pacing capability; ICD ¼ implantable cardioverter-defibrillator; PM ¼pacemaker.

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Any/RMNone, and 95% confidence intervals (CIs) weredetermined from the patient deaths, and the follow-upduration determined for patients in each group. All-cause survival was compared for each device typeamong patients with high %TRM, low %TRM, and RMNone using multivariable Cox proportional hazardsmodeling with stratification based on age and cova-riates of sex plus the RM predictor census variables.The Cox proportional hazard ratio (HR) and 95% CIwere determined. Length of follow-up was calculatedfor each patient as the time from device implantationuntil device explantation, replacement, death, or endof study surveillance. To assess socioeconomic in-fluences on %TRM, the 8 census variables were eval-uated between high %TRM and low %TRM usinglogistic regression and stepwise backward eliminationfor p values <0.2. These variables were then used foradjustment in the Cox survival regression.

All statistical analyses were performed withRevolution R Enterprise 7.1.0 (Revolution Analytics,Mountain View, California). Patient demographicswere assessed as mean � SD, median (interquartilerange), or n (%). The Student t test was used todetermine the p value for mean comparison and thechi-square test for the p value for count.

To assess the geographic distribution of RM useacross the United States, a 2-dimensional clusteringof RM Any patients was performed based on latitudeand longitude from the 3-digit ZIP code. For low-density regions, additional aggregation was per-formed using a nearest-neighbor method to combineadjacent ZIP codes until a minimum of 100 patientsper geographic grouping was obtained. The groupswere then merged, and the center of the combinedgroup was determined as the weighted average of thelatitude and longitude for the combined group. Allresulting geographic groups were divided into tertilesbased on the mean %TRM in each group.

RESULTS

From the initial 371,217 patients, 101,746 whose de-vices were not RM capable were excluded (Figure 1).The study cohort consisted of 269,471 automatic RMcapable patients (age, 71.0 � 13.5 years; 64.8% male)with a mean follow-up of 2.9 � 1.0 years (Table 1).Missing ZIP code data accounted for <0.1% (1,694) ofpatients for whom missing values were imputed fromthe median value for the state of residence.

Overall, 141,765 (53%) patients with automatic RM-capable devices never used RM (RM None). Amongthose patients using RM (RM Any; n ¼ 127,706), dis-tribution of use was skewed (Central Illustration, top),but 90,087 patients (70.6%) used RM $50% of the

time. Dichotomization by a 75% use value (close to themedian) divided RM Any into relatively balanced pa-tient populations of high %TRM (n ¼ 67,920 [53.1%])and low%TRM (n¼ 59,786 [46.9%]). Thus, high %TRMcomprised 25.2% (67,920 of 269,471) of all automaticRM capable patients. The median (interquartile range)time to initiation of RM from device implantation was12 (4 to 33) weeks for RM Any, 33 (11 to 74) weeks forlow %TRM, and 6 (3 to 15) weeks for high %TRM.

MORTALITY AND SURVIVAL RESULTS. Overall, sur-vival was greater in those patients with some %TRM

TABLE 1 Patient Demographic Characteristics

ParameterAll

(N ¼ 269,471)RM None*

(n ¼ 141,765)RM Any*

(n ¼ 127,706) p Value

Follow-up, yrs 2.9 � 1.0 2.8 � 1.1 3.0 � 1.0 <0.001

Age, yrs 71.0 � 13.5 70.8 � 14.0 71.1 � 12.9 <0.001

Male 174,553 (64.8) 92,103 (65.0) 82,450 (64.6) 0.028

Device type

ICD 85,014 (31.6) 45,232 (31.9) 39,782 (31.2) <0.001

PM 115,076 (42.7) 60,494 (42.7) 54,582 (42.7) <0.001

CRT† 69,381 (25.8) 36,039 (25.4) 33,342 (26.1) <0.001

Remote monitoring

RM use, % NA NA 78.1 (41.6–92.9)

First RM transmission, weeks NA NA 12 (4–33)

Last RM transmission, weeks NA NA 1 (1–10)

