Evidence map›Paper›PMID 37566686›Full record

ArticleAnesthesiology2023

Electrical Impedance Tomography Identifies Evolution of Regional Perfusion in a Porcine Model of Acute Respiratory Distress Syndrome.

Kevin T Martin, Yi Xin, Timothy G Gaulton, Marcus Victor, Roberta R Santiago, Taehwan Kim, Caio C A Morais, Aubrey A Kazimi, Marc Connell, Sarah E Gerard and 11 more

Open access · greenAbstract read
In one paragraph

Article in Anesthesiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
5.9field-weighted citation impact, top 3% 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

16 citing papers in PubMed, 23 citations in OpenAlex.

  1. Review
  2. Article
  3. Electrical impedance tomography reveals ventilation and perfusion heterogeneity in infants with bronchopulmonary dysplasia.Journal of perinatology : official journal of the California Perinatal Association · 2026
    Article
  4. Article
  5. Dead space in critical care: a practical approach with clinical scenarios.Journal of anesthesia, analgesia and critical care · 2026
    Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. 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
  13. 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
  14. Review
  15. Article
  16. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors at 6 institutions in 2 countries.

Kevin T MartinDepartment of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, California; Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania.
Yi XinDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
Timothy G GaultonDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
Marcus VictorDepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Electronics Engineering Division, Aeronautics Institute of Technology, São Paulo, Brazil.
Roberta R SantiagoDepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
Taehwan KimDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania.
Caio C A MoraisDepartment of Physical Therapy, Federal University of Pernambuco, Recife, Brazil.
Aubrey A KazimiDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania.
Marc ConnellDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania; University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania.
Sarah E GerardRoy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa.
Jacob HerrmannRoy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa.
Ariel L MuellerDepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
Austin LenartDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania.
Jiacheng ShenDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania.
Sherbano S KhanDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania.
Mihail PetrovDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania.
Kristan ReutlingerDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania.
Karina RozenbergDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania.
Marcelo AmatoDepartment of Cardio-Pulmonary, University of São Paulo, São Paulo, Brazil.
Lorenzo BerraDepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
Maurizio CeredaDepartment of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
University of Pennsylvania · USHarvard University · USUniversity of Iowa · USUniversidade de São Paulo · BRUniversidade Federal de Pernambuco · BRUniversity of Pittsburgh · US

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
NHLBI NIH HHS R01 HL137389
6 · The paper itself

Abstract

backgroundBedside electrical impedance tomography could be useful to visualize evolving pulmonary perfusion distributions when acute respiratory distress syndrome worsens or in response to ventilatory and positional therapies. In experimental acute respiratory distress syndrome, this study evaluated the agreement of electrical impedance tomography and dynamic contrast-enhanced computed tomography perfusion distributions at two injury time points and in response to increased positive end-expiratory pressure (PEEP) and prone position.

methodsEleven mechanically ventilated (VT 8 ml · kg-1) Yorkshire pigs (five male, six female) received bronchial hydrochloric acid (3.5 ml · kg-1) to invoke lung injury. Electrical impedance tomography and computed tomography perfusion images were obtained at 2 h (early injury) and 24 h (late injury) after injury in supine position with PEEP 5 and 10 cm H2O. In eight animals, electrical impedance tomography and computed tomography perfusion imaging were also conducted in the prone position. Electrical impedance tomography perfusion (QEIT) and computed tomography perfusion (QCT) values (as percentages of image total) were compared in eight vertical regions across injury stages, levels of PEEP, and body positions using mixed-effects linear regression. The primary outcome was agreement between QEIT and QCT, defined using limits of agreement and Pearson correlation coefficient.

resultsPao2/Fio2 decreased over the course of the experiment (healthy to early injury, -253 [95% CI, -317 to -189]; early to late injury, -88 [95% CI, -151 to -24]). The limits of agreement between QEIT and QCT were -4.66% and 4.73% for the middle 50% quantile of average regional perfusion, and the correlation coefficient was 0.88 (95% CI, 0.86 to 0.90]; P < 0.001). Electrical impedance tomography and computed tomography showed similar perfusion redistributions over injury stages and in response to increased PEEP. QEIT redistributions after positional therapy underestimated QCT in ventral regions and overestimated QCT in dorsal regions.

conclusionsElectrical impedance tomography closely approximated computed tomography perfusion measures in experimental acute respiratory distress syndrome, in the supine position, over injury progression and with increased PEEP. Further validation is needed to determine the accuracy of electrical impedance tomography in measuring perfusion redistributions after positional changes. EDITOR’S PERSPECTIVE:

Indexed as

Respiratory Distress SyndromeTomography, X-Ray ComputedAnimalsElectric ImpedanceFemaleLungMalePerfusionSwineTomography

Identifiers

PMID37566686
PMCPMC10840641
OpenAlexW4385753868

What Socratic 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 Socratic graph.