TY - JOUR
AU - TRUST Investigators
AB - Abstract Aims Benefits of automatic remote home monitoring (HM) among implantable cardioverter defibrillator (ICD) patients may require high transmission frequency. However, transmission reliability and effects on battery longevity remain uncertain. We hypothesized that HM would have high transmission success permitting punctual guideline based follow-up, and improve battery longevity. This was tested in the prospective randomized TRUST trial. Methods and results Implantable cardioverter defibrillator patients were randomized post-implant 2:1 to HM (n = 908) (transmit daily) or to Conventional in-person monitoring [conventional management (CM), n = 431 (HM disabled)]. In both groups, five evaluations were scheduled every 3 months for 15 months. Home Monitoring technology performance was assessed by transmissions received vs. total possible, and number of scheduled HM checks failing because of missed transmissions. Battery longevity was compared in HM vs. CM at 15 months, and again in HM 3 years post-implant using continuously transmitted data. Transmission success per patient was 91% (median follow-up of 434 days). Overall, daily HM transmissions were received in 315 795 of a potential 363 450 days (87%). Only 55/3759 (1.46%) of unsuccessful scheduled evaluations in HM were attributed to transmission loss. Shock frequency and pacing percentage were similar in HM vs. CM. Fifteen month battery longevity was 12% greater in HM (93.2 ± 8.8% vs. 83.5 ± 6.0% CM, P < 0.001). In extended follow-up of HM patients, estimated battery longevity was 50.9 ± 9.1% (median 52%) at 36 months. Conclusion Automatic remote HM demonstrated robust transmission reliability. Daily transmission load may be sustained without reducing battery longevity. Home Monitoring conserves battery longevity and tracks long term device performance. Clinical trial registration ClinicalTrials.gov; NCT00336284. Statements and guidelines , Implantable cardioverter defibrillator , Patient monitoring , Follow-up , Remote monitoring , Battery , Transmission reliability What’s new? Benefits of remote ICD patient management may require high transmission frequency, but associated transmission reliability and effects on battery longevity remain uncertain. Here, in a large randomized trial, automatic remote monitoring: Demonstrated robust transmission reliability. Conserved battery longevity despite high frequency transmission load. Introduction Remote patient management is becoming the preferred method of post-implant follow-up of patients receiving cardiac implantable electronic devices (CIEDs).1,2 This has been driven by the evolution of remote technology from patient-activated inductive systems to automatic wireless systems, for which transmission frequency may be set from three monthly intervals (for remote interrogation) to daily (for ‘continuous‘ remote monitoring), according to clinical need and physician preference, and proprietary technology.3 Higher frequency has been associated with improved patient outcomes, but concern exists that this accelerates battery drain.4,5 However, neither successful delivery of programmed transmissions, nor effect of transmission frequency on device longevity, has been well characterized. The TRUST (The Lumos-T Safely Reduces Routine Office Device Follow-Up) trial compared remote patient management using automatic home monitoring (HM) to conventional care (CM) during post-implant follow-up of ICD patients. Primary trial results have been reported previously. These demonstrated safety and efficacy of HM based remote management, with the added advantages of reducing the load of device clinic in-person evaluations (IPE) yet improving patient retention.6–8 In TRUST, HM was activated promptly post-implant in patients randomized to remote management and programmed to daily transmissions. Here, we assessed both transmission reliability of daily transmissions and their impact on battery longevity during extended follow-up. Methods The objectives of the current post-hoc analysis were to assess success of daily transmission and their impact on ICD battery longevity in the TRUST trial. TRUST was an investigator-initiated prospective multi-centre randomized clinical trial designed by a steering committee consisting of physicians (also serving as investigators) in collaboration with the sponsor. The protocol was written by the principal investigator and sponsor. All hypotheses and data queries were initiated by the principal investigator without sponsor involvement. The study complied with the declaration of Helsinki i.e. the research protocol was approved by the locally appointed ethics committee at all participating sites, and that the informed consent of the subjects was obtained. All enrolled patients received devices embedded with a single proprietary remote monitoring technology (‘Home Monitoring‘, Biotronik). Home monitoring is based on an embedded antenna within the pulse generator which