Evidence map›Paper›PMID 42758152›Full record

ReviewESC heart failure2026

Remote dielectric sensing for pulmonary congestion assessment: current concepts and clinical perspectives.

Teruhiko Imamura

Abstract readReview
In one paragraph

Review in ESC heart failure, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Teruhiko ImamuraThe Second Department of Internal Medicine, University of Toyama, 2630 Sugitani, Toyama, Toyama 930-0194, Japan.ORCID 0000-0002-7294-7637

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pulmonary congestion is a major determinant of symptoms, hospitalization, and prognosis in cardiovascular disease, yet its quantitative assessment remains challenging. Remote dielectric sensing is a non-invasive electromagnetic method that provides a quantitative estimate of pulmonary fluid content within ∼1 min. This narrative review summarizes the principles, validation, clinical interpretation, applications, and limitations of remote dielectric sensing. In a validation study of 46 hospitalized patients, including 28 with heart failure, remote dielectric sensing correlated with computed tomography-derived high-attenuation lung area (r = 0.65, P < .001). In a prospective study of 153 patients with heart failure, remote dielectric sensing correlated with lung ultrasound B-line count (r = 0.544); a threshold of 34.5% identified lung ultrasound-defined pulmonary congestion with an area under the curve of 0.748, sensitivity of 73.5%, and specificity of 70.2%. In 133 patients hospitalized with acute heart failure, with a median age of 78 years, 59% men, and a median left ventricular ejection fraction of 57%, an increase in remote dielectric sensing from admission to discharge was associated with all-cause death or heart failure rehospitalisation (adjusted hazard ratio 4.37, 95% confidence interval 1.13-16.81). In a proof-of-concept randomized trial of 100 patients hospitalized with acute decompensated heart failure, the primary endpoint occurred in 2% with remote dielectric sensing-guided care vs 20% with routine care (hazard ratio 0.094). Available evidence supports diagnostic validity and potential prognostic value, but external validation remains limited. Evidence that remote dielectric sensing-guided management improves clinical outcomes remains preliminary. Remote dielectric sensing should be considered a complementary component of multimodality congestion assessment.

Indexed as

Heart FailurePulmonary EdemaRemote Sensing TechnologyHumansPrognosisStroke VolumeCongestionDiureticsHeart failureReDS

Identifiers

PMID42758152
PMCPMC13614269

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.