Success of pacemaker remote monitoring using app-based technology: Does patient age matter?

BACKGROUND: Remote monitoring (RM) improves patient outcomes. App-based RM is a novel technology that enables transmission of implantable device data using smart devices. Limited data exist on the impact of age and sex on the use of app-based RM. OBJECTIVE: To examine the impact of age and sex on the proportion of pacemaker patients who activated app-based RM, time from order to activation, and patient follow-up transmission adherence per guidelines. METHODS: A retrospective analysis was performed using deidentified data from U.S. pacemaker patients enrolled in the Medtronic CareLink database with an app-based monitor (MyCareLink Smart™). Activation was defined as first RM transmission and was considered early if it occurred < 90 days from order. Adherence analysis was limited to patients with ≥12 months' follow-up from activation and excluded transmissions < 90 days from activation. RESULTS: Of 48,016 patients assigned app-based RM, 40,511 (84.4%) activated their device; of these, 31,640 (65.9%) activated their device early. Among 14,232 activated patients (with 12 months' follow-up), 12,743 (89.5%) were considered adherent to guidelines by transmitting at least once more within 3-12 months following their activation transmission. While there were statistical differences in activation, early activation, and adherence among age and sex groups due to large sample sizes, the differences were not clinically significant and the majority of older patients were able to successfully use app-based RM. CONCLUSIONS: Most patients in this large and first-of-its kind reported cohort used smart devices to successfully activate app-based RM technology and remained adherent per guidelines irrespective of age or sex.

Technician-Supported Remote Interrogation of CIEDs: Initial Use in US Emergency Departments and Perioperative Areas.

BACKGROUND: Interrogation/interpretation of cardiac implantable electronic devices (CIEDs) is frequently required in the emergency department (ED) or perioperative areas (OR) where resources to do this are often not available. CareLink Express (CLE; Medtronic, plc, Mounds View, MN, USA) is a technician-supported real-time remote interrogation system for Medtronic CIEDs. Using data from 136 US locations, this retrospective study was designed to assess CLE efficiency compared to traditional device management, and examine its findings. METHODS: All 7,044 US CLE transmissions from the ED and OR (January 2012-October 2014) were compared to 217 traditional requests where CIED interrogations/interpretations were performed by calling industry representatives to these sites. RESULTS: CLE reduced the time to device interrogation/interpretation by 78%: 100 ± 140-22 ± 14 minutes, P < 0.0001, improving response time and consistency; ED: 82 ± 103-23 ± 18 minutes, P, ≤ 0.01; OR: 127 ± 181-17 ± 10 minutes, P < 0.0001. Actionable events (AE) (arrhythmia, device/lead abnormalities) were infrequent: 9.1% overall (ED: 9.9%; OR: 4.1%). Only 6.5% of patients with syncope/presyncope and 13.6% with a perceived shock had AE. AEs were more common in those with suspected device problems (30.4%) or audible alerts (52.6%). They were more likely in patients not enrolled in long-term remote monitoring (23.9% vs 8.2%, P < 0.0001) and in those with older CIED systems (7.4% in year 1 vs 31.0% after 10 years). CONCLUSIONS: The many patients with CIEDs, and the ability to quickly identify the minority with high-risk AE from the no/low-risk majority, strongly support CLE use in the ED and OR, sites which are expensive and prioritize efficiency.

Compliance with remote monitoring of ICDS/CRTDS in a real-world population.

BACKGROUND: Remote monitoring (RM) of defibrillators (implantable cardioverter defibrillators [ICDs]) and cardiac resynchronization therapy devices (CRTDs) has been shown to be cost effective, convenient, and associated with reduced mortality and a reduction in the time to physician intervention for actionable events. However, patient compliance with monitoring over time and what factors might influence such compliance have not been well described. This study sought to identify factors contributing to patient noncompliance with RM of ICDs and CRTDs in a large real-world population. METHODS: Deidentified data on U.S. patients enrolled in the Medtronic CareLink RM system were used to compare patients with no (noncompliant, n = 14,848) and with ≥ 2 RM transmissions (compliant, n = 103,284) during a 14-month period. RESULTS: Overall noncompliance with RM was 21%. Younger age (≤ 40), female sex, wanded device, Medicare Census Division, and small clinic size all predicted patient noncompliance (P < 0.01). Device type (ICD vs CRTD) did not (P = 0.52). Multivariate analysis suggested clinically important predictors of noncompliance to be: age ≤ 40, odds ratio (OR) 2.64 (95% confidence interval, 2.42-2.88); Medicare Census Division (Mountain vs West North Central), OR 2.15 (1.96-2.37); and small clinic size (1-4 vs >100 patients), OR 4.38 (3.92-4.91). CONCLUSIONS: There is room for improvement in RM usage among enrolled patients. Younger patients, smaller clinics, and certain geographic areas may be targets for research into interventions to further improve the use of RM.

Downloadable algorithm to reduce inappropriate shocks caused by fractures of implantable cardioverter-defibrillator leads.

