Use of a Cardiac Scale to Predict Heart Failure Events: Design of SCALE-HF 1.

algorithms electric impedance heart failure hospitalization prospective studies

Journal

Circulation. Heart failure
ISSN: 1941-3297
Titre abrégé: Circ Heart Fail
Pays: United States
ID NLM: 101479941

Informations de publication

Date de publication:
05 2023
Historique:
medline: 18 5 2023
pubmed: 16 5 2023
entrez: 16 5 2023
Statut: ppublish

Résumé

There is a need for simple, noninvasive solutions to remotely monitor and predict worsening heart failure (HF) events. SCALE-HF 1 (Surveillance and Alert-Based Multiparameter Monitoring to Reduce Worsening Heart Failure Events) is a prospective, multicenter study that will develop and assess the accuracy of the heart function index-a composite algorithm of noninvasive hemodynamic biomarkers from a cardiac scale-in predicting worsening HF events. Approximately 300 patients with chronic HF and recent decompensation will be enrolled in this observational study for model development. Patients will be encouraged to take daily cardiac scale measurements. Approximately 50 HF events, defined as an urgent, unscheduled clinic, emergency department, or hospitalization for worsening HF will be used for model development. The composite index will be developed from hemodynamic biomarkers derived from ECG, ballistocardiogram, and impedance plethysmogram signals measured from the cardiac scale. Biomarkers of interest include weight, peripheral impedance, pulse rate and variability, and estimates of stroke volume, cardiac output, and blood pressure captured through the cardiac scale. The sensitivity, unexplained alert rate, and alerting time of the index in predicting worsening HF events will be evaluated and compared with the performance of simple weight-based rule-of-thumb algorithms (eg, weight increase of 3 lbs in 1 day or 5 lbs in 7 days) that are often used in practice. SCALE-HF 1 is the first study to develop and evaluate the performance of a composite index derived from noninvasive hemodynamic biomarkers measured from a cardiac scale in predicting worsening HF events. Subsequent studies will validate the heart function index and assess its ability to improve patient outcomes. URL: https://www. gov; Unique identifier: NCT04882449.

Sections du résumé

BACKGROUND
There is a need for simple, noninvasive solutions to remotely monitor and predict worsening heart failure (HF) events. SCALE-HF 1 (Surveillance and Alert-Based Multiparameter Monitoring to Reduce Worsening Heart Failure Events) is a prospective, multicenter study that will develop and assess the accuracy of the heart function index-a composite algorithm of noninvasive hemodynamic biomarkers from a cardiac scale-in predicting worsening HF events.
METHODS
Approximately 300 patients with chronic HF and recent decompensation will be enrolled in this observational study for model development. Patients will be encouraged to take daily cardiac scale measurements.
RESULTS
Approximately 50 HF events, defined as an urgent, unscheduled clinic, emergency department, or hospitalization for worsening HF will be used for model development. The composite index will be developed from hemodynamic biomarkers derived from ECG, ballistocardiogram, and impedance plethysmogram signals measured from the cardiac scale. Biomarkers of interest include weight, peripheral impedance, pulse rate and variability, and estimates of stroke volume, cardiac output, and blood pressure captured through the cardiac scale. The sensitivity, unexplained alert rate, and alerting time of the index in predicting worsening HF events will be evaluated and compared with the performance of simple weight-based rule-of-thumb algorithms (eg, weight increase of 3 lbs in 1 day or 5 lbs in 7 days) that are often used in practice.
CONCLUSIONS
SCALE-HF 1 is the first study to develop and evaluate the performance of a composite index derived from noninvasive hemodynamic biomarkers measured from a cardiac scale in predicting worsening HF events. Subsequent studies will validate the heart function index and assess its ability to improve patient outcomes.
REGISTRATION
URL: https://www.
CLINICALTRIALS
gov; Unique identifier: NCT04882449.

Identifiants

pubmed: 37192288
doi: 10.1161/CIRCHEARTFAILURE.122.010012
pmc: PMC10180020
doi:

Banques de données

ClinicalTrials.gov
['NCT04882449']

Types de publication

Observational Study Multicenter Study Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e010012

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Auteurs

Marat Fudim (M)

Duke Clinical Research Institute (M.F., A.D.D.), Duke University School of Medicine, Durham, NC.
Department of Medicine (M.F., A.D.D.), Duke University School of Medicine, Durham, NC.

Daniel Yazdi (D)

Division of Cardiology, Department of Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (D.Y.).
Bodyport Inc, San Francisco, CA (D.Y., C.C., K.O., S.S.).

Ugochukwu Egolum (U)

The Heart Center of Northeast Georgia Medical Center, Gainesville (U.E.).

Amir Haghighat (A)

Cardiovascular Institute of Northwest Florida, Panama City (A.H.).

Anupama Kottam (A)

Division of Cardiology, Wayne State University, Detroit, MI (A.K.).

Andrew J Sauer (AJ)

Saint Luke's Mid America Heart Institute, University of Missouri, Kansas City (A.J.S.).

Hirak Shah (H)

Department of Cardiovascular Medicine, University of Kansas Medical Center, Kansas City (H.S.).

Priya Kumar (P)

Division of Cardiology, Self Regional Healthcare, Greenwood, SC (P.K.).

Val Rakita (V)

Section of Advanced Heart Failure and Transplant, Temple Heart and Vascular Institute, Temple University, Philadelphia, PA (V.R.).

Corey Centen (C)

Bodyport Inc, San Francisco, CA (D.Y., C.C., K.O., S.S.).

Kivanc Ozonat (K)

Bodyport Inc, San Francisco, CA (D.Y., C.C., K.O., S.S.).

Sarah Smith (S)

Bodyport Inc, San Francisco, CA (D.Y., C.C., K.O., S.S.).

Adam D DeVore (AD)

Duke Clinical Research Institute (M.F., A.D.D.), Duke University School of Medicine, Durham, NC.
Department of Medicine (M.F., A.D.D.), Duke University School of Medicine, Durham, NC.

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Classifications MeSH