Evidence map›Paper›PMID 35219521›Full record

ArticleTransplantation proceedings2022

Intraoperative High Tidal Volume Ventilation and Postoperative Acute Respiratory Distress Syndrome in Liver Transplant.

Jun Yang, Drew Cheng, Ira Hofer, Christine Nguyen-Buckley, Andrew Disque, Christopher Wray, Victor W Xia

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Transplantation proceedings, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 38% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

7 authors at 1 institution in 1 country.

Jun YangDepartment of Critical Care Medicine, Yantaishan Hospital, Yantai, Shandong, P. R. China; Department of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Drew ChengDepartment of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Ira HoferDepartment of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Christine Nguyen-BuckleyDepartment of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Andrew DisqueDepartment of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Christopher WrayDepartment of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Victor W XiaDepartment of Anesthesiology and Perioperative Medicine, Ronald Reagan UCLA Medical Center, David Geffen School of Medicine at UCLA, Los Angeles, California, USA. Electronic address: vxia@mednet.ucla.edu.
Ronald Reagan UCLA Medical Center · US

Funding

Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical PatientsR01HL144692 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CANNESSON, MAXIME · 2019 to 2023
$3.5M
Biomedical Informatics Tools for Applied Perioperative PhysiologyR01EB029751 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CANNESSON, MAXIME · 2020 to 2023
$2.5M
Use of Machine Learning on Integrated Electronic Medical Record, Genetic andWaveform Data to Predict Perioperative Cardiorespiratory InstabilityK01HL150318 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HOFER, IRA · 2020 to 2024
$855k
NHLBI NIH HHS K01 HL150318NHLBI NIH HHS R01 HL144692NIBIB NIH HHS R01 EB029751
6 · The paper itself

Abstract

backgroundMechanical ventilation plays an important role in perioperative management and patient outcomes. Although mechanical ventilation with high tidal volume (HTV) is injurious in patients in the intensive care unit, the effects of HTV ventilation in patients undergoing liver transplant (LT) has not been reported. The aim of this study was to determine if intraoperative HTV ventilation was associated with the development of acute respiratory distress syndrome (ARDS).

methodsPatients undergoing LT between 2013 and 2018 at a tertiary medical center were reviewed. The tidal volume was recorded at 3 time points: after anesthesia induction, before liver reperfusion, and at the end of surgery. Patients were divided into 2 groups: HTV (>10 mL/kg predicted body weight [pBW]) and non-HTV (≤10 mL/kg pBW). The 2 groups were compared. Independent risk factors were identified by multivariable logistic models.

resultsOf 780 LT patients, 85 (10.9%) received HTV ventilation. Female sex and greater difference between actual body weight and pBW were independent risk factors for HTV ventilation. Patients who received HTV ventilation had a significantly higher incidence of ARDS (10.3% vs 3.9%; P = .01) than those who received non-HTV ventilation.

conclusionsIn this retrospective study, we showed that HTV ventilation during LT was common and was associated with a higher incidence of ARDS. Therefore, tidal volume should be carefully selected during LT surgery. More studies using a prospective randomized controlled design are needed.

Indexed as

Liver TransplantationRespiratory Distress SyndromeBody WeightFemaleHumansProspective StudiesRespiration, ArtificialRetrospective StudiesTidal Volume

Identifiers

PMID35219521
PMCPMC9699994
OpenAlexW4210818763

What OpenQuestion holds

Textmetadata
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.