Evidence map›Paper›PMID 29644056›Full record

ArticleClinical kidney journal2018

Lung ultrasonography in end-stage renal disease: moving from evidence to practice-a narrative review.

Daniel W Ross, Mohammed M Abbasi, Kenar D Jhaveri, Mala Sachdeva, Ilene Miller, Richard Barnett, Mangala Narasimhan, Paul Mayo, Massini Merzkani, Anna T Mathew

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Clinical kidney journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07028060 (The Detection of Lung Pathologies Among Dialysis Patients Using Novel Mobile-Phone Ultrasound - A Feasibility Pilot Study), which is not on this map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
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.

NCT07028060 nacompletednot on this mapstarted 2022, after this paper: background citation

The Detection of Lung Pathologies Among Dialysis Patients Using Novel Mobile-Phone Ultrasound - A Feasibility Pilot Study

TypeinterventionalSponsorPulseNmoreRan2022 to 2023Enrolled10ConditionsHemodialysis PatientsArmsPulseNmore MC™
3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Effect of Machine Settings on Ultrasound Assessment of B-lines.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2020
    Article
  9. Review
  10. Observational
  11. Article
  12. The newClinical kidney journal · 2019
    Article
  13. Article
  14. 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

10 authors at 3 institutions in 1 country.

Daniel W RossDivision of Nephrology, North Shore University Hospital and Long Island Jewish Medical Center and Hofstra Northwell School of Medicine, Great Neck, NY, USA.
Mohammed M AbbasiDivision of Pulmonary and Critical Care, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, USA.
Kenar D JhaveriDivision of Nephrology, North Shore University Hospital and Long Island Jewish Medical Center and Hofstra Northwell School of Medicine, Great Neck, NY, USA.
Mala SachdevaDivision of Nephrology, North Shore University Hospital and Long Island Jewish Medical Center and Hofstra Northwell School of Medicine, Great Neck, NY, USA.
Ilene MillerDivision of Nephrology, North Shore University Hospital and Long Island Jewish Medical Center and Hofstra Northwell School of Medicine, Great Neck, NY, USA.
Richard BarnettDivision of Nephrology, North Shore University Hospital and Long Island Jewish Medical Center and Hofstra Northwell School of Medicine, Great Neck, NY, USA.
Mangala NarasimhanDepartment of Internal Medicine, North Shore University Hospital and Long Island Jewish Medical Center and Hofstra Northwell School of Medicine, Great Neck, NY, USA.
Paul MayoDepartment of Internal Medicine, North Shore University Hospital and Long Island Jewish Medical Center and Hofstra Northwell School of Medicine, Great Neck, NY, USA.
Massini MerzkaniDivision of Nephrology, North Shore University Hospital and Long Island Jewish Medical Center and Hofstra Northwell School of Medicine, Great Neck, NY, USA.
Anna T MathewDivision of Nephrology, North Shore University Hospital and Long Island Jewish Medical Center and Hofstra Northwell School of Medicine, Great Neck, NY, USA.
North Shore University Hospital · USLong Island Jewish Medical Center · USAlbert Einstein College of Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditionally, point of care ultrasonography in nephrology has been used for renal biopsies and dialysis line placement. However, there is an emerging literature supporting the value of point of care lung ultrasonography in the assessment of volume status for dialysis patients. We conducted a review and identified 12 studies that examined the utility of lung ultrasonography in assessing volume status in patients with end-stage renal disease. We conclude that lung ultrasonography can be used to determine volume status in chronic dialysis patients by identifying lung congestion using the B-line score. Incorporating this technique into practice may have significant diagnostic and prognostic value for this high-risk population, as it provides the nephrologist with a useful bedside technique to assess extravascular lung water. Developing competence in lung ultrasonography is straightforward. The nephrology community should consider adding this useful tool into fellowship training, paralleling its broader use in other internal medicine specialties.

Indexed as

fellowshiplung ultrasonographylung ultrasonography in nephrologyultrasonography in dialysisvolume assessment

Identifiers

PMID29644056
PMCPMC5887421
OpenAlexW2758434183

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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.