Evidence map›Paper›PMID 29330154›Full record

ArticleCirculation. Heart failure2018

Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients.

Omer T Inan, Maziyar Baran Pouyan, Abdul Q Javaid, Sean Dowling, Mozziyar Etemadi, Alexis Dorier, J Alex Heller, A Ozan Bicen, Shuvo Roy, Teresa De Marco and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Circulation. Heart failure, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers.

0numbers the graph read from it
0cells of the map it votes in
88citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

88 citing papers in PubMed.

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28 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Omer T InanFrom the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.). inan@gatech.edu.
Maziyar Baran PouyanFrom the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
Abdul Q JavaidFrom the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
Sean DowlingFrom the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
Mozziyar EtemadiFrom the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
Alexis DorierFrom the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
J Alex HellerFrom the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
A Ozan BicenFrom the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
Shuvo RoyFrom the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
Teresa De MarcoFrom the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).
Liviu KleinFrom the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta (O.T.I., M.B.P., A.Q.J., A.D., A.O.B.); Division of Cardiology (S.D., T.D.M., L.K.) and Department of Bioengineering and Therapeutic Sciences (S.R.), University of California, San Francisco; and Department of Anesthesiology and Department of Biomedical Engineering, Northwestern University, Chicago, IL (M.E., J.A.H.).

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007618 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ANDERSON, MARK S · 1985 to 2020
$29.6M
Non-invasive biosensors to detect cardiovascular changes in heart failureR01HL130619 · NHLBI · GEORGIA INSTITUTE OF TECHNOLOGY · PI INAN, OMER TOLGA, KLEIN, LIVIU · 2016 to 2020
$2.9M
Wearable Cardiomechanics Monitor to Decrease Heart Failure ReadmissionsR56AG048458 · NIA · GEORGIA INSTITUTE OF TECHNOLOGY · PI INAN, OMER TOLGA, KLEIN, LIVIU · 2015 to 2015
$616k
NHLBI NIH HHS R01 HL130619NIA NIH HHS R56 AG048458NIGMS NIH HHS T32 GM007618
6 · The paper itself

Abstract

backgroundRemote monitoring of patients with heart failure (HF) using wearable devices can allow patient-specific adjustments to treatments and thereby potentially reduce hospitalizations. We aimed to assess HF state using wearable measurements of electrical and mechanical aspects of cardiac function in the context of exercise. METHODS AND

resultsPatients with compensated (outpatient) and decompensated (hospitalized) HF were fitted with a wearable ECG and seismocardiogram sensing patch. Patients stood at rest for an initial recording, performed a 6-minute walk test, and then stood at rest for 5 minutes of recovery. The protocol was performed at the time of outpatient visit or at 2 time points (admission and discharge) during an HF hospitalization. To assess patient state, we devised a method based on comparing the similarity of the structure of seismocardiogram signals after exercise compared with rest using graph mining (graph similarity score). We found that graph similarity score can assess HF patient state and correlates to clinical improvement in 45 patients (13 decompensated, 32 compensated). A significant difference was found between the groups in the graph similarity score metric (44.4±4.9 [decompensated HF] versus 35.2±10.5 [compensated HF];

conclusionsWearable technologies recording cardiac function and machine learning algorithms can assess compensated and decompensated HF states by analyzing cardiac response to submaximal exercise. These techniques can be tested in the future to track the clinical status of outpatients with HF and their response to pharmacological interventions.

Indexed as

AlgorithmsMachine LearningWearable Electronic DevicesAdultAgedElectrocardiographyEquipment DesignExerciseFemaleHeart FailureHumansKinetocardiographyMaleMiddle Agedheart failurehospitalizationoutpatientwalk testwearable electronic devices

Identifiers

PMID29330154
PMCPMC5769154

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.