transmits stored diagnostic data to a bedside, or mobile, communicator for relay telephonically (cellular and/or landline) to a service centre for automatic processing and online review. The system automatically (i.e. without patient or clinic participation) attempts daily periodic transmissions and additional alert notifications for out of bounds parameters. The devices utilized in the TRUST trial were the first to wirelessly transmit intracardiac electrograms as well as stored data (Figure 1). Figure 1 Open in new tabDownload slide Example of wirelessly transmitted electrogram event notification. Figure 1 Open in new tabDownload slide Example of wirelessly transmitted electrogram event notification. TRUST tested safety and efficacy of HM for remote patient management by assessing success of three-monthly scheduled evaluations and early detection of interim system problems or arrhythmias.6 In the trial protocol, recipients of single and dual chamber ICDs with HM implanted for Class I/II indications (but not pacemaker dependent) were randomized post-implant 2:1 to either HM (transmission enabled at enrollment and remote transmitter provided) or to conventional management (CM) with remote monitoring disabled. Follow-up checks were scheduled at 3, 6, 9, 12, and 15 months post-implant in both groups (evaluations within 30 days of these appointments were considered to fall in the ‘scheduled’ window). Conventional patients were assessed with in-person visits only. HM patients were assessed remotely at identical time points. Three and 15 month checks in HM were followed by in-person visits, maintaining the first post implant office visit and the minimum yearly face to face examinations according to recommendations. Six, nine, and 12 month checks were remotely accessed, and thus dependent on successful data delivery. Prespecified requirements were: physician attestation that HM derived data were sufficient for clinical management decisions; documentation of transmission failure as a cause for unsuccessful HM evaluation, and the time taken for its correction; and reprogramming changes (increases in pacing outputs >1V and/or increase in pulse width, changes in VT/SVT algorithm/settings or of lowest tachycardia rate boundary for the purpose of preventing inappropriate shocks and delivering appropriate shocks). Transmission reliability of the remote technology was assessed by (i) the number of days during which remote transmissions were successfully transmitted (measured by the HM service centre) per patient relative to total transmission days possible, i.e. daily until study exit, and (ii) the proportion of remote only evaluations that failed due to lack of refreshed data. In these, the duration of lapsed transmissions, and the time taken to establish reconnection, was measured. The impact of transmissions on battery longevity was performed in two stages. Firstly, device estimated percentage battery life remaining was compared between patients randomized to HM on vs. CM patients without activated HM. The cohort included only patients who were assessed at the 15 month time point (i.e. excluding those who exited earlier and/or failed to adhere to their 15 month in person appointment. The follow-up adherence for the complete TRUST study cohort has been reported previously.7 For this battery analysis cohort, the total scheduled follow-up visit compliance per patient with HM was 95.8 ± 9.9% vs. 92.9 ± 13.6% in CM (P = 0.003). Battery status and shock history were compared in these groups from their respective HM transmissions (HM group) or in office interrogation (CM group). Atrial and ventricular pacing percentage data were collected from HM transmissions and therefore only from patients with activated remote monitoring (whether HM or CM group patients) after 15 months. The 15 month battery status was limited to the first calendar month transmission prior to, but within 30 days, of the completed 15 month follow-up. This excluded some 15 month battery data if both the first HM transmission of the month occurred after the 15 month visit date and the first HM transmission in the month prior was >30 days prior to the 15 month date. Direct comparison of estimated battery longevity at 15 months between patients randomized to HM vs. CM was made accounting for shock history and estimated pacing burden using a linear regression model. Both single and dual chamber ICDs were implanted and device models (with slightly different battery technologies) changed during the course of the trial, reflecting availability of newer models (initial Lumos to newer Lumax series) with potentially different capacities for maintaining daily HM transmissions. Therefore, battery capacities for these were assessed separately. For both cohorts the device serial number, the implantation date, the explant date as well as the patient death date were known. In the second part of the analysis, the effect on battery life was tracked through 36 months post implant