Evidence map›Paper›PMID 40436838›Full record

ArticleNature communications2025

GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients.

Divya Sharma, Neta Gotlieb, Daljeet Chahal, Joseph C Ahn, Bastian Engel, Richard Taubert, Eunice Tan, Lau Kai Yun, Sara Naimimohasses, Ankit Ray and 14 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Emerging applications of traditional and generative artificial intelligence in liver transplantation.Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society · 2026
    Article
  3. Article
  4. 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

24 authors.

Divya Sharma *Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.ORCID http://orcid.org/0009-0004-5022-697X
Neta Gotlieb *Department of Medicine, University of Ottawa, Ottawa, ON, Canada.
Daljeet Chahal *Vancouver General Hospital, Division of Gastroenterology and Hepatology, and Liver Transplant Program of BC, University of British Columbia, Vancouver, Canada.
Joseph C AhnDivision of Gastroenterology and Hepatology at Mayo Clinic, Rochester, MN, USA.
Bastian EngelDepartment of Gastroenterology, Hepatology, Infectious Diseases and Endocrinology, Hannover Medical School, Hannover, Germany.ORCID http://orcid.org/0000-0002-0972-3454
Richard TaubertDepartment of Gastroenterology, Hepatology, Infectious Diseases and Endocrinology, Hannover Medical School, Hannover, Germany.
Eunice TanDivision of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore.
Lau Kai YunDivision of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore.
Sara NaimimohassesAjmera Transplant Program, University Health Network, Toronto, ON, Canada.
Ankit RayAjmera Transplant Program, University Health Network, Toronto, ON, Canada.ORCID http://orcid.org/0009-0000-7765-8271
Yoojin HanAjmera Transplant Program, University Health Network, Toronto, ON, Canada.
Sara GehlautAjmera Transplant Program, University Health Network, Toronto, ON, Canada.
Maryam ShojaeeAjmera Transplant Program, University Health Network, Toronto, ON, Canada.
Surabie SivanendranAjmera Transplant Program, University Health Network, Toronto, ON, Canada.
Maryam NaghibzadehAjmera Transplant Program, University Health Network, Toronto, ON, Canada.
Amirhossein AzhieAjmera Transplant Program, University Health Network, Toronto, ON, Canada.
Sareh KeshavarziPrincess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Kai DuanDepartment of Pathology, University Health Network, Toronto, ON, Canada.
Leslie LillyAjmera Transplant Program, University Health Network, Toronto, ON, Canada.
Nazia SelznerAjmera Transplant Program, University Health Network, Toronto, ON, Canada.
Cynthia TsienAjmera Transplant Program, University Health Network, Toronto, ON, Canada.
Elmar JaeckelAjmera Transplant Program, University Health Network, Toronto, ON, Canada.
Wei XuPrincess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Mamatha BhatAjmera Transplant Program, University Health Network, Toronto, ON, Canada. Mamatha.bhat@uhn.ca.ORCID http://orcid.org/0000-0003-1960-8449

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver transplant recipients (LTRs) are at risk of graft injury, leading to cirrhosis and reduced survival. Liver biopsy, the diagnostic gold standard, is invasive and risky. We developed a hybrid multi-class neural network (NN) model, 'GraftIQ,' integrating clinician expertise for non-invasive graft pathology diagnosis. Biopsies from LTRs (1992-2020) were classified into six categories using demographic, clinical, and lab data from 30 days pre-biopsy. The dataset (5217 biopsies) was split 70/30 for training/testing, with external validation at Mayo Clinic, Hannover Medical School, and NUHS Singapore. Bayesian fusion was used to combine clinician-derived probabilities with NN predictions, improving performance. Here we show that GraftIQ (MulticlassNN+clinical insight) achieved an AUC of 0.902 (95% CI:0.884-0.919), up from 0.885 with NN alone. Internal and external validation demonstrated 10-16% higher AUC than conventional ML models. GraftIQ demonstrates high accuracy in identifying graft etiologies and offers a valuable clinical decision support tool for LTRs.

Indexed as

Graft RejectionLiverLiver TransplantationNeural Networks, ComputerAdultAgedBayes TheoremBiopsyFemaleHumansMaleMiddle AgedTransplant Recipients

Identifiers

PMID40436838
PMCPMC12120053

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.