Evidence map›Paper›PMID 39283362›Full record

ArticleThe ultrasound journal2024

Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure.

Cristiana Baloescu, Alvin Chen, Alexander Varasteh, Jane Hall, Grzegorz Toporek, Shubham Patil, Robert L McNamara, Balasundar Raju, Christopher L Moore

Abstract read
In one paragraph

Article in The ultrasound journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Observational
  2. Review
  3. Review
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

9 authors.

Cristiana BaloescuDepartment of Emergency Medicine, Yale University School of Medicine, 464 Congress Avenue, Suite 260, New Haven, Connecticut, 06519, USA. Cristiana.Baloescu@yale.edu.ORCID http://orcid.org/0000-0001-7012-1260
Alvin ChenPhilips Research Americas, 222 Jacobs Street, Cambridge, MA, 02141, USA.
Alexander VarastehDepartment of Emergency Medicine, Yale University School of Medicine, 464 Congress Avenue, Suite 260, New Haven, Connecticut, 06519, USA.
Jane HallDepartment of Emergency Medicine, University of Washington, Seattle, WA, USA.
Grzegorz ToporekPhilips Research Americas, 222 Jacobs Street, Cambridge, MA, 02141, USA.
Shubham PatilPhilips Research Americas, 222 Jacobs Street, Cambridge, MA, 02141, USA.
Robert L McNamaraDivision of Cardiology, Department of Internal Medicine, Yale University School of Medicine, PO Box 208017, New Haven, CT, 06520, USA.
Balasundar RajuPhilips Research Americas, 222 Jacobs Street, Cambridge, MA, 02141, USA.
Christopher L MooreDepartment of Emergency Medicine, Yale University School of Medicine, 464 Congress Avenue, Suite 260, New Haven, Connecticut, 06519, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863Philips Research Americas Philips Research Americas
6 · The paper itself

Abstract

backgroundUltrasound can detect fluid in the alveolar and interstitial spaces of the lung using the presence of artifacts known as B-lines. The aim of this study was to determine whether a deep learning algorithm generated B-line severity score correlated with pulmonary congestion and disease severity based on clinical assessment (as identified by composite congestion score and Rothman index) and to evaluate changes in the score with treatment. Patients suspected of congestive heart failure underwent daily ultrasonography. Eight lung zones (right and left anterior/lateral and superior/inferior) were scanned using a tablet ultrasound system with a phased-array probe. Mixed effects modeling explored the association between average B-line score and the composite congestion score, and average B-line score and Rothman index, respectively. Covariates tested included patient and exam level data (sex, age, presence of selected comorbidities, baseline sodium and hemoglobin, creatinine, vital signs, oxygen delivery amount and delivery method, diuretic dose).

resultsAnalysis included 110 unique subjects (3379 clips). B-line severity score was significantly associated with the composite congestion score, with a coefficient of 0.7 (95% CI 0.1-1.2 p = 0.02), but was not significantly associated with the Rothman index.

conclusionsUse of this technology may allow clinicians with limited ultrasound experience to determine an objective measure of B-line burden.

Indexed as

Artificial intelligenceB-linesHeart failureLung ultrasoundPoint-of-care ultrasound

Identifiers

PMID39283362
PMCPMC11405569

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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 Socratic graph.