Factors affecting electrogram sensing in an insertable cardiac monitor: Insights from surface electrocardiogram mapping analysis.

BACKGROUND: Fidelity of electrogram sensing may reduce false alerts from an insertable cardiac monitor (ICM). OBJECTIVE: The purpose of this study was to assess the effect of vector length, implant angle, and patient factors on electrogram sensing using surface electrocardiogram (ECG) mapping. METHODS: Twelve separate precordial single-lead surface ECGs were acquired from 150 participants at 2 interelectrode distances (75 and 45 mm), at 3 vector angles (vertical, oblique, and horizontal), and in 2 postures (upright and supine). A subset of 50 patients also received a clinically indicated ICM implant in 1:1 ratio (Reveal LINQ [Medtronic, Minneapolis, MN]/BIOMONITOR III [Biotronik, Berlin, Germany]). All ECGs and ICM electrograms were analyzed by blinded investigators using DigitizeIt software (V2.3.3, Braunschweig, Germany). The P-wave visibility threshold was set at > 0.015 mV. Logistic regression was used to identify factors affecting P-wave amplitude. RESULTS: A total of 1800 tracings from 150 participants (44.5% [n = 68] female; median age 59 [35-73] years) were assessed. The median P- and R-wave amplitudes were 45% and 53% larger with vector lengths of 75 and 45 mm, respectively (P < .001 for both). The oblique orientation yielded the best P- and R-wave amplitudes, while posture change did not affect P-wave amplitude. Mixed effects modeling found that visible P-waves occur more frequently with a vector length of 75 mm than with 45 mm (86% vs 75%, respectively; P < .0001). A longer vector length improved both P-wave amplitude and visibility in all body mass index categories. There was a moderate correlation of P- and R-wave amplitudes from the ICM electrograms to those from surface ECG recordings (intraclass correlation coefficient 0.74 and 0.80, respectively). CONCLUSION: Longer vector length and oblique implant angle yielded the best electrogram sensing and are relevant considerations for ICM implantation procedures.

Impact of intensive follow-up of cardiac implantable electronic devices via remote monitoring: A pilot study.

Background: The volume of remote monitoring (RM) data generates a significant workload and is generally dealt with by clinic staff during standard office hours, potentially delaying clinical action. Objective: The purpose of this study was to determine the clinical efficiency and workflow of implementing intensive RM (IRM) in patients with cardiac implantable electronic device (CIED) when compared with standard RM (SRM). Methods: From a cohort of >1500 remotely monitored devices, 70 patients were randomly selected to undergo IRM. For comparison, an equal number of matched patients were prospectively selected for SRM. Intensive follow-up occurred via automated vendor-neutral software with rapid alert processing by International Board of Heart Rhythm Examiners-certified device specialists. Standard follow-up was conducted by clinic staff during office hours via individual device vendor interfaces. Alerts were categorized on the basis of the level of acuity as actionable (red [high], yellow [moderate]), or green [not requiring action]). Results: Over 9 months of follow-up, 922 remote transmissions were received; 339 (36.8%) were coded as actionable alerts (118 in IRM and 221 in SRM; P < .001). The median time from initial transmission to review was 6 hours (interquartile range [IQR] 1.8-16.8 hours) in the IRM group compared with 10.5 hours (IQR 6.0-32.2 hours) in the SRM group (P < .001). The median time from transmission to review of actionable alerts in the IRM group was 5.1 hours (IQR 2.3-8.9 hours) compared with 9.1 hours (IQR 6.7-32.5 hours) in the SRM group (P < .001). Conclusion: Intensive and managed RM results in a significant reduction in time to review alerts and number of actionable alerts. Monitoring with enhanced alert adjudication is needed to facilitate device clinic efficiency and optimize patient care. Study Registration: ACTRN12621001275853.

Assessment of available online educational resources for patients with atrial fibrillation.

Background: A diagnosis of atrial fibrillation (AF) often leads patients to search online for information, which can expose them to information of varied quality. Objective: We conducted a qualitative systematic review of websites that contain useful information regarding AF. Methods: The following terms were searched on 3 search engines (Google/Yahoo/Bing): (Atrial fibrillation for patients), (What is atrial fibrillation), (Atrial fibrillation patient information), (Atrial fibrillation educational resources). Inclusion criteria included websites with comprehensive AF information and information about treatment options. The Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P) and PEMAT for Audiovisual Materials assessed understandability and actionability (score range 0-100). Those with a mean PEMAT-P score of >70, meaning acceptable understandability and actionability, underwent DISCERN score assessment of information content quality and reliability (score range 16-80). Results: The search yielded 720 websites that underwent full review. After exclusions, 49 underwent full scoring. The mean overall PEMAT-P score was 69.3 ± 17.2. The mean PEMAT-AV score was 63.4 ± 13.6. Of the websites that scored >70% on the PEMAT-P, 23 (46%) underwent DISCERN scoring. The mean DISCERN score was 54.7 ± 4.6. Conclusions: There is a wide variation in the understandably, actionability, and quality of websites, many not providing patient-level materials. Knowledge of quality websites could provide an important adjunct for improving patients understanding of AF.

