ArticleClinical transplantation2025
Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.
Article in Clinical transplantation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
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Who cites it
2 citing papers in PubMed.
- Transforming lung transplantation with artificial intelligence: a narrative review from organ allocation to post-transplant management.Journal of thoracic disease · 2026Review
- Machine Learning-Based Predictive Model for Grade 3 Primary Graft Dysfunction Following Lung Transplantation: A Retrospective Cohort Study.International journal of general medicine · 2026Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
introductionPrimary graft dysfunction (PGD) is a significant barrier to survival in lung transplant (LTx) recipients. PGD in patients with systemic sclerosis (SSc) remains especially underrepresented in research.
methodsWe investigated 92 SSc recipients (mean age 51 years ± 10) who underwent bilateral LTx between 2007 and 2020. PGD was defined as grade 3 PGD at 72 h post-LTx. A comprehensive set of CT image features was automatically computed from recipient chest CT scans using deep learning algorithms. Volumetric analysis of recipients' lungs and chest cavity was used to estimate lung-size matching. Four machine learning (ML) algorithms were developed to predict PGD, including multivariate logistic regression, support vector machine (SVM), random forest classifier (RFC), and multilayer perceptron (MLP).
resultsPGD was significantly associated with BMI >30 kg/m
conclusionCT-derived features are significantly associated with PGD, and models incorporating these features can predict PGD in SSc recipients.
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