New implantable cardiac monitor with three-lead ECG and active noise detection

Remote monitoring of implantable cardiac monitors in patients with unexplained syncope: Predictors of false-positive alert episodes

BACKGROUND

Remote monitoring is recommended for patients with implantable cardiac monitors (ICMs), but compared to other cardiac implantable devices, ICMs are less accurate and transmit a higher number of alerts.

OBJECTIVE

The aim of this study was to investigate the predictors of false-positive (FP) arrhythmic alerts in patients with unexplained syncope who were implanted with ICM and followed by an automatic remote monitoring system.

METHODS

We retrospectively evaluated all consecutive patients who received a long-sensing vector ICM for unexplained syncope between January 2019 to September 2021 at our Syncope Unit. The primary endpoint was the incidence of the first FP episode. The secondary endpoints included assessing the incidence of FP episodes for all types of algorhythms and indentifying the reasons for the misdetection of these episodes.

RESULTS

Among 105 patients (44.8% males, median age 51 years), 51 (48.6%) transmitted at least one FP alert during a median follow-up of 301 days. The presence of pre-ventricular complexes (PVCs) on the resting electrocardiogram was the only clinical characteristic associated with an increased risk of FP alerts (adjusted Hazard ratio [HR] 5.76 [2.66-12.4], p = 0.010). The other significant device-related variables were a low-frequency filter at 0.05 Hz versus the default 0.5 Hz (adjusted HR 3.82 [1.38-10.5], p = 0.010) and the R-wave amplitude (adjusted HR 0.35 [0.13-0.99], p = 0.049).

CONCLUSION

Patients who have PVCs are at higher risk of inappropriate ICM activations. To reduce the occurrence of FP alerts, it may be beneficial to target a large R-wave amplitude during device insertion and avoid programming a low-frequency filter at 0.05 Hz.

Insertable cardiac monitor with a long sensing vector: Impact of obesity on sensing quality and safety

Background

Fat layers in obese patients can impair R-wave detection and diagnostic performance of a subcutaneous insertable cardiac monitor (ICM). We compared safety and ICM sensing quality between obese patients [body mass index (BMI) ≥ 30 kg/m²] and normal-weight controls (BMI <30 kg/m²) in terms of R-wave amplitude and time in noise mode (noise burden) detected by a long-sensing-vector ICM.

Materials and methods

Patients from two multicentre, non-randomized clinical registries are included in the present analysis on January 31, 2022 (data freeze), if the follow-up period was at least 90 days after ICM insertion, including daily remote monitoring. The R-wave amplitudes and daily noise burden averaged intraindividually for days 61-90 and days 1-90, respectively, were compared between obese patients (n = 104) and unmatched (n = 268) and a nearest-neighbour propensity score (PS) matched (n = 69) normal-weight controls.

Results

The average R-wave amplitude was significantly lower in obese (median 0.46 mV) than in normal-weight unmatched (0.70 mV, P < 0.0001) or PS-matched (0.60 mV, P = 0.003) patients. The median noise burden was 1.0% in obese patients, which was not significantly higher than in unmatched (0.7%; P = 0.056) or PS-matched (0.8%; P = 0.133) controls. The rate of adverse device effects during the first 90 days did not differ significantly between groups.

Conclusion

Although increased BMI was associated with reduced signal amplitude, also in obese patients the median R-wave amplitude was >0.3 mV, a value which is generally accepted as the minimum level for adequate R-wave detection. The noise burden and adverse event rates did not differ significantly between obese and normal-weight patients.Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04075084 and NCT04198220.

R-wave amplitude changes with posture and physical activity over time in an insertable cardiac monitor

Background

Insertable cardiac monitors (ICMs) are accepted tools in cardiac arrhythmia management. Consistent R-wave amplitude (RWA) is essential for optimal detection.

Objectives

Assess RWAs with posture/activities at insertion and at 30 days.

Methods

Participants (n = 90) with Confirm Rx™ ICM had RWAs measured in different postures (supine, right-side [RS], left-side [LS], sitting, and standing) and defined physical activities (including isometric push [IPUSH] and pull) at 2 time points. ICMs were inserted in 45° to sternum and parasternal orientations.

Results

There were significant reductions at insertion with RS, LS, sitting, or standing vs supine (reference position) (all P < .05). At 30 days, significant changes only occurred with LS and sitting (P < .05). Sex had an effect on RWAs, with females having significant variability at insertion (supine vs RS, LS, sitting, standing, and IPUSH; all P < .05). Males showed large RWA interpatient variabilities but minimal differences between positions vs supine. At 30 days, RS, LS, and sitting positions remained significant for females (P < .05), while in males RWAs were higher than at insertion for most postures and activities. The orientation 45° to sternum had consistently higher RWAs vs parasternal orientation at both time points (P < .0001). In females, ICM orientation had no significant effect on RWAs; however, in males the 45° to sternum produced higher RWAs. ICM movement from the insertion site showed no correlation with RWA changes.

Conclusion

The mean RWAs were higher at 30 days with less interparticipant and interpostural variability; males had higher RWAs compared to females; 45° to sternum orientation had higher RWAs; and ICM migration from the insertion site did not affect RWAs.