Impact of device length on electrogram sensing in miniaturized insertable cardiac monitors.

BACKGROUND: Little data exists on electrogram sensing in current generation of miniaturized insertable cardiac monitors (ICMs). OBJECTIVE: To compare the sensing capability of ICM with different vector length: Medtronic Reveal LINQ (~40 mm) vs. Biotronik Biomonitor III (BM-III, ~70 mm). METHODS: De-identified remote monitoring transmissions from n = 40 patients with BM-III were compared with n = 80 gender and body mass index (BMI)-matched patients with Reveal LINQ. Digital measurement of P- and R-wave amplitude from calibrated ICM electrograms was undertaken by 3 investigators independently. Further, we evaluated the impact of BMI and gender on P-wave visibility. RESULTS: Patients in both groups were well matched for gender and BMI (53% male, mean BMI 26.7 kg/m2, both p = NS). Median P- and R-wave amplitude were 97% & 56% larger in the BM-III vs. LINQ [0.065 (IQR 0.039-0.10) vs. 0.033 (IQR 0.022-0.050) mV, p < .0001; & 0.78 (IQR 0.52-1.10) vs. 0.50 (IQR 0.41-0.89) mV, p = .012 respectively). The P/R-wave ratio was 36% greater with the BM-III (p < .001). The 25th percentile of P-wave amplitude for all 120 patients was .026 mV. Logistic regression analysis showed BM-III was more likely than LINQ to have P-wave amplitude ≥.026 mV (OR 7.47, 95%CI 1.965-29.42, p = .003), and increasing BMI was negatively associated with P-wave amplitude ≥.026 mV (OR 0.84, 95%CI 0.75-0.95, p = .004). However, gender was not significantly associated with P-wave amplitude ≥.026 mV (p = .37). CONCLUSION: The longer ICM sensing vector of BM-III yielded larger overall P- and R- wave amplitude than LINQ. Both longer sensing vector and lower BMI were independently associated with greater P-wave visibility.

False remote monitoring alerts from explanted cardiac implantable electronic device: How is this possible?

A 72-year-old woman with a dual-chamber implantable cardioverter-defibrillator (Biotronik Lumax 540 DR-T) at elective replacement indicator presented for generator replacement. A new MicroPort generator (Platinium DR) was attached to her existing leads. Eight days later, multiple red alert messages were received on the Biotronik remote monitoring system from the explanted generator. Investigations revealed alert transmission via a CardioMessenger Smart mobile device registered to another patient that came into proximity of the explanted generator. The Biotronik remote monitoring system is unique in that red alerts could be sent through any CardioMessenger Smart device regardless of whether they were paired.

Development of the Digital Arthritis Index, a Novel Metric to Measure Disease Parameters in a Rat Model of Rheumatoid Arthritis.

Despite a broad spectrum of anti-arthritic drugs currently on the market, there is a constant demand to develop improved therapeutic agents. Efficient compound screening and rapid evaluation of treatment efficacy in animal models of rheumatoid arthritis (RA) can accelerate the development of clinical candidates. Compound screening by evaluation of disease phenotypes in animal models facilitates preclinical research by enhancing understanding of human pathophysiology; however, there is still a continuous need to improve methods for evaluating disease. Current clinical assessment methods are challenged by the subjective nature of scoring-based methods, time-consuming longitudinal experiments, and the requirement for better functional readouts with relevance to human disease. To address these needs, we developed a low-touch, digital platform for phenotyping preclinical rodent models of disease. As a proof-of-concept, we utilized the rat collagen-induced arthritis (CIA) model of RA and developed the Digital Arthritis Index (DAI), an objective and automated behavioral metric that does not require human-animal interaction during the measurement and calculation of disease parameters. The DAI detected the development of arthritis similar to standard in vivo methods, including ankle joint measurements and arthritis scores, as well as demonstrated a positive correlation to ankle joint histopathology. The DAI also determined responses to multiple standard-of-care (SOC) treatments and nine repurposed compounds predicted by the SMarTRTM Engine to have varying degrees of impact on RA. The disease profiles generated by the DAI complemented those generated by standard methods. The DAI is a highly reproducible and automated approach that can be used in-conjunction with standard methods for detecting RA disease progression and conducting phenotypic drug screens.