ZIP code–linked data‡

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

Receive SNAP 1.1 � 1.1 1.2 � 1.1 1.1 � 1.1 <0.001

Uninsured 14.6 � 7.5 15.2 � 7.9 13.9 � 6.9 <0.001

Residence: urban 76.3 � 33.4 79.4 � 21.5 72.4 � 35.1 <0.001

Not in labor force 37.4 � 8.9 37.5 � 8.9 37.4 � 9.0 <0.001

Unemployed 9.7 � 4.4 10.1 � 4.5 9.3 � 4.2 <0.001

Values are mean � SD, n (%), or median (interquartile range). *For some parameters, comparison between RMAny and RM None yields differences that are very small in magnitude but statistically significant. This is due tothe huge number of patients in each group, for whom even a small difference between largely similar populationsbecomes 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 wasthousands of dollars in ZIP code.

CRT ¼ cardiac resynchronization therapy; CRT-D ¼ cardiac resynchronization therapy with defibrillationcapability; CRT-P ¼ cardiac resynchronization therapy with pacing capability; ICD ¼ implantable cardioverter-defibrillator; NA ¼ not available; PM ¼ pacemaker; RM ¼ remote monitoring; RM Any ¼ remote monitoringused at least once; RM None ¼ no remote monitoring use; SNAP ¼ Supplemental Nutrition Assistance Program.

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compared with those who failed to use RM at all(Table 2). This relationship existed in all CIED cate-gories, including PMs. The MIRR for RM Any versusRM None was #0.55 across all CIED devices, demon-strating that patients using RM have substantiallydecreased mortality. The Central Illustration showsthat for all devices, patients with high %TRM had alower MIR (MIR: 3,083 of 6,330 deaths per 100,000patient-years; MIRR: 0.49) and greater survival thanRM None (adjusted HR: 2.10; 95% CI: 2.04 to 2.16;p < 0.001). Significantly, patients with low %TRM alsohad lower mortality (MIR: 3,865 of 6,330; MIRR: 0.61)and greater survival than patients with RM None(adjusted HR: 1.58; 95% CI: 1.54 to 1.62; p < 0.001).Patients with high %TRM had lower mortality thanthose with low %TRM (adjusted HR: 1.32; 95% CI: 1.27to 1.36; p < 0.001). (These differences remained un-changed whether 75% use or median [78.1%] was usedas a cut point to split RM Any into high %TRM and low%TRM groups.) These observations indicate a gradientbetween the relationship of RM use and outcome.

These relationships were explored further accord-ing to individual device types. Results are depicted in

Figure 2 with HR and p value. Overall, outcomes weresuperior in high %TRM and low %TRM comparedwith RM None for all device types including PMs.Outcomes were also better in high %TRM comparedto low%TRM, except for CRT-P, likely due to the muchsmaller number of patients studied. Because the trendwas directionally consistent with CRT-D (and othergroups) (Table 2), we anticipate that a larger studypopulation and/or longer follow-up may reveal asignificant difference between these 2 categories forCRT-P.

SOCIOECONOMIC ANALYSIS. All 8 socioeconomicvariables linked by ZIP code to the patients in thisstudy were found to be statistically significant inpredicting degree of RM use (high %TRM or low%TRM), but the magnitude of the associations wasinsubstantial. A landline phone or cell phone in thehome and completion of at least 4 years of collegewere positive predictors of RM use. Living below thepoverty line, lacking health insurance, unemployed,not in the work force, lower median income, andliving in an urban neighborhood predicted lessRM use (all p < 0.001). Neither age nor sex affectedRM use substantially (RM None vs. RM Any: 70.8 vs.71.1 years; 65.0% female vs. 64.6% male). (Note thatthe economic status and education of the specificpatients were not known: this was simply an analysisof ZIP code–associated data).

The geographic distribution of %TRM is shown intertiles of use (Figure 3). The apparent scarcity ofpatients in the High Plains and Intermountain West isdue to aggregation of data to maintain patient pri-vacy. There are fewer patients in that region, but theyare more dispersed than suggested by this projection.There is wide geographic and socioeconomic vari-ability in the degree of RM use nationally, with asmall but statistically significant bias toward ruralresidence for high %TRM patients.

