International Multi-center Study to Validate an Early Warning Algorithm for Worsening Heart Failure

NCT04758429 | Unknown

• 1 other identifier: PREDIGMA001
• Study Type: observational
• Target: 552
• Locations: 0 countries
• Sites: N/A

Brief Summary

The study is a multi-center, prospective, non-randomized, observational study to collect data to develop and validate a machine learning algorithm for early detection of worsening heart failure events using multi-parametric sensor data from wearable data capture device The VESTA study will enroll up to 552 subjects in up to 25 centers in order to collect data on a total of at least 56 worsening heart failure events (independently adjudicated hospitalizations or unscheduled intravenous administration of decongestive drugs).The duration of follow-up per participant will be between 3-6 months.

Conditions

Chronic Heart Failure

Outcome Measures

Primary Outcomes (1)

• Detection of worsening heart failure (WHF) event

  Sensitivity and Specificity of detection of WHF event by a machine-learning algorithm system using multi-parametric data captured by non-invasive telemonitoring. WHF event definition: * Any hospital admission for WHF(e.g. due to new or progressive symptoms/signs of decompensated HF including significant weight gain, worsening dyspnea or fatigue, newly elevated jugular venous pressure, new cardiac S3 gallop rhythm, the development of pulmonary rales, hepatic congestion or lower extremity edema), OR * Any administration of intravenous HF therapy in an unscheduled setting (e.g. clinic/office/emergency department, ED).

  6 months

Secondary Outcomes (7)

• Hospitalizations for HF that did not meet WHF

  6 months

• HF-hospitalization in number of days

  6 months

• Outpatient health encounters leading to changes or adjustments of oral HF medication

  6 months

• Other healthcare encounters do not meet criteria of WHF event, points a, b and c but are deemed related to HF

  6 months

• Number of Adverse and Serious Adverse Events known to be associated with wearable devices with sensors

  Occuring during the 6 month follow-up

Other Outcomes (4)

• All-cause hospitalizations

  6 months

• Deaths (Heart failure attributed)

  6 months

• Deaths (all-cause)

  6 months

Study Arms (2)

Development or Training Cohort

will provide the data to be used for algorithm development and training.

Device: Machine-learning Algorithm

Test or Validation Cohort

will provide data to be used for algorithm validation.

Device: Machine-learning Algorithm

Interventions

Machine-learning Algorithm DEVICE

System technology/Software

Development or Training Cohort; Test or Validation Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study will be enrolling male and female patients with history of chronic heart failure.

You may qualify if:

• years of age and above
• New York Heart Association(NYHA) functional class II-IV
• Reduced ejection fraction (HFrEF) with left ventricular ejection fraction (LVEF) ≤40%
• History of chronic HF as evidenced by
• LVEF ≤40% measured at least 30 days before enrollment
• At increased risk for HF decompensation as defined by
• currently inpatient with hospital admission due to HF decompensation (acute on chronic heart failure), or
• history of hospitalization for HF decompensation in the last 6 months
• Understands the study requirements and the study procedures and provides written informed consent before any trial-specific tests or procedures are performed

You may not qualify if:

• Known allergy to any component of the study device (cotton, elastane, polyester)
• Current HF hospitalization due to acute de novo heart failure
• Current HF decompensation due to triggers like surgery or perioperative complications, pulmonary embolic episode or acute myocardial infarction
• Severe chronic kidney disease with glomerular filtration rate (GFR) \<30 ml/ min/1.73 m2 and/or renal replacement therapy
• Evidence of hepatic disease as determined by any one of the following: serum glutamic-oxaloacetic transaminase (SGOT/AST) or serum glutamic-pyruvic transaminase (SGPT/ALT) values exceeding 3x upper limit of normal, bilirubin \>1.5 mg/dl
• Body mass index (BMI)\>35 kg/m2
• Severe uncorrected valvular heart disease, or hypertrophic obstructive cardiomyopathy, active myocarditis, constrictive pericarditis, or clinically significant congenital heart disease
• Receiving mechanical circulatory support
• Candidates on heart transplant list
• Factors or conditions, that according to investigator assessment may affect compliance with protocol or cause confound data interpretation. Examples may include but are not limited to:
• Other serious medical illness (e.g., cancer) with estimated life expectancy of less than 12 months.
• Current problems with substance addiction
• Participating in another investigational drug or device clinical trial
• Pregnant or nursing

Sponsors & Collaborators

• Chronolife: lead

Central Study Contacts

Zarifah Reed, MD

CONTACT

+33665038601; zreed@chronolife.net

Dominique Dury

CONTACT

ddury@chronolife.net

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 8, 2021
• First Posted: February 17, 2021
• Study Start: May 31, 2021
• Primary Completion: May 31, 2022
• Study Completion: May 31, 2022
• Last Updated: February 17, 2021

Record last verified: 2021-02

Data Sharing

• IPD Sharing: Will not share