in all available HM cohort subjects, irrespective of whether they followed up in-person at 15 months at study exit, using data from the first remote transmission of each month. Values were derived from the HM database. Analysis and statistics An independent Clinical Events Committee comprising three physicians not participating in the trial and blinded to investigational sites, patient identities and randomization assignment, adjudicated all adverse events. The committee also adjudicated disputed classifications of actionability of events, including reprogramming changes. Continuous variables were summarized as means and standard deviations, unless otherwise noted. Categorical variables were summarized in frequency distributions. Group differences were compared with Student t-tests. A probability value of 0.05 was considered evidence of statistical significance. Effect of HM on battery longevity was determined. Mean value comparisons were made using a linear regression model including pacing percentage, shock-incidence (shocks per patient year), and model type as independent covariates. For the primary analysis, mean battery percentage at 15 months was compared between HM on and off groups. To do this, a linear regression model was fitted using mean battery percentage at 15 months as the dependent variable and device type, shock-incidence, and pacing percentage (over the 15 months period) as independent covariates. By including pacing percentage and shock-incidence in the model as independent covariates, any confounding effects of pacing percentage or shock-incidence in the battery percentage comparison are accounted for. Additionally, mean pacing percentage and shock-incidence was compared between HM groups to check for any biases not eliminated by randomization. Results One hundred and two US study sites enrolled 1450 patients from November 2005 to February 2008. Data were analysed in patients having at least one follow-up: 908/977 (92.9%) in HM vs. 431/473 (91.1%) in CM (P = 0.25). Home monitoring and CM patient populations were similar though ischaemic heart disease was slightly more prevalent in CM (Table 1). Almost three-quarters of enrolled patients had a primary prophylactic indication for ICD implant. During the course of the trial, 556 (61.2%) patients in HM and 288 (66.8%) of CM patients had no VT/VF events. Following trial termination at the 15 month study visit, 42.9% of CM patients adopted remote management, and only 2.1% of HM patients (14/660) adopted conventional in person follow-up (Figure 2). Table 1 Patient characteristics Parameter . Home monitoring . Conventional monitoring . P-value . n = 908 . n = 431 . Implant to enrolment (days) 15.9 ± 12.8 15.8 ± 12.7 days 0.93 Age (years) 63.3 ± 12.8 64.0 ± 12.1 0.365 Male gender 72.0 73.1 0.695 Primary prevention (%) 72.2 73.8 0.599 LVEF (%) 29.0 ± 10.7 28.5 ± 9.8 0.497 Ischaemic etiology (%) 64.8 71.7 0.013 Dual chamber device (%) 57.8 56.6 0.679 Amiodarone use (%) 13.2 12.5 0.794 Active patients at 3 months having a 3 month post op visit (%) 92.1 93.7 0.31 Interval between implant and first post op visit (days) 100 ± 21.3 101 ± 20.8 0.54 Parameter . Home monitoring . Conventional monitoring . P-value . n = 908 . n = 431 . Implant to enrolment (days) 15.9 ± 12.8 15.8 ± 12.7 days 0.93 Age (years) 63.3 ± 12.8 64.0 ± 12.1 0.365 Male gender 72.0 73.1 0.695 Primary prevention (%) 72.2 73.8 0.599 LVEF (%) 29.0 ± 10.7 28.5 ± 9.8 0.497 Ischaemic etiology (%) 64.8 71.7 0.013 Dual chamber device (%) 57.8 56.6 0.679 Amiodarone use (%) 13.2 12.5 0.794 Active patients at 3 months having a 3 month post op visit (%) 92.1 93.7 0.31 Interval between implant and first post op visit (days) 100 ± 21.3 101 ± 20.8 0.54 Open in new tab Table 1 Patient characteristics Parameter . Home monitoring . Conventional monitoring . P-value . n = 908 . n = 431 . Implant to enrolment (days) 15.9 ± 12.8 15.8 ± 12.7 days 0.93 Age (years) 63.3 ± 12.8 64.0 ± 12.1 0.365 Male gender 72.0 73.1 0.695 Primary prevention (%) 72.2 73.8 0.599 LVEF (%) 29.0 ± 10.7 28.5 ± 9.8 0.497 Ischaemic etiology (%) 64.8 71.7 0.013 Dual chamber device (%) 57.8 56.6 0.679 Amiodarone use (%) 13.2 12.5 0.794 Active patients at 3 months having a 3 month post op visit (%) 92.1 93.7 0.31 Interval between implant and first post op visit (days) 100 ± 21.3 101 ± 20.8 0.54 Parameter . Home monitoring . Conventional monitoring . P-value . n = 908 . n = 431 . Implant to enrolment (days) 15.9 ± 12.8 15.8 ± 12.7 days 0.93 Age (years) 63.3 ± 12.8 64.0 ± 12.1 0.365 Male gender 72.0 73.1 0.695 Primary prevention (%) 72.2 73.8 0.599 LVEF (%) 29.0 ± 10.7 28.5 ± 9.8 0.497 Ischaemic etiology (%) 64.8 71.7 0.013 Dual chamber device (%) 57.8 56.6 0.679 Amiodarone use (%) 13.2 12.5 0.794 Active patients at 3 months having a 3 month post op visit (%) 92.1 93.7 0.31 Interval between implant and first post op visit (days) 100 ± 21.3 101 ± 20.8 0.54 Open in new tab Figure 2 Open in new tabDownload slide Patient cohorts. Figure 2 