DISCUSSION

In this nationwide comparative effectiveness study ofRM use in more than 269,000 patients with implantedCIEDs, there are 3 main findings. First, RM use wasassociated with improved survival. Second, the de-gree of adherence to remote management correlatedstrikingly with the magnitude of survival gain, sug-gesting a gradient of effect. Thus, patients with high%TRM ($75%) exhibited the best survival, but thosewith low %TRM still had markedly better survivalcompared with patients not using RM at all. For alldevices, the MIR (per 100,000 patient-years) was3,083 for high %TRM, 3,865 for low %TRM, and 6,330for RM None (p < 0.001). Finally, the association

CENTRAL ILLUSTRATION 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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TABLE 2 Mortality, MIR, and MIRR

Device n Deaths RM Any (%)

MIR(per 100,000 pt-yr)

RM None(95% CI)

RM Any(95% CI) MIRR

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

CRT-P 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.

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between RM and use persisted across the spectrum ofpatients receiving CIEDs, including CRT-D, ICD,and, importantly, PMs. These associations were notaltered substantively by age, sex, or socioeconomicvariations. Remarkably, only one-fourth of all patientsreceiving automatic RM–capable CIEDs in this nation-wide analysis were in the high %TRM category, indi-cating that the vast majority of recipients do not usethe full capabilities of their implantable devices.

The ALTITUDE observational study in patients withICDs and CRT-Ds reported improved survival in pa-tients assigned to remote management compared withthose without (11). Our results are important for notonly confirming this association in a larger patientcohort and with a separate proprietary remote tech-nology, but also for extending this to analysis of PMsand to testing the effect of differing levels of RM use.Furthermore, an ALTITUDE substudy analysis recog-nized that physician and hospital factors determined alack of patient enrollment in RM. In these patients,other practice constraints and lower adherence toother recommended treatments possibly may havecontributed to poorer patient outcome (13). (A reviewof CIED follow-up practice among U.S. Medicare bene-ficiaries from 2005 to 2008 revealed that patientsurvival was diminished among patients withinfrequent post-CIED follow-up [14]). Unlike theALTITUDE study, we restricted our analysis to onlypatients who received devices capable of automaticRM, thereby eliminating at least 1 level of selectionbias. Hence, our results showing the correlation of RMwith survival gain and its modulation by degree of useare unlikely to be due to systematic differences in RMavailability.

An important discovery in our study is that thestrong association between RM and improved survivalextended to patients with PMs who typically do nothave left ventricular dysfunction or heart failure.Consistent with this “lower risk,” PM patients in our

analysis exhibited the best survival among the CIEDgroups tested, with or without RM (Table 2). Thissuggests that RM has advantages in patients regard-less of their susceptibility to ventricular arrhythmiasand/or shock therapies, which were considered factorscontributing to results in the ALTITUDE study. Iden-tification of atrial arrhythmias, high-rate episodes,and changes in pacing and lead parameters may allrepresent potential actionable findings in patientswith PMs (9). Earlier intervention for these problemsmay lead to improved outcomes. This hypothesis wassupported by the results of the COMPAS (ComparativeFollow-up Schedule with Home Monitoring) random-ized clinical trial, although the study was not suffi-ciently powered to evaluate survival (15).

The current results illustrate the critical impact ofadherence. To benefit from RM, patients (and pro-viders) must use it. Earlier activation and then main-tenance of consistent transmissions were associatedwith the best outcomes. The demonstration of agraded effect of RM use on outcome extends the valueof an observational analysis beyond that of previouswork that simply compared effects of RM on or off(12,13). In support of a direct RM effect, our results forhigh %TRM parallel the degree of survival benefitnoted among heart failure patients treated with ICDsand CRT randomized to RM with a different pro-prietary technology (Home Monitoring, Biotronik,Berlin, Germany) when a consistently high (85%) levelof connection was ensured (5). Nevertheless, here wedemonstrated that patients maintaining some levelof connectivity, although gaining less benefit, stillderived some survival advantage comparedwith thosenot using RM at all. This relationship is analogous tothe achievement of therapeutic anticoagulation inpatients with atrial fibrillation: %TRM may be asimportant for device patients as the time in thera-peutic range is for patients with atrial fibrillationtaking warfarin.

