Remote monitoring of implantable electronic devices to predict heart failure decompensation

Abstract Funding Acknowledgements Type of funding sources: None. Background Remote monitoring (RM) of cardiac implantable electronic devices (CIED) in patients with heart failure allows to regularly analyze the devices" and patients" conditions. Purpose The purpose of this study was evaluation of the ultimate transmissions sent before death in patients monitored remotely. Methods The last transmissions delivered by the devices in patients enrolled into COMMIT-HF Registry (NCT02536443) who died when monitored remotely have been retrospectively analysed. The characteristics and contents of the transmissions and clinical reactions undertaken have been obtained from the RM systems of four major RM providers. Results Of 1,306 patients with CIEDs who were enrolled at the RM programme in our centre, 267 died and their last transmission occurred less than 90 days before death, of which 133 (49.8%) were scheduled and 134 (50.2%) alert-triggered. The median period between transmission and death was 31 days for scheduled and 8 days for alert-triggered transmissions. The most frequent alert-triggered transmissions were atrial fibrillation/flutter (35.8%) and ventricular tachyarrhythmias (24.6%). A clinical reaction has been undertaken after 9.8% of planned and 67.1% of alert-triggered transmissions and consisted mainly of telephone consultations and referrals for hospital admissions. Conclusions This is the first analysis of the ultimate transmissions delivered by CIEDs before death. In approximately 50% of patients, the last transmission has been alert-triggered. Hence, an appropriate organization of the RM facility, which should immediately analyse and react to the transmission, seems mandatory to obtain clinical benefit in patients with HF and RM. Causes of alerts and clinical reactionsCause of alertAll alert-triggered transmissions (N = 134)AF/AFL episode, n (%)48 (35.8%)Ventricular tachycardia, n (%)18 (13.4%)Ventricular fibrillation, n (%)15 (11.2%)Biventricular pacing percentage reduction, n (%)15 (11.2%)Others38 (28.3%)Congestion monitor indications, n (%)14 (10.4%)Clinical reactionPlanned transmission (N = 133)Alert-triggered transmission (N = 134)Telephone consultation10 (7.5%)58 (43.2%)Referral to the GP or outpatient specialist clinic visit2 (1.5%)12 (8.9%)Referral for hospital admission1 (0.7%)18 (13.4%)Pharmacotherapy modificationN/A2 (1.5%)Abstract Figure.

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Remote Monitoring of Cardiac Implantable Electronic Devices in Patients Undergoing Hybrid Comprehensive Telerehabilitation in Comparison to the Usual Care. Subanalysis from Telerehabilitation in Heart Failure Patients (TELEREH-HF) Randomised Clinical Trial

Journal of Clinical Medicine ◽

10.3390/jcm9113729 ◽

2020 ◽

Vol 9 (11) ◽

pp. 3729

Author(s):

Sławomir Pluta ◽

Ewa Piotrowicz ◽

Ryszard Piotrowicz ◽

Ewa Lewicka ◽

Wojciech Zaręba ◽

...

Keyword(s):

Heart Failure ◽

Usual Care ◽

Remote Monitoring ◽

Electronic Devices ◽

Left Ventricular ◽

Left Ventricular Ejection ◽

Ventricular Ejection Fraction ◽

Cardiac Implantable Electronic Devices ◽

Hospitalised Patients ◽

The Impact

Background: The impact of cardiac rehabilitation on the number of alerts in patients with remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) is unknown. We compared alerts in RM and outcomes in patients with CIEDs undergoing hybrid comprehensive telerehabilitation (HCTR) versus usual care (UC). Methods: Patients with heart failure (HF) after a hospitalization due to worsening HF within the last 6 months (New York Heart Association (NYHA) class I-III and left ventricular ejection fraction (LVEF) ≤40%) were enrolled in the TELEREH-HF study and randomised 1:1 to HCTR or UC. Patients with HCTR and CIEDs received RM (HCTR-RM). Patients with UC and CIEDs were offered RM optionally (UC-RM). Data from the initial 9 weeks of the study were analysed. Results: Of 850 enrolled patients, 208 were in the HCTR-RM group and 62 in the UC-RM group. The HCTR-RM group was less likely to have alerts of intrathoracic impedance (TI) decrease (p < 0.001), atrial fibrillation (AF) occurrence (p = 0.031) and lower mean number of alerts per patient associated with TI decrease (p < 0.0001) and AF (p = 0.019) than the UC-RM group. HCTR significantly decreased the occurrence of alerts in RM of CIEDs, 0.360 (95%CI, 0.189–0.686; p = 0.002), in multivariable regression analysis. There were two deaths in the HCTR-RM group (0.96%) and no deaths in the UC-RM group (p = 1.0). There were no differences in the number of hospitalised patients between the HCTR-RM and UC-RM group (p = 1.0). Conclusions: HCTR significantly reduced the number of patients with RM alerts of CIEDs related to TI decrease and AF occurrence. There were no differences in mortality or hospitalisation rates between HCTR-RM and UC-RM groups.

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Novel findings of respiratory rate increases using the multisensor HeartLogic heart failure monitoring algorithm in COVID-19-positive patients: a case series

European Heart Journal - Case Reports ◽

10.1093/ehjcr/ytab067 ◽

2021 ◽

Vol 5 (2) ◽

Author(s):

Andrea Rebecca Yapejian ◽

Marat Fudim

Keyword(s):

Heart Failure ◽

Respiratory Rate ◽

Remote Monitoring ◽

Case Series ◽

Fluid Status ◽

Cardiac Devices ◽

Heart Failure Decompensation ◽

Failure Monitoring ◽

Monitoring Algorithm ◽

Heart Failure Monitoring

Abstract Background With the ongoing coronavirus disease 2019 (COVID-19) epidemic, remote monitoring of patients with implanted cardiac devices has become more important than ever, as physical distancing measures have placed limits on in-clinic device monitoring. Remote monitoring alerts, particularly those associated with heart failure trends, have proved useful in guiding care in regard to monitoring fluid status and adjusting heart failure medications. Case summary This report describes use of Boston Scientific’s HeartLogic algorithm, which is a multisensor device algorithm in implantable cardioverter-defibrillator devices that is proven to be an early predictor of heart failure decompensation by measuring several variables, including respiratory rate, nighttime heart rate, and heart sounds. We present three cases of patients who were actively surveilled by the various HeartLogic device algorithm sensors and were identified to have increasing respiratory rates high enough to trigger a HeartLogic alert prior to a positive COVID-19 diagnosis. Discussion We propose that the HeartLogic algorithm and its accompanying individual physiologic sensors demonstrate potential for use in identifying non-heart failure-related decompensation, such as COVID-19-positive diagnoses.

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