Subcutaneous cardiac rhythm monitors: state of the art review

Introduction: Subcutaneous cardiac rhythm monitors (SCRMs) provide continuous ambulatory electrocardiographic monitoring for surveillance of known and identification of infrequent arrhythmias. SCRMs have proven to be helpful for the evaluation of unexplained symptoms and correlation with intermittent cardiac arrhythmias. Successful functioning of SCRM is dependent on accurate detection and successful transmission of the data to the device clinic. As the use of SCRM is steadily increasing, the amount of data that requires timely adjudication requires substantial resources. Newer algorithms for accurate detection and modified workflow systems have been proposed by physicians and the manufacturers to circumvent the issue of data deluge.Areas covered: This paper provides an overview of the various aspects of ambulatory rhythm monitoring with SCRMs including indications, implantation techniques, programming strategies, troubleshooting for issue of false positive and intermittent connectivity and strategies to circumvent data deluge.Expert opinion: SCRM is an invaluable technology for prolonged rhythm monitoring. The clinical benefits from SCRM hinge on accurate arrhythmia detection, reliable transmission of the data and timely adjudication for possible intervention. Further improvement in SCRM technology is needed to minimize false-positive detection, improve connectivity to the central web-based server, and devise strategies to minimize data deluge.

Implantable loop recorders for cardiac dysrhythmia monitoring

Incorporation of technological advances in healthcare delivery has played a foundational role in development of modern healthcare. With the use of wireless transmission in conjunction with digitization of electrocardiography, continuous monitoring strategies have redefined our approach to dysrhythmia. These devices show promising results in evolving implantable loop recorder technology. In this review, we summarize the history of remote monitoring, indications for loop recorders, devices available, evidence for specific devices and anticipated studies.

Accuracy of implantable loop recorders for detecting atrial tachyarrhythmias after atrial fibrillation catheter ablation

Background

Implantable loop recorders (ILRs) can provide an enhanced possibility to detect atrial fibrillation (AF), but the accuracy, especially the positive predictive value (PPV), is controversial. This study aimed to evaluate the accuracy of ILRs for detecting AF through a comparison with Holter.

Method and results

Thirteen patients who underwent AF ablation were enrolled. ILRs were implanted in all patients, who were scheduled to have Holter monitorings after the procedure. The incidence of AF was compared between the two modalities and analyzed for any correlations. A total of 51 Holters (67,985.5 min) and concomitant ILRs were available for the comparison. The judgment of the presence of AF did not perfectly correlate between the ILR and Holter (Kappa = 0.866, P < 0.001). In the ILR data, the sensitivity of detecting AF on the Holter was 81.6% (95% confidence interval [CI] 0.812–0.820; P < 0.001). The specificity was 99.9% (95% CI 0.998–0.999; P < 0.001). When the ILR detected AF, the PPV was 99.5% (95% CI 0.994–0.995), but the ILR did not detect AF, and the negative predictive value was 94.2% (95% CI 0.941–0.944). A separate analysis of AF/atrial tachycardia (AT) showed that the AT detection rate of the ILR was 2.3%.

Conclusion

The ILR had a low false positive value and high PPV for AF events. However, it was limited in identifying AT.

Application of Neural Networks to 12-Lead Electrocardiography - Current Status and Future Directions

The 12-lead electrocardiogram (ECG) is a fast, non-invasive, powerful tool to diagnose or to evaluate the risk of various cardiac diseases. The vast majority of arrhythmias are diagnosed solely on 12-lead ECG. Initial detection of myocardial ischemia such as myocardial infarction (MI), acute coronary syndrome (ACS) and effort angina is also dependent upon 12-lead ECG. ECG reflects the electrophysiological state of the heart through body mass, and thus contains important information on the electricity-dependent function of the human heart. Indeed, 12-lead ECG data are complex. Therefore, the clinical interpretation of 12-lead ECG requires intense training, but still is prone to interobserver variability. Even with rich clinically relevant data, non-trained physicians cannot efficiently use this powerful tool. Furthermore, recent studies have shown that 12-lead ECG may contain information that is not recognized even by well-trained experts but which can be extracted by computer. Artificial intelligence (AI) based on neural networks (NN) has emerged as a strong tool to extract valuable information from ECG for clinical decision making. This article reviews the current status of the application of NN-based AI to the interpretation of 12-lead ECG and also discusses the current problems and future directions.

Clinical evaluation of a small implantable cardiac monitor with a long sensing vector

Introduction

We conducted this study to show the safety and efficacy of a new implantable cardiac monitor (ICM), the BioMonitor 2 (Biotronik SE & Co. KG; Berlin, Germany), and to describe the arrhythmia detection performance.

Methods

The BioMonitor 2 has an extended sensing vector and is implanted close to the heart. It can transmit up to six subcutaneous electrocardiogram strips by Home Monitoring each day. We enrolled 92 patients with a standard device indication for an ICM in a single‐arm, multicenter prospective trial. Patients were followed for 3 months, and 48‐h Holter recordings were used to evaluate the arrhythmia detection performance.

Results

One patient withdrew consent and in one patient, the implantation failed. Two study device‐related serious adverse events were reported, satisfying the primary safety hypothesis. Implantations took 7.4 ± 4.4 min from skin cut to suture. At 1 week, the R‐wave amplitude was 0.75 ± 0.53 mV. In the 82 patients with completed Holter recordings, all patients with arrhythmias were correctly identified. False positive detections of arrhythmia were mostly irregular rhythms wrongly detected as atrial fibrillation (episode‐based positive predictive value 72.5%). Daily Home Monitoring transmission was 94.9% successful.

Conclusion

Safety and efficacy of the new device has been demonstrated. The detected R‐wave amplitudes are large, leading to a low level of inappropriate detections due to over‐ or undersensing.