FIGURE 2 Kaplan-Meier Survival Curves

1.000.950.900.850.800.750.700.650.60

0 1 2 3Years from Implant

PacemakerA

CRT-PC

ICDB

Prop

ortio

n Su

rviv

ing

4 5

1.000.950.900.850.800.750.700.650.60

0

High %TRM 31,652

1,9911,6344,281 3,776 3,288 1,244 101

1,552 1,398 611 451,918 1,710 631 47

19,427

14,85014,86731,758 28,231 24,632 14,400 5,599 542

14,151 12,817 7,854 3,279 33314,423 13,128 7,040 2,511 179

20,355 19,530 18,094 12,057 5,761 70945,232 41,196 36,847 23,050 10,140 1,211

18,913 17,454 9,971 4,067 35422,93060,494 55,934 50,463 24,026 2,183

21,988 20,164 10,197 1,15230,843 28,227 12,170

- - - Number at Risk - - -

- - - Number at Risk - - - - - - Number at Risk - - -

- - - Number at Risk - - -

- - - Cox Survival - - -

- - - Cox Survival - - - - - - Cox Survival - - -

- - - Cox Survival - - -

1,101Low %TRM

RM None

High %TRMLow %TRM

RM None

High %TRMLow %TRM

RM None

High %TRMLow %TRM

RM None

High %TRM vs. RM None HR: 1.93 [1.84–2.02], p<0.001HR: 1.45 [1.38–1.51], p<0.001HR: 1.31 [1.24–1.39], p<0.001

HR: 1.82 [1.58–2.11], p<0.001HR: 1.79 [1.54–2.09], p<0.001HR: 1.01 [0.83–1.22], p<0.929

HR: 2.24 [2.13–2.36], p<0.001HR: 1.78 [1.69–1.87], p<0.001HR: 1.26 [1.18–1.34], p<0.001

Low %TRM vs. RM NoneHigh %TRM vs. Low %TRM

High %TRM vs. RM NoneLow %TRM vs. RM NoneHigh %TRM vs. Low %TRM

High %TRM vs. RM NoneLow %TRM vs. RM NoneHigh %TRM vs. Low %TRM

Mean follow-up: 2.73 (0.85) years

Mean follow-up: 2.56 (0.89) years

Mean follow-up: 3.07 (1.15) years

HR: 2.11 [2.00–2.22], p<0.001HR: 1.64 [1.57–1.72], p<0.001HR: 1.28 [1.20–1.36], p<0.001

High %TRM vs. RM NoneLow %TRM vs. RM NoneHigh %TRM vs. Low %TRMMean follow-up: 2.91 (1.14) years

1 2 3Years from Implant

Prop

ortio

n Su

rviv

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4 5

CRT-DD1.000.950.900.850.800.750.700.650.60

0 1 2 3Years from Implant

Prop

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ing

4 5

1.000.950.900.850.800.750.700.650.60

0 1 2 3Years from Implant

Prop

ortio

n Su

rviv

ing

4 5

High %TRMLow %TRMRM None

High %TRMLow %TRMRM None

High %TRMLow %TRMRM None

High %TRMLow %TRMRM None

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.

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RM in isolation is not a treatment but a mechanismfor accessing important data regarding device func-tion or incipient clinical conditions. Alerts that driveurgent in-person evaluation carry a high probabilityof actionability for reprogramming and/or changesin drug therapy, either of which has the potentialto improve outcome (4,10). However, physician

responses are unavailable given the nature of thecurrent study. Several interdependent cardiovascularfactors (e.g., arrhythmias, shifts in right ventricular/biventricular pacing burden, vagal withdrawal, de-creased patient activity) may change several days toweeks before clinical deterioration (16,17), permittingprovider intervention based on RM data upstream

FIGURE 3 U.S. Geographic RM Distribution

Continental United States

Legend

Low Mean %TRMModerate Mean %TRMHigh Mean %TRM

100 - 199200 - 599600 - 9991,000 - 1,999

Hawaii

Alaska

Geographic Distribution of Remote Monitoring

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.

