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CONgestion Detection using the monti®-PlAtform with an Integrated Self-Supervised Contrastive Learning-Derived Risk Index in Patients with CoNgestive Heart Failure

CONgestion Detection using the monti®-PlAtform with an Integrated Self-Supervised Contrastive Learning-Derived Risk Index in Patients with CoNgestive Heart Failure - CONAN

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
DRKS
Registry ID
DRKS00034502
Enrollment
282
Registered
2025-03-04
Start date
2025-09-03
Completion date
Unknown
Last updated
2026-03-30

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

I50

Interventions

Group 1: Patients with Congestive Heart Failure under continuous monitoring via monti®-Platform for 90 days

Sponsors

Medizinische Fakultät der RWTH Aachen
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: - Acute-decompensated heart failure confirmed within the last 24 hours - Age >= 18 years and able to understand design and objectives of the trial - Signed written informed consent and data safety agreement before any study-related activities - Willingness to wear the wearable during the treatment period and return it on a follow-up visit

Exclusion criteria

Exclusion criteria: - Active malignant disease - Pregnancy - Active severe systemic infection, defined as intravenous administration of antibiotics - End stage renal disease with dialysis - Medical or mental conditions, such as dementia, that hinder the continuous use of the monitoring platform and associated equipment during the monitoring period - Any condition that makes wearing the wearable device impossible (e.g., presence of a shunt, active bleeding, or absence of limbs) - Mental incapacity or language barriers which preclude adequate understanding or cooperation, known or suspected not to comply with study directives or not to be reliable or trustworthy, or subject who in the opinion of the investigator should not participate in the study

Design outcomes

Primary

MeasureTime frame
- Calculating AUROC ("Area Under the Receiver Operating Characteristics curve") to assess the detection capabilities of episodes with ADHF by the self-supervised contrastive learning-derived risk index based on wearable recorded time series

Secondary

MeasureTime frame
- Assessing data integrity of the monti®-platform and quality of the mobile monti-app (mean % of wearable data per day, time difference between logged time point of measurement and database, score in Mobile App Rating Scale (MARS-G))

Countries

Germany

Contacts

Public ContactMalte Jacobsen

Medizinische Klinik I der Uniklinik RWTH Aachen

mjacobsen@ukaachen.de+49 (0)241 80-0

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Apr 4, 2026