Evidence map›Paper›PMID 40984985›Full record

ArticleEuropean heart journal. Digital health2025

Congestion assessment using a self-supervised contrastive learning-derived risk index in patients with congestive heart failure (CONAN): protocol and design of a prospective cohort study.

Bojan Hartmann, Niels-Ulrik Hartmann, Julia Brandts, Marlo Verket, Nikolaus Marx, Niveditha Dinesh, Lisa Schuetze, Anna Emilia Pape, Dirk Müller-Wieland, Markus Kollmann and 8 more

Abstract read
In one paragraph

Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

18 authors.

Bojan HartmannDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.
Niels-Ulrik HartmannDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.
Julia BrandtsDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.ORCID https://orcid.org/0000-0002-1141-1126
Marlo VerketDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.ORCID https://orcid.org/0000-0002-3361-3722
Nikolaus MarxDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.ORCID https://orcid.org/0000-0001-6141-634X
Niveditha DineshDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.
Lisa SchuetzeDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.
Anna Emilia PapeDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.
Dirk Müller-WielandDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.
Markus KollmannDepartment of Biology, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.
Katharina Marx-SchüttDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.
Martin BergerDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.
Andreas PuetzDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.
Felix MichelsDepartment of Biology, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.
Luca Leon HappelDepartment of Haematology, Oncology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.
Lars MüllerDepartment of Haematology, Oncology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.
Guido KobbeDepartment of Haematology, Oncology, and Clinical Immunology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.
Malte JacobsenDepartment of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, Aachen 52074, Germany.ORCID https://orcid.org/0000-0003-0780-0003

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Recurrent congestive episodes are a primary cause of hospitalizations in patients with heart failure. Hitherto, outpatient management adopts a reactive approach, assessing patients clinically through frequent follow-up visits to detect congestion early. This study aims to assess the capabilities of a self-supervised contrastive learning-derived risk index to detect episodes of acute decompensated heart failure (ADHF) in patients using continuously recorded wearable time-series data. Methods and results: This is the protocol for a single-arm, prospective cohort pilot study that will include 290 patients with ADHF. Acute decompensated heart failure is diagnosed by clinical signs and symptoms, as well as additional diagnostics (e.g. NT-proBNP). Patients will receive standard-of-care treatment, supplemented by continuous wearable-based monitoring of vital signs and physical activity, and are followed for 90 days. During follow-up, study visits will be conducted and presentations without clinical ADHF will be referred to as 'regular' and data from these episodes will be presented to a deep neural network that is trained by a self-supervised contrastive learning objective to extract features from the time-series that are typical in regular periods. The model is used to calculate a risk index measuring the dissimilarity of observed features from those of regular periods. The primary outcome of this study will be the risk index's accuracy in detecting episodes with ADHF. As secondary outcome data integrity and the score in the validated questionnaire System Usability Scale will be evaluated. Conclusion: Demonstrating reliable congestion detection through continuous monitoring with a wearable and self-supervised contrastive learning could assist in pre-emptive heart failure management in clinical care. Clinical trial registration: The study was registered in the German clinical trials register (DRKS00034502).

Indexed as

Acute decompensated heart failureCongestive heart failureRemote patient monitoringSelf-supervised contrastive learningWearable

Identifiers

PMID40984985
PMCPMC12450524

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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.