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of clinical symptoms. Optimized management of clin-ical conditions and/or device function may underliethe survival advantage among remotely managedpatients (5,16,17).

Our observational study cannot confirm a di-rect cause-and-effect relationship between RM andsurvival, although alignment of the described mor-tality effect with that in a smaller numbers of patientsmanaged remotely in randomized trials may pointto such an effect (5). Association may be attributedto a “healthy-user effect,” that is, patients who useRM more are less sick and more compliant in generaland/or have physicians who are more up to date withrecommended treatment. An ALTITUDE subanalysisindicated that both implantation of an RM-capabledevice and patient activation were diminished inpatients with disadvantaged socioeconomic statusand/or greater comorbidities (13). However, account-ing for 17 such factors generated a modest area underthe curve of only 0.62, meaning this “risk-treatmentparadox” was an incomplete explanation of the RMeffect.

In this regard, our observations in low-voltageCIED categories are salient: a similar increment insurvival gained by high adherence in both PM andCRT-D patients supports an effect of RM use that isindependent of the gravity of underlying cardiacdisease and associated comorbidities. The similargain among different CIED categories also indicatesthat the RM effect is independent of the degree ofprogramming versatility or therapeutic potential ofthe CIED itself (greatest in CRT-D, least in PMs).Notably, the TRUST (Lumos-T Safely RedUceSRoutine Office Device Follow-up) trial demonstratedthat use of RM itself facilitated patient compliancebecause randomization to RM promoted patientengagement with follow-up services (18). A similareffect was observed in follow-up clinics: randomiza-tion to RM improved patient retention to long-termfollow-up (18,19). Collectively, these actions (to“induce” a positive behavioral change) may improveinitiation and maintenance of recommended treat-ments (e.g., medications), extending effects beyonddevice management. These actions may account for

PERSPECTIVES

COMPETENCY IN PATIENT CARE: RM technology embedded

in cardiac rhythm devices enables close follow-up of patients

after implantation and is associated with improved clinical

outcomes proportionate to adherence to periodic data

transmissions.

TRANSLATIONAL OUTLOOK: Understanding the mecha-

nisms by which RM confers survival benefit and the factors

responsible for patient nonadherence to RM are important

objectives for future investigations.

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the graded relationship between RM use and survivalnoted here. Clearly, the benefit of RM is multifacto-rial. This may explain why no single interventionleading to a clear-cut mortality benefit was isolatedin the IN-TIME trial. In this regard, our nationwide“big data” analysis is more likely to discover andconfirm the total result of interconnected factorsthan a randomized trial with a narrow field of view(20,21).

STUDY LIMITATIONS. Our results apply to implant-able units enabled with automatic remote trans-mission technology and cannot be extended to otherremote management systems. In particular, non-implantable RM systems (characterized by modestadherence) have failed to improve patient outcomes(22). Causes of discontinuation or lost transmission inthe current study cannot be ascertained. Althoughsocioeconomic factors significantly affected connec-tivity, the magnitude of this association was slightand insufficient to affect outcomes. Inclusion ofearlier versions of RM technology demanding greaterpatient participation are more vulnerable to trans-mission loss (23). Change of residence may accountfor some attrition (19). Our study period commencedafter publication of recommendations for CIEDfollow-up describing the role of RM as an adjunctivemechanism to in-person evaluation, without advo-cating for continuous monitoring functions (2). Thismay have contributed to variable connectivity amongour patients. Clinical profiles beyond age and sexwere unavailable. Demographic characteristics, med-ications, etiology of heart failure and left ventricularfunction, comorbidities, heart failure hospitaliza-tions, and, importantly, individual responses toremotely acquired data may all affect mortality. Thisstudy was not a randomized clinical trial and there-fore cannot comment on efficacy. However, althoughlacking access to detailed clinical data, this analysisreports outcomes from consecutive patients in anationwide clinical practice, and, as such, the dataare generalizable as opposed to the highly controlled,selected, and relatively small populations studied inclinical trials (20,21).

