Evidence map›Paper›PMID 35306614›Full record

ArticleUpdates in surgery2022

Graft weight integration in the early allograft dysfunction formula improves the prediction of early graft loss after liver transplantation.

Tommaso Maria Manzia, Quirino Lai, Hermien Hartog, Virginia Aijtink, Marco Pellicciaro, Roberta Angelico, Carlo Gazia, Wojciech G Polak, Massimo Rossi, Giuseppe Tisone

Registry-linked trialOpen access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Updates in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06130046 (MR-proADM as a Early Biomarker for DGF and AR in Kidney and Liver Transplantation), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 33% 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.

NCT06130046 recruitingnot on this map

MR-proADM as a Early Biomarker for DGF and AR in Kidney and Liver Transplantation

TypeobservationalSponsorUniversity of Rome Tor VergataRan2022 to 2026Enrolled300ConditionsKidney Transplant, Complications, Liver Transplant, ComplicationsArmsMR-proADM dosage
3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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 4 institutions in 2 countries.

Tommaso Maria Manzia *Department of Surgery Science, University of Rome Tor Vergata, U.O.C. Chirurgia Epatobiliare e Trapianti, Fondazione PTV, Rome, Italy.
Quirino Lai *Department of Surgery and Organ Transplantation, Sapienza University of Rome, Rome, Italy. quirino.lai@uniroma1.it.ORCID http://orcid.org/0000-0003-1487-3235
Hermien HartogDivision of Hepatopancreatobiliary and Transplant Surgery, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Virginia AijtinkDivision of Hepatopancreatobiliary and Transplant Surgery, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Marco PellicciaroDepartment of Surgery Science, University of Rome Tor Vergata, U.O.C. Chirurgia Epatobiliare e Trapianti, Fondazione PTV, Rome, Italy.
Roberta AngelicoDepartment of Surgery Science, University of Rome Tor Vergata, U.O.C. Chirurgia Epatobiliare e Trapianti, Fondazione PTV, Rome, Italy.
Carlo GaziaDepartment of Surgery Science, University of Rome Tor Vergata, U.O.C. Chirurgia Epatobiliare e Trapianti, Fondazione PTV, Rome, Italy.
Wojciech G PolakDivision of Hepatopancreatobiliary and Transplant Surgery, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Massimo RossiDepartment of Surgery and Organ Transplantation, Sapienza University of Rome, Rome, Italy.
Giuseppe TisoneDepartment of Surgery Science, University of Rome Tor Vergata, U.O.C. Chirurgia Epatobiliare e Trapianti, Fondazione PTV, Rome, Italy.
University of Rome Tor Vergata · ITErasmus MC · NLPoliclinico Umberto I · ITSapienza University of Rome · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The role of the graft-to-recipient weight ratio (GRWR) in adult liver transplantation (LT) has been poorly investigated so far. The aim is to evaluate the contribution of the GRWR to the well-recognized early allograft dysfunction (EAD) model (i.e., Olthoff model) for the prediction of 90-day graft loss after LT in adults. Three hundred thirty-one consecutive adult patients undergoing LT between 2009 and 2018 at Tor Vergata and Sapienza University in Rome, Italy, served as the Training-Set. The Validation-Set included 123 LTs performed at the Erasmus Medical Center, Rotterdam, the Netherlands. The mEAD model for 90-day graft loss included the following variables: GRWR [Formula: see text] 1.57 = 2.5, GRWR [Formula: see text] 2.13 = 2.5, total bilirubin ≥ 10.0 mg/dL = 2.0, INR ≥ 1.60 = 2.3, and aminotransferase > 2000 IU/L = 2.2. The mEAD model showed an AUC = 0.74 (95%CI = 0.66-0.82; p < 0.001) and AUC = 0.68 (95%CI = 0.58-0.88; p = 0.01) in the Training-Set and Validation-Set, respectively, outperforming conventional EAD in both cohorts (Training-Set: AUC = 0.64, 95%CI = 0.57-0.72; p = 0.001; Validation-Set: AUC = 0.52, 95%CI = 0.35-0.69, p = 0.87). Incorporation of graft weight in a composite multivariate model allowed for better prediction of patients who presented an aminotransferase peak > 2000 IU/L after LT (OR = 2.39, 95%CI = 1.47-3.93, p = 0.0005). The GRWR is important in determining early graft loss after adult LT, and the mEAD model is a useful predictive tool in this perspective, which may assist in improving the graft allocation process.

Indexed as

Liver TransplantationAdultAllograftsGraft SurvivalHumansRetrospective StudiesRisk FactorsTransaminasesTransaminasesEarly allograft dysfunctionGraft lossGraft weightLiver transplantation

Identifiers

PMID35306614
PMCPMC9338117
OpenAlexW4220746876

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.