Evidence map›Paper›PMID 39816954›Full record

ArticleJournal of inflammation research2025

Predicting Time to First Rejection Episode in Lung Transplant Patients Using a Comprehensive Multi-Indicator Model.

Youpeng Chen, Enzhong Li, Qingqing Yang, Zhenglin Chang, Baodan Yu, Jiancai Lu, Haojie Wu, Peiyan Zheng, Zhangkai J Cheng, Baoqing Sun

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of inflammation research, 2025. 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
–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

1 citing paper in PubMed.

  1. Understanding Machine Learning Applications in Lung Transplantation: A Narrative Review.Transplant international : official journal of the European Society for Organ Transplantation · 2025
    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.

Youpeng Chen *Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510140, People's Republic of China.ORCID 0000-0002-9677-2333
Enzhong Li *Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Qingqing YangDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510140, People's Republic of China.
Zhenglin ChangDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510140, People's Republic of China.
Baodan YuDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510140, People's Republic of China.
Jiancai LuDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510140, People's Republic of China.ORCID 0009-0005-9273-5999
Haojie WuDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510140, People's Republic of China.
Peiyan ZhengDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510140, People's Republic of China.ORCID 0000-0002-8795-4899
Zhangkai J Cheng *Department of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510140, People's Republic of China.ORCID 0000-0002-8621-4119
Baoqing SunDepartment of Clinical Laboratory, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510140, People's Republic of China.ORCID 0000-0002-1671-0723

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Rejection hinders long-term survival in lung transplantation, and no widely accepted biomarkers exist to predict rejection risk. This study aimed to develop and validate a prognostic model using laboratory data to predict the time to first rejection episode in lung transplant recipients. Methods: Data from 160 lung transplant recipients were retrospectively collected. Univariate Cox analysis assessed the impact of patient characteristics on time to first rejection episode. Kaplan-Meier survival analysis, LASSO regression, and multivariate Cox analysis were used to select prognostic indicators and develop a riskScore model. Model performance was evaluated using Kaplan-Meier analysis, time-dependent ROC curves, and multivariate Cox regression. Results: Patient characteristics were not significantly associated with the time to the first rejection episode. Six laboratory indicators-Activated Partial Thromboplastin Time, IL-10, estimated intrapulmonary shunt, 50% Hemolytic Complement, IgA, and Complement Component 3-were identified as significant predictors and integrated into the riskScore. The riskScore demonstrated good predictive performance. It outperformed individual indicators, was an independent risk factor for rejection, and was validated in the validation dataset. Conclusion: The riskScore model effectively predicts time to first rejection episode in lung transplant recipients.

Indexed as

laboratory indicatorslung transplantationprognostic modelrejection

Identifiers

PMID39816954
PMCPMC11734520

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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.