Open in new tabDownload slide Patient cohorts. In HM, time to first HM transmission was median 1 day (IQ range 0–5 days) after enrolment. Thereafter, transmission success per patient was 91% during a median follow-up of 434 days (Figure 3). Overall, daily HM transmissions failed in 47 655 of a potential 363 450 days (13.1%). Physicians confirmed that HM data alone was sufficient for clinical assessment in 97% of total HM evaluations.8 During the 15 month trial period, 55/3759 (1.46%) of scheduled evaluations in HM were unsuccessful and attributed to transmission loss. These occurred in 49/908 (5.4%) individuals i.e. losses generally occurred only once in any affected patient. In these, last updated transmission occurred at a median time of 36 days prior to those ‘failed’ scheduled remote checks which were followed by a completed in-person visit. Transmission was reinstated within a median interval of 1 day after those in-person follow-ups. Figure 3 Open in new tabDownload slide Transmission success in HM. Figure 3 Open in new tabDownload slide Transmission success in HM. Battery capacity was compared between HM and Conventional patients. Of 1339 patients of the original cohort, battery status was available in 604 HM and 156 conventional patients with data available at the time of the 15 Month follow-up (device distribution is shown in Table 2). One conventional patient with a Sprint Fidelis lead crossed over from Conventional to HM care and was analysed as intention-to-treat. During the study, there was one generator change due to ERI because of patient twiddling (causing lead fracture and multiple charging cycles)7 and this was excluded from analysis. Overall, mean battery status at 15 month for all devices was 91.2%. Home monitoring and CM were compared. The incidence of increased pacing outputs of >1V and/or increase in pulse width of pacing stimulus threshold in HM was 26/1762 (1.5%) evaluations vs. 9/802 (1.1%) evaluations in CM (P = 0.58). Reprogramming to reduce or eliminate inappropriate therapy at in office evaluations occurred in 79/1762 (4.5%) in HM and 23/802 (2.9%) in CM between 0–15 months (P = 0.06) i.e. a greater trend in HM. Shock frequency (shocks/patient year: 0.29 ± 1.08 in HM vs. 0.31 ± 1.10 in CM, P = 0.80), and percent combined atrial and ventricular pacing burden (15.9 ± 23.6% for HM vs. 19.7 ± 21.6% for CM, P = 0.072) did not differ. Median atrial and ventricular pacing was 0%. Zero percent ventricular pacing was recorded in the majority of transmissions [27907/32476 (86%) HM vs. 385/438 (88%) CM transmission days, P = 0.27] and 100% in a minority [593/32476 (1.8%) vs. 3/438 (0.6%) transmission days]. Zero percent atrial pacing was recorded in occurred in the majority [11303/18198 (62%) HM vs. 162/292 (55%) CM transmission days] and 100% in a minority [277/18198 (2%) vs. 4/292 (1%) transmission days]. Overall, ICD battery longevity at 15 months in patients randomized to HM compared to CM was 93.2 ± 8.8% vs. 83.5 ± 6.0% representing a 12% relative improvement (Figure 4). This difference remained statistically significant with and without accounting for pacing percentage, shock-incidence, and model type as independent covariates in the linear regression model (P < 0.001). Table 2 Device type distribution in the TRUST trial Device type . Number . 15 month battery statusa . . . HM . CM . Lumos VR-T 393 186 42 Lumos DR-T 449 215 65 Lumax 340 VR-T 163 58 10 Lumax 340 DR-T 297 129 36 Lumax 300 VR-T 17 8 2 Lumax 300 DR-T 20 8 1 Total 1339 604 156 Device type . Number . 15 month battery statusa . . . HM . CM . Lumos VR-T 393 186 42 Lumos DR-T 449 215 65 Lumax 340 VR-T 163 58 10 Lumax 340 DR-T 297 129 36 Lumax 300 VR-T 17 8 2 Lumax 300 DR-T 20 8 1 Total 1339 604 156 a Completed 15 month visit with battery status available. Open in new tab Table 2 Device type distribution in the TRUST trial Device type . Number . 15 month battery statusa . . . HM . CM . Lumos VR-T 393 186 42 Lumos DR-T 449 215 65 Lumax 340 VR-T 163 58 10 Lumax 340 DR-T 297 129 36 Lumax 300 VR-T 17 8 2 Lumax 300 DR-T 20 8 1 Total 1339 604 156 Device type . Number . 15 month battery statusa . . . HM . CM . Lumos VR-T 393 186 42 Lumos DR-T 449 215 65 Lumax 340 VR-T 163 58 10 Lumax 340 DR-T 297 129 36 Lumax 300 VR-T 17 8 2 Lumax 300 DR-T 20 8 1 Total 1339 604 156 a Completed 15 month visit with battery status available. Open in new tab Figure 4 Open in new tabDownload slide Comparison of Battery Capacity shows 12% superior longevity among patients assigned to HM (n = 604) vs. Conventional management (CM, n = 156) 15 months post implant. Figure 4 Open in new tabDownload slide Comparison of Battery Capacity shows 12% superior longevity among patients assigned to HM (n = 604) vs. Conventional management (CM, n = 156) 15 months post implant. Dual chamber and single chamber devices exhibited similar characteristics. Thus, for all single chamber devices (VR, n = 306, 40.3%), the overall mean battery status percentage was 92.6%, comprising 