CONCLUSIONS

RM of patients with cardiovascular disease receivingall types of CIEDs (including PMs) is associated withimproved all-cause survival, but maximal gain de-pends on earlier implementation and consistentadherence. Although our observational study cannotdetermine a cause-and-effect relationship, the re-striction of our analysis to only patients receivingwireless RM, the correlation with survival to the de-gree of use, and similar gains irrespective of devicetype among patients with differing gravity of under-lying disease provide strong indirect evidence of anindependent influence of RM on patient outcome.Our findings endorse recommendations advocatingthe importance of post-implantation CIED follow-up,but also support extension of function from a peri-odic remote interrogation mechanism to a dailymonitoring system enabling improved outcome (2,7).This result has a potential impact on millions of in-dividuals with implanted devices worldwide.

ACKNOWLEDGMENT Data were provided by St. JudeMedical.

REPRINT REQUESTS AND CORRESPONDENCE: Dr.Niraj Varma, Heart and Vascular Institute, J2-2,Cleveland Clinic, 9500 Euclid Avenue, Cleveland,Ohio 44195. E-mail: varman@ccf.org.

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2. Wilkoff BL, Auricchio A, Brugada J, et al. HRS/EHRA expert consensus on the monitoring ofcardiovascular implantable electronic devices(CIEDs): description of techniques, indications,personnel, frequency and ethical considerations.Heart Rhythm 2008;5:907–25.

3. Schoenfeld MH, Compton SJ, Mead RH,et al. Remote monitoring of implantablecardioverter defibrillators: a prospective anal-ysis. Pacing Clin Electrophysiol 2004;27:757–63.

4. Varma N, Epstein A, Irimpen A, et al. Efficacyand safety of automatic remote monitoring for ICDFollow-Up: the TRUST trial. Circulation 2010;122:325–32.

5. Hindricks G, Taborsky M, Glikson M, et al.Implant-based multiparameter telemonitoringof patients with heart failure (IN-TIME): a ran-domised controlled trial. Lancet 2014;384:583–90.

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at: http://factfinder2.census.gov. AccessedNovember 25, 2014.

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15. Mabo P, Victor F, Bazin P, et al. A randomizedtrial of long-term remote monitoring of pace-maker recipients (the COMPAS trial). Eur Heart J2012;33:1105–11.

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17. Whellan DJ, Ousdigian KT, Al-Khatib SM, et al.Combined heart failure device diagnostics identifypatients at higher risk of subsequent heart failurehospitalizations: results from PARTNERS HF(Program to Access and Review Trending Infor-mation and Evaluate Correlation to Symptoms inPatients With Heart Failure) study. J Am CollCardiol 2010;55:1803–10.

18. Varma N, Michalski J. Home monitored ICDpatients are more loyal to follow up-the paradox

of remote patient management in the TRUST trial(abstr). Eur Heart J 2013;34:263.

19. Varma N, Michalski J, Stambler B, Pavri BB, forthe TRUST Investigators. Superiority of automaticremote monitoring compared with in-personevaluation for scheduled ICD follow-up in theTRUST trial-testing execution of the recommen-dations. Eur Heart J 2014;35:1345–52.

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22. Chaudhry SI, Mattera JA, Curtis JP, et al. Tel-emonitoring in patients with heart failure. N Engl JMed 2010;363:2301–9.

23. Cronin E, Ching EA, Varma N, Martin DO,Wilkoff B, Lindsay BD. Remote monitoring of car-diovascular devices- a time and activity analysis.Heart Rhythm 2012;9:1947–51.

KEY WORDS big data, cardiac electronicimplantable devices, cardiac resynchronizationtherapy, device, mortality, survival,time in remote monitoring