Dataset Information

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

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.

SUBMITTER: Yapejian AR

PROVIDER: S-EPMC7896811 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Yapejian Andrea Rebecca AR Fudim Marat M

European heart journal. Case reports 20210220 2

<h4>Background</h4>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.<h4>Case summary</h4>This report describes ...[more]

PMID: 33644668

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Project description:BACKGROUND:The proliferation of wearable devices that collect activity and heart rate data has facilitated new ways to measure sleeping and waking durations unobtrusively and longitudinally. Most existing sleep/wake identification algorithms are based on activity only and are trained on expensive and laboriously annotated polysomnography (PSG). Heart rate can also be reflective of sleep/wake transitions, which has motivated its investigation herein in an unsupervised algorithm. Moreover, it is necessary to develop a personalized approach to deal with interindividual variance in sleep/wake patterns. OBJECTIVE:We aimed to develop an unsupervised personalized sleep/wake identification algorithm using multifaceted data to explore the benefits of incorporating both heart rate and activity level in these types of algorithms and to compare this approach's output with that of an existing commercial wearable device's algorithms. METHODS:In this study, a total of 14 community-dwelling older adults wore wearable devices (Fitbit Alta; Fitbit Inc) 24 hours a day and 7 days a week over period of 3 months during which their heart rate and activity data were collected. After preprocessing the data, a model was developed to distinguish sleep/wake states based on each individual's data. We proposed the use of hidden Markov models and compared different modeling schemes. With the best model selected, sleep/wake patterns were characterized by estimated parameters in hidden Markov models, and sleep/wake states were identified. RESULTS:When applying our proposed algorithm on a daily basis, we found there were significant differences in estimated parameters between weekday models and weekend models for some participants. CONCLUSIONS:Our unsupervised approach can be effectively implemented based on an individual's multifaceted sleep-related data from a commercial wearable device. A personalized model is shown to be necessary given the interindividual variability in estimated parameters.

Dataset Information

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

Background

Case summary

Discussion

Publications

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

Similar Datasets

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