94.3% for HM vs. 84.5% for the CM group (P < 0.001), even after accounting for number of shocks per patient year using an ordinary least squares regression model (0.36 ± 1.29 HM vs. 0.36 ± 1.22 for HM, P = 0.99). For all dual chamber devices, the overall mean battery status percentage was 90.2%. The number of shocks per patient year was statistically different between the two groups (0.24 ± 0.90 for HM vs. 0.29 ± 1.04 for CM, P < 0.001). Battery status was 92.3 ± 9.12% for HM vs. 82.9 ± 6.54% for CM. This difference remained significant accounting for shocks per patient year as an independent co-variate in the linear regression model (P < 0.001). The different device models used exhibited the same differences between HM and CM. The Lumos series comprised the commonest models used (66.8%). For the dual chamber Lumos DR-T (n = 280, 37% of all devices used), at the time of the 15 month follow-up, overall mean battery status percentage was 88.8%. Shocks per patient year were similar between groups (HM 0.29 ± 1.09 vs. CM 0.30 ± 1.02, P = 0.94). Mean pacing percentage also did not differ (HM 23.7 ± 23.72% vs. 26.1 ± 16.9% for CM, P = 0.499). However, estimated mean battery capacity percentage was 91.0% in HM vs. 81.3% in CM (P < 0.001). In the single chamber Lumos VR-T model (n = 228) which comprised 30% of all devices used, overall mean battery status percentage was 91.8% at the time of the 15 month follow-up. Shock frequency (0.41 ± 1.44 in HM vs. 0.44 ± 1.37 per patient year in CM, P = 0.88) and mean pacing percentage (HM vs. CM: 7 ± 17.57% vs. 11 ± 26.86%, P = 0.235) were similar between groups. Estimated mean battery capacity percentage was 93.6% in HM vs. 84.1% in CM (P < 0.001). Results with the newer models (Lumax, 33% of all ICDs used) were similar. For Lumax 340 DR-T (n = 165), estimated mean battery capacity percentage was 94.5 ± 7.43% in HM vs. 85.7 ± 5.55% in CM (P < 0.001) despite similar shocks per patient year (0.16 ± 0.50 in HM vs. 0.27 ± 1.09 in CM, P = 0.41) and pacing percentage (19.3 vs. 22.8%, P = 0.386). For the Lumax 340 VR-T (n = 68), number of shocks per patient year (HM 0.22 ± 0.71 vs. CM 0.08 ±0.24, P = 0.54) and mean pacing percentage (HM 8.1 ± 20.92% vs. CM 5.3 ± 9.6%, P= 0.68) were similar but mean battery status percentage was superior for HM vs. CM (97.3 ± 5.95% vs. 85.7 ± 3.74%, P < 0.001). During extended follow-up, 686 HM patients continued daily transmission and were tracked without any in-person evaluation. This set of patients included all those assessed at 15 months (Figure 4) as well as those who had missed this time point for in person evaluation, but continued remote management. Estimated battery longevity was 50.9 ± 9.1% (median 52%) at 36 months (Figure 5). Figure 5 Open in new tabDownload slide Battery longevity for all available HM subjects transmitting 3 years post ICD implant (device breakdown at 36 months: 158 Lumos DR-T, 146 Lumos VR-T, 110 Lumax 340 DR-T, 6 Lumax 300 DR-T, 7 Lumax 300 VR-T, and 54 Lumax 340 VR-T). Figure 5 Open in new tabDownload slide Battery longevity for all available HM subjects transmitting 3 years post ICD implant (device breakdown at 36 months: 158 Lumos DR-T, 146 Lumos VR-T, 110 Lumax 340 DR-T, 6 Lumax 300 DR-T, 7 Lumax 300 VR-T, and 54 Lumax 340 VR-T). Discussion Knowledge of transmission reliability and effect on battery longevity of any particular remote monitoring technology to be utilized is important before its application to chronic patient management. Missed transmissions and/or excessive battery drain are potentially detrimental. The current results, from a large randomized clinical trial testing one automatic wireless remote monitoring system, demonstrated high transmission reliability while operating a daily transmission schedule, without sacrificing battery longevity over 15 months. Recently recommendations advocate remote monitoring as the standard of care for following patients with CIEDs.1,2 This contrasts to prior statements for use of remote patient management as an adjunct to conventional periodic calendar-based in-person evaluation.9 This difference marks a shift in the operating paradigm of remote patient management from remote ‘interrogation‘ structured to mirror in-office checks, to a system of near continuous monitoring with clinic interactions for alert notifications triggered by changes in device function or disease state,3 as and when necessary. The HomeGuide Registry confirmed the high quality of remotely acquired data with HM since the majority (>80%) of clinically meaningful events were remotely detected (sensitivity 84%; positive predictive value 97%).10 Given this important role, robust technology performance is mandatory. Failure exposes patients (and physicians) to risk. Potentially, a breakdown in the remote monitoring process can occur at any one of several steps. Dependence on patient activation—known to be vulnerable to non-compliance—was not a factor here since HM is fully automatic, requiring no deliberate