HeartLogic Multisensor Algorithm Identifies Patients During Periods of Significantly Increased Risk of Heart Failure Events

Gardner, Roy S.; Singh, Jagmeet P.; Stančák, B; Nair, Devi G.; Cao, Michael; Schulze, Christopher; Thakur, Pramodsingh H.; An, Q.; Wehrenberg, Scott; Hammill, Eric; Zhang, Yi; Boehmer, John

doi:10.1161/circheartfailure.117.004669

Cited by 86 publications

(79 citation statements)

References 24 publications

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“…This finding provides further evidence of the ability of the index to stratify risk, because patients at higher risk of HF events are plausibly those in worse clinical condition at the baseline. A sub‐analysis of the MultiSENSE study showed that HeartLogic maintained its predictive value for HF events even after correction for baseline variables in a multivariate model that included ejection fraction, New York Heart Association class, and diabetes, together with other variables.…”

Section: Discussionsupporting

confidence: 86%

Preliminary experience with the multisensor HeartLogic algorithm for heart failure monitoring: a retrospective case series report

et al. 2019

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Aims In the Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients study, a novel algorithm for heart failure (HF) monitoring was implemented. The HeartLogic (Boston Scientific) index combines data from multiple implantable cardioverter defibrillator (ICD)‐based sensors and has proved to be a sensitive and timely predictor of impending HF decompensation. The remote monitoring of HF patients by means of HeartLogic has never been described in clinical practice. We report post‐implantation data collected from sensors, the combined index, and their association with clinical events during follow‐up in a group of patients who received a HeartLogic‐enabled device in clinical practice. Methods and results Patients with ICD and cardiac resynchronization therapy ICD were remotely monitored. In December 2017, the HeartLogic feature was activated on the remote monitoring platform, and multiple ICD‐based sensor data collected since device implantation were made available: HeartLogic index, heart rate, heart sounds, thoracic impedance, respiration, and activity. Their association with clinical events was retrospectively analysed. Data from 58 patients were analysed. During a mean follow‐up of 5 ± 3 months, the HeartLogic index crossed the threshold value (set by default to 16) 24 times (over 24 person‐years, 0.99 alerts/patient‐year) in 16 patients. HeartLogic alerts preceded five HF hospitalizations and five unplanned in‐office visits for HF. Symptoms or signs of HF were also reported at the time of five scheduled visits. The median early warning time and the time spent in alert were longer in the case of hospitalizations than in the case of minor events of clinical deterioration of HF. HeartLogic contributing sensors detected changes in heart sound amplitude (increased third sound and decreased first sound) in all cases of alerts. Patients with HeartLogic alerts during the observation period had higher New York Heart Association class ( P = 0.025) and lower ejection fraction ( P = 0.016) at the time of activation. Conclusions Our retrospective analysis indicates that the HeartLogic algorithm might be useful to detect gradual worsening of HF and to stratify risk of HF decompensation.

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Section: Discussionsupporting

confidence: 86%

Preliminary experience with the multisensor HeartLogic algorithm for heart failure monitoring: a retrospective case series report

et al. 2019

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“…The P2P app designed in this study had 4 core patient-facing components: a heart health score calculated from CIED data (a fictitious concept inspired by existing research [ 53 , 54 ]), self-assessments on recommended heart failure self-care domains (eg, medication use, sodium-restricted diet), tips and strategies for better self-management, and logs of data captured by the app. We designed and tested different implementations of these core concepts, changing information architecture and amending feature sets as we received feedback from study participants.…”

Section: Methodsmentioning

confidence: 99%

Untold Stories in User-Centered Design of Mobile Health: Practical Challenges and Strategies Learned From the Design and Evaluation of an App for Older Adults With Heart Failure

Cornet¹,

Toscos²,

Bolchini³

et al. 2020

JMIR Mhealth Uhealth

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Background User-centered design (UCD) is a powerful framework for creating useful, easy-to-use, and satisfying mobile health (mHealth) apps. However, the literature seldom reports the practical challenges of implementing UCD, particularly in the field of mHealth. Objective This study aims to characterize the practical challenges encountered and propose strategies when implementing UCD for mHealth. Methods Our multidisciplinary team implemented a UCD process to design and evaluate a mobile app for older adults with heart failure. During and after this process, we documented the challenges the team encountered and the strategies they used or considered using to address those challenges. Results We identified 12 challenges, 3 about UCD as a whole and 9 across the UCD stages of formative research, design, and evaluation. Challenges included the timing of stakeholder involvement, overcoming designers’ assumptions, adapting methods to end users, and managing heterogeneity among stakeholders. To address these challenges, practical recommendations are provided to UCD researchers and practitioners. Conclusions UCD is a gold standard approach that is increasingly adopted for mHealth projects. Although UCD methods are well-described and easily accessible, practical challenges and strategies for implementing them are underreported. To improve the implementation of UCD for mHealth, we must tell and learn from these traditionally untold stories.

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“…A post-hoc analysis of the MultiSENSE data by Gardner and colleagues showed that a HeartLogic TM index of ≥16 compared with a HeartLogic TM index <16 was associated with a 10.6-fold higher heart failure event ratio (0.80 events per patient year "in alert" state (HeartLogic TM index ≥ 16) compared with 0.08 events per patient year "out of alert" state) [38]. Increased NT-Pro BNP (N-terminal B-type natriuretic peptide) levels (defined as > 1000 pg/mL) at baseline were also associated with a significant risk for heart failure events.…”

Section: Studies Published To Datementioning

confidence: 99%

Early Detection of Fluid Retention in Patients with Advanced Heart Failure: A Review of a Novel Multisensory Algorithm, HeartLogicTM

Feijen

Egorova

Beeres

et al. 2021

Sensors

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Heart failure (HF) hospitalisations due to decompensation are associated with shorter life expectancy and lower quality of life. These hospitalisations pose a significant burden on the patients, doctors and healthcare resources. Early detection of an upcoming episode of decompensation may facilitate timely optimisation of the ambulatory medical treatment and thereby prevent heart-failure-related hospitalisations. The HeartLogicTM algorithm combines data from five sensors of cardiac implantable electronic devices into a cumulative index value. It has been developed for early detection of fluid retention in heart failure patients. This review aims to provide an overview of the current literature and experience with the HeartLogicTM algorithm, illustrate how the index can be implemented in daily clinical practice and discuss ongoing studies and potential future developments of interest.

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HeartLogic Multisensor Algorithm Identifies Patients During Periods of Significantly Increased Risk of Heart Failure Events

Abstract: https://www.clinicaltrials.gov. Unique identifier: NCT01128166.

Cited by 86 publications

References 24 publications

Preliminary experience with the multisensor HeartLogic algorithm for heart failure monitoring: a retrospective case series report

Preliminary experience with the multisensor HeartLogic algorithm for heart failure monitoring: a retrospective case series report

Untold Stories in User-Centered Design of Mobile Health: Practical Challenges and Strategies Learned From the Design and Evaluation of an App for Older Adults With Heart Failure

Early Detection of Fluid Retention in Patients with Advanced Heart Failure: A Review of a Novel Multisensory Algorithm, HeartLogicTM

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