Evidence map›Paper›PMID 38588918›Full record

ReviewNitric oxide : biology and chemistry2024

Imaging the pulmonary vasculature in acute respiratory distress syndrome.

Timothy G Gaulton, Yi Xin, Marcus Victor, Alice Nova, Maurizio Cereda

Open access · greenAbstract readReview
In one paragraph

Review in Nitric oxide : biology and chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.3field-weighted citation impact, top 13% 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.

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

6 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Review
  3. Dead space in critical care: a practical approach with clinical scenarios.Journal of anesthesia, analgesia and critical care · 2026
    Review
  4. Article
  5. Noise effect analysis and pulmonary perfusion estimation in electrical impedance tomography.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024
    Article
  6. Time-domain and 3D methods for lung perfusion data clustering in electrical impedance tomography.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024
    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

5 authors at 2 institutions in 2 countries.

Timothy G GaultonDepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA. Electronic address: tgaulton@mgh.harvard.edu.
Yi XinDepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA.
Marcus VictorDepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA; Electronics Engineering Division, Aeronautics Institute of Technology, Sao Paulo, Brazil.
Alice NovaDepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA.
Maurizio CeredaDepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Boston, MA, USA.
Harvard University · USInstituto Tecnológico de Aeronáutica · BR

Funding

An Integrated Approach to Predict and Improve the Outcomes of Lung InjuryR01HL137389 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI CEREDA, MAURIZIO FRANCO · 2018 to 2021
$3.0M
Imaging the Pulmonary Circulation to Aid Personalized Management of Acute Respiratory Distress SyndromeR01HL171199 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Maurizio Franco Cereda · 2024 to 2026
$2.4M
NHLBI NIH HHS R01 HL137389NHLBI NIH HHS R01 HL171199
6 · The paper itself

Abstract

Acute respiratory distress syndrome (ARDS) is characterized by a redistribution of regional lung perfusion that impairs gas exchange. While speculative, experimental evidence suggests that perfusion redistribution may contribute to regional inflammation and modify disease progression. Unfortunately, tools to visualize and quantify lung perfusion in patients with ARDS are lacking. This review explores recent advances in perfusion imaging techniques that aim to understand the pulmonary circulation in ARDS. Dynamic contrast-enhanced computed tomography captures first-pass kinetics of intravenously injected dye during continuous scan acquisitions. Different contrast characteristics and kinetic modeling have improved its topographic measurement of pulmonary perfusion with high spatial and temporal resolution. Dual-energy computed tomography can map the pulmonary blood volume of the whole lung with limited radiation exposure, enabling its application in clinical research. Electrical impedance tomography can obtain serial topographic assessments of perfusion at the bedside in response to treatments such as inhaled nitric oxide and prone position. Ongoing technological improvements and emerging techniques will enhance lung perfusion imaging and aid its incorporation into the care of patients with ARDS.

Indexed as

LungRespiratory Distress SyndromeAnimalsHumansPerfusion ImagingPulmonary CirculationTomography, X-Ray Computed

Identifiers

PMID38588918
PMCPMC11253040
OpenAlexW4394014925

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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 OpenQuestion graph.