patient participation in the transmission process. Transmission failure is a concern. In one prior clinical trial, evaluation of another automatic RM system reported a 45% transmission failure rate over a similar period to that tested here.4,11 In this light, the current results are significant since 91% of all daily transmissions were successfully delivered. The number of scheduled evaluations dependent on remotely transmitted data that were unsuccessful was <1%, indicating that when transmission loss occurred it was sporadic and rarely sustained. The latter were rapidly rectified after discovery indicating that missed transmissions likely resulted from simple disconnections (e.g. vacation). These results support robust performance of automatic wireless remote HM in clinical practice during extended use. A daily frequency of remote transmissions with one system was tested here. Different proprietary technologies have different default transmission frequencies and differing options for their programmability. However, recent data indicate that there exists a graded relationship of survival linked with the level of adherence to remote monitoring.12 A potential mechanism is that a messaging system employing a higher frequency of transmission increases the probability of early detection and thus timely medical reaction. In one prospective comparison of remote monitoring systems using transmission frequencies, daily transmission was associated with a higher probability of early event detection.13 Parameter updates that occur daily provide high temporal resolution and thus improved accuracy of alert notifications. In randomized trials, daily data transmission was associated with reduced heart failure hospitalizations and improved survival in high risk heart failure patients. In meta-analysis, systems using lesser transmission frequencies did not accrue these advantages.4 Daily transmission may therefore meet the requirements of automatic wireless remote monitoring as expressed in recent recommendations.1,2 While high transmission frequency may be clinically desirable, there is a prevalent concern that this load may promote early battery depletion. Our data show the reverse, since during the trial period of 15 months battery life was better preserved in the patient group randomized to HM use (Figure 4). Significantly, in extended analysis to 36 months after implant (i.e. following >1000 transmissions) median battery longevity was >50% (aligning with contemporary ICD service life of 5–7 years14). These advantages were observed across both single and dual chamber ICDs, and different model types [newer devices used extended duration of wireless electrogram transmission (Figure 1)], indicating that these were attributes of HM and not device platform. The reasons are uncertain. In prior reports using the same HM system, diminished shock delivery resulting from reprogramming triggered by remotely transmitted data was proposed to be the mechanism for improved battery life.15 However, in our study, shock incidence between the two randomized patient groups did not differ (the TRUST trial protocol did not mandate device settings). Notably, battery life of pacemakers was improved when patients were managed with HM, indicating that reduction in shock delivery is not necessary to observe this effect.16 However, that retrospective analysis was confounded by the increased incidence of heart block, advanced age, pacemaker generator changes, and prevalence of chronic leads in the conventionally managed group, raising the possibility that improved battery longevity with HM resulted merely from reduced pacing load (frequency and output). This factor was accounted for in our prospective study, since only non-pacer dependent patients were enrolled. During the course of the trial, percent pacing, and the incidence of re-programming lead outputs (≤1.5%) remained equal between the study arms, yet battery longevity was improved by HM. A possible mechanism may be that action taken on remotely acquired data optimized device function. In this regard, the incidence of reprogramming changes to reduce or eliminate inappropriate therapy between 0–15 months was higher in HM compared to CM arms [79/1762 (4.5%) vs. 23/802 (2.9, respectively, P = 0.06]. However, due to the limitation of the data collected on study case report forms, no specific information is available to the effect of reprogramming changes on the total number of charging cycles.17 Overall, our data suggest that any battery ‘cost’ associated with running HM, if present, is more than compensated for by its enabling effect to optimize device programming during follow-up. Indirectly, these actions may reduce incidence of future battery replacement with its attendant risk of complications (surgeries are not benign)18 and accompanying costs. In one analysis, an increase in device longevity of about 2 years was estimated to yield a relative saving of about 20% over a 15-year time horizon.19 