BACKGROUND: The primary method for monitoring implantable cardioverter-defibrillator lead integrity is periodic measurement of impedance. Sprint Fidelis leads are prone to pace-sense lead fractures, which commonly present as inappropriate shocks caused by oversensing. METHODS AND RESULTS: We developed and tested an algorithm to enhance early identification of lead fractures and to reduce inappropriate shocks. This lead-integrity algorithm, which can be downloaded into presently implanted implantable cardioverter-defibrillators, alerts the patient and/or physician when triggered by either oversensing or excessive increases in impedance. To reduce inappropriate shocks, the lead-integrity algorithm increases the number of intervals to detect (NID) ventricular fibrillation when triggered. The lead-integrity algorithm was tested on data from 15 970 patients with Fidelis leads (including 121 with clinically diagnosed fractures) and 95 other fractured leads confirmed by analysis of returned product. The effect of the NID on inappropriate shocks was tested in 92 patients with 927 shocks caused by lead fracture. Increasing the NID reduced inappropriate shocks (P<0.0001). The lead-integrity algorithm provided at least a 3-day warning of inappropriate shocks in 76% (95% CI, 66 to 84) of patients versus 55% (95% CI, 43 to 64) for optimal impedance monitoring (P=0.007). Its positive predictive value was 72% for lead fractures and 81% for lead fractures or header-connector problems requiring surgical intervention. The false-positive rate was 1 per 372 patient-years of monitoring. CONCLUSIONS: A lead-integrity algorithm developed for download into existing implantable cardioverter-defibrillators increases short-term warning of inappropriate shocks in patients with lead fractures and reduces the likelihood of inappropriate shocks. It is the first downloadable RAMware to enhance the performance of nominally functioning implantable cardioverter-defibrillators and the first implantable cardioverter-defibrillator monitoring feature that triggers real-time changes in ventricular fibrillation detection parameters to reduce inappropriate shocks.

Automatic identification of clinical lead dysfunctions.

Implantable cardioverter defibrillators (ICD) lead dysfunctions can cause inappropriate shocks. Current ICDs store lead diagnostics and detected episodes. This stored information with intracardiac electrograms (EGM) and sensed RR interval patterns may characterize the ICD lead performance. The aim of this analysis was to determine the sensitivity and positive predictive value (PPV) of an automatic lead dysfunction identification algorithm. This algorithm uses RR and EGM data to distinguish noncardiac oversensing (OS), for example, due to conductor fracture, and cardiac OS, for example, T-wave OS, from detected episodes. The algorithm also uses lead diagnostics: sensing integrity counter trends (e.g., RR intervals <140 ms), nonsustained tachyarrhythmias episodes with a mean RR <200 ms and impedance trends to identify lead fractures. The PPV was determined using the stored memory from 1,756 ICD patients enrolled in a 13-center long-term lead study with an average follow-up of 18.3 patient-months. Sensitivity was determined in 35 patients who presented with OS or lead fracture-related adverse events confirmed by stored ICD diagnostics. The algorithm sensitivity was 97.1% (34/35). There were 43 additional patients identified by the algorithm without an adverse event. Stored ICD diagnostics confirmed lead dysfunctions in 32 of 43 patients corresponding with an 85.7% PPV (66/77). ICD memory diagnostics and episodes with intracardiac EGM may be used to identify ICD lead dysfunctions with high sensitivity and PPV. This algorithm may be implemented in postprocessing ICD environments (e.g., remote server, programmer) to rapidly identify lead dysfunction prior its clinical manifestation.

An algorithm to predict implantable cardioverter-defibrillator lead failure.

OBJECTIVES: The goal of this analysis was to test an algorithm that identifies implantable cardioverter-defibrillator (ICD) lead problems before clinical failure and/or inappropriate therapy. BACKGROUND: The ICD lead failures typically present as inappropriate shock therapy. Identifying lead failures before their clinical presentation may prevent patient discomfort, improve device longevity, and avoid device-induced proarrhythmia. METHODS: We tested an algorithm that uses two measures of oversensing and one measure of abnormal impedance to detect a lead failure. The oversensing measures consisted of a counter for RR intervals <140 ms and nonsustained ventricular tachycardia episodes with mean RR interval <200 ms. The impedance measure tracked lead impedances every day and each week. Abnormal impedance was defined as a decrease in impedances or an outlier value compared with baseline. Lead failures were identified when both oversensing measures were met or abnormal impedance and one oversensing measure occurred. The stored data from 696 patients with an ICD were analyzed to determine the sensitivity and specificity of the algorithm to detect lead failures. RESULTS: Twenty-nine patients demonstrated clinical lead failures with an average of 6 +/- 9 inappropriate shocks per patient. The two oversensing measures used in the algorithm predicted 72% (21 of 29) of the lead failures. Fulfilling at least two of the three impedance and oversensing measures, the sensitivity of our algorithm was 83% (24 of 29) with a 100% (667 of 667) specificity. CONCLUSION: Oversensing combined with abnormal impedance trends may be used to identify ICD lead failures with high sensitivity and very high specificity.