Our study illustrates the unique power of remote monitoring for assessment of CIED function, in this case battery longevity. This mechanism is independent of direct patient contact while the transmission link is preserved (Figure 5). Automatic warehousing of device data updated daily from large consecutive patient cohorts permits long-term longitudinal device evaluation. In comparison, contemporary techniques such as intermittent in person evaluation, or analysis of voluntary return of products, test small samples that are vulnerable to reporting bias, undermining the important task of CIED component surveillance and assessment of reliability. Continuous remote monitoring, as currently described, is a stringent method for tracking device function. This meets a need for understanding performance in real world practice and quality assurance.2 Strengths and limitations There are few data regarding battery performance outside product manuals. The few clinical studies have been single center, enrolled few patients and ignored the impact of remote monitoring transmissions. In contrast, our analysis is from a prospective multicenter trial. However, our study groups are imbalanced for patient numbers since patient retention was greater in HM (already favoured by 2:1 randomization) and analysis of CM patients was dependent on conversion to HM at the end of the trial, which was not mandatory. This may introduce some systematic bias. We evaluated only one automatic wireless remote monitoring technology. Thus our results cannot be transferred to other proprietary technologies with their varying transmission frequencies, methods of alert notification, and battery technologies. Differences may be significant. In our study battery longevity was 50% over 3 years and ∼1000 transmissions, whereas this load would result in battery exhaustion with other systems.20 We did not assess total battery life. Our analysis accounted for ventricular pacing (recognized as a significant drain on battery) and also atrial pacing and shock history which have been inconsistently addressed in prior studies.14,21 Precise reasons for improved battery longevity with HM cannot be discerned in this post hoc analysis. It is unlikely that in-clinic interrogation in CM affected battery longevity significantly, since charge consumption is in the low single digit micro-ampere range even with repeated and prolonged communication sessions, and thus negligible compared to the daily operation of an ICD. The source of difference between the two study arms likely relates to reactions to remotely acquired data in HM e.g. identification of non-sustained ventricular arrhythmias and prevention of aborted charging cycles.17 Identification of these mechanisms may permit their employment to enhance battery life further. Conclusions In summary, automatic remote HM operating a daily transmission schedule is highly reliable and may be sustained without reducing battery longevity. The results of our study endorse the HRS Class 1A recommendation that remote monitoring should be employed for battery conservation.2 Conflict of interest: N.V. was Consultant to Biotronik for Trial Design; C.J.L. has been consultant to Biotronik; P.M. and J.M. are Biotronik employees. Trial sponsor: Biotronik. References 1 Varma N , Auricchio A. Recommendations for post implant monitoring of patients with CIEDs: where do we stand today? Europace 2013 ; 15 (Suppl 1) : i11 – 3 . Google Scholar Crossref Search ADS PubMed WorldCat 2 Slotwiner D , Varma N, Akar JG, Annas G, Beardsall M, Fogel RI et al. HRS expert consensus statement on remote interrogation and monitoring for cardiovascular implantable electronic devices . Heart Rhythm 2015 ; 12 : e69 – 100 . Google Scholar Crossref Search ADS PubMed WorldCat 3 Burri H. Remote follow-up and continuous remote monitoring, distinguished . Europace 2013 ; 15 (Suppl 1) : i14 – 6 . Google Scholar Crossref Search ADS PubMed WorldCat 4 Parthiban N , Esterman A, Mahajan R, Twomey DJ, Pathak RK, Lau DH et al. Remote monitoring of implantable cardioverter-defibrillators: a systematic review and meta-analysis of clinical outcomes . J Am Coll Cardiol 2015 ; 65 : 2591 – 600 . Google Scholar Crossref Search ADS PubMed WorldCat 5 Hindricks G , Varma N, Kacet S, Lewalter T, Søgaard P, Guédon-Moreau L et al. Daily remote monitoring of implantable cardioverterdefibrillators: insights from the pooled patient-level data from three randomised controlled trials (IN-TIME, ECOST, TRUST). . Eur Heart J 2017 . Google Scholar OpenURL Placeholder Text WorldCat 6 Varma N , Epstein A, Irimpen A, Schweikert R, Shah J, Love CJ et al. Efficacy and safety of automatic remote monitoring for ICD follow-up: the TRUST trial . Circulation 2010 ; 122 : 325 – 32 . Google Scholar Crossref Search ADS PubMed WorldCat 7 Varma N , Michalski J, Epstein AE, Schweikert R. Automatic remote monitoring of implantable cardioverter-defibrillator lead and generator performance: the Lumos-T Safely RedUceS RouTine Office Device Follow-Up (TRUST) trial . Circ Arrhythm Electrophysiol 2010 ; 3 : 428 – 36 . Google Scholar Crossref Search ADS PubMed WorldCat 8 Varma N , Michalski J, Stambler B, Pavri BB, TRUST Investigators Superiority of automatic remote monitoring compared with in-person evaluation for scheduled ICD follow-up in the TRUST trial-testing execution of the recommendations . Eur Heart J 2014 ; 35 : 1345 – 52 . Google Scholar Crossref Search ADS PubMed WorldCat 9 Wilkoff BL , Auricchio A, Brugada J, Cowie M, Ellenbogen KA, Gillis AM et al. HRS/EHRA expert consensus on the monitoring of cardiovascular implantable electronic devices (CIEDs): description of techniques, indications, personnel, frequency and ethical considerations . Heart Rhythm 2008 ; 5 : 907 – 25 . Google Scholar Crossref Search ADS PubMed WorldCat 10 Ricci RP , Morichelli L, D’Onofrio A, Calo L, Vaccari D, Zanotto G et al. Effectiveness of remote monitoring of CIEDs in detection and treatment of clinical and device-related cardiovascular events in daily practice: the HomeGuide Registry . Europace 2013 ; 15 : 970 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat 11 Crossley G , Boyle A, Vitense H, Chang Y, Mead RH. The clinical evaluation of remote notification to reduce time to clinical decision (CONNECT) trial: the value of wireless remote monitoring with automatic clinician alerts . J Am Coll Cardiol 2011 ; 57 : 1181 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 12 Varma N , Piccini JP, Snell J, Fischer A, Dalal N, Mittal S. The relationship between level of adherence to automatic wireless remote monitoring and survival in pacemaker and defibrillator patients . J Am Coll Cardiol 2015 ; 65 : 2601 – 10 . Google Scholar Crossref Search ADS PubMed WorldCat 13 de Ruvo E , Sciarra L, Martino AM, Rebecchi M, Iulianella RV, Sebastiani F et al. A prospective comparison of remote monitoring systems in implantable cardiac defibrillators: potential effects of frequency of transmissions . J Interv Card Electrophysiol 2016 ; 45 : 81 – 90 . Google Scholar Crossref Search ADS PubMed WorldCat 14 Zanon F , Martignani C, Ammendola E, Menardi E, Narducci ML, DE Filippo P et al. Device longevity in a contemporary cohort of ICD/CRT-D patients undergoing device replacement . J Cardiovasc Electrophysiol 2016 ; 27 : 840 – 5 . Google Scholar Crossref Search ADS PubMed WorldCat 15 Guedon-Moreau L , Lacroix D, Sadoul N, Clementy J, Kouakam C, Hermida JS et al. A randomized study of remote follow-up of implantable cardioverter defibrillators: safety and efficacy report of the ECOST trial . Eur Heart J 2012 ; 34 : 605 – 14 . Google Scholar Crossref Search ADS PubMed WorldCat 16 Ricci RP , Morichelli L, Quarta L, Porfili A, Magris B, Giovene L et al. Effect of daily remote monitoring on pacemaker longevity: a retrospective analysis . Heart Rhythm 2015 ; 12 : 330 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat 17 Varma N , Johnson MA. Prevalence of cancelled shock therapy and relationship to shock delivery in recipients of implantable cardioverter-defibrillators assessed by remote monitoring . Pacing Clin Electrophysiol 2009 ; 32 ( Suppl 1) : S42 – 6 . Google Scholar Crossref Search ADS PubMed WorldCat 18 Poole JE , Gleva MJ, Mela T, Chung MK, Uslan DZ, Borge R et al. Complication rates associated with pacemaker or implantable cardioverter-defibrillator generator replacements and upgrade procedures: results from the REPLACE registry . Circulation 2010 ; 122 : 1553 – 61 . Google Scholar Crossref Search ADS PubMed WorldCat 19 Boriani G , Braunschweig F, Deharo JC, Leyva F, Lubinski A, Lazzaro C. Impact of extending device longevity on the long-term costs of implantable cardioverter-defibrillator therapy: a modelling study with a 15-year time horizon . Europace 2013 ; 15 : 1453 – 62 . Google Scholar Crossref Search ADS PubMed WorldCat 20 Medtronic . ProtectaTMXT/ProtectaTMProjected service life information related to remotemonitoring. EVERA™ XT VR DVBB1D1 . Clinician Manual Supplement 2015 . Manual No:M945739A001A. OpenURL Placeholder Text WorldCat 21 Horlbeck FW , Mellert F, Kreuz J, Nickenig G, Schwab JO. Real-world data on the lifespan of implantable cardioverter-defibrillators depending on manufacturers and the amount of ventricular pacing . J Cardiovasc Electrophysiol 2012 ; 23 : 1336 – 42 . Google Scholar Crossref Search ADS PubMed WorldCat Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For Permissions, please email: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For Permissions, please email: journals.permissions@oup.com.
TI - Automatic remote monitoring utilizing daily transmissions: transmission reliability and implantable cardioverter defibrillator battery longevity in the TRUST trial
JF - Europace
DO - 10.1093/europace/eux059
DA - 2018-04-01
UR - https://www.deepdyve.com/lp/oxford-university-press/automatic-remote-monitoring-utilizing-daily-transmissions-transmission-Isr9aqmTPY
SP - 622
EP - 628
VL - 20
IS - 4
DP - DeepDyve
ER -