Primary Graft Dysfunction After Lung Transplantation: A Temporal Classification and Machine Learning Clustering
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Le résumé fourni par la source
Background. Primary graft dysfunction (PGD) is a major cause of morbidity and mortality after lung transplantation (LTx). PGD is graded at static time points, limiting insight into its temporal dynamics. Statistical risk-factor analysis may overlook the multifactorial complexity of PGD. Machine learning (ML) may address this but is constrained by small sample sizes. We aim to introduce a temporal PGD classification, perform ML-based clustering and overcome sample-size limitations by generating synthetic patient data. Methods. A prospectively collected database of 794 LTx at University Hospitals Leuven (Belgium) from January 2012 to November 2023 was analyzed. Recipients were classified into 4 temporal PGD phenotypes: no, early, late, and persistent PGD. Unsupervised ensemble k -means clustering was used to group patients based on 30 clinical variables. To generate synthetic data, a Wasserstein Generative Adversarial Network with Gradient Penalty was trained. Results. Six hundred ninety primary double LTx cases were included. Temporal PGD phenotypes showed significantly different 5-y survival (log-rank test: P < 0.001). K -means clustering revealed stable patient subgroups, with k = 5 and k = 9 identified as optimal cluster numbers. Key features affecting clustering included preoperative intensive care unit stay and postoperative chest x-rays. The synthetic data set preserved the correlation structure of the original data, and the combined data had similar clustering behavior, with optimal clustering at k = 5, k = 7, and k = 9. Conclusions. This study demonstrates the potential of temporal PGD classification to better understand how the dynamic character of PGD affects survival. ML techniques, including unsupervised clustering and synthetic data generation, could be promising strategies to unravel complex interactions between clinical factors and overcome sample-size limitations.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Primary Graft Dysfunction After Lung Transplantation: A Temporal Classification and Machine Learning Clustering
- Date Crossref
- 20/07/2026
- Éditeur
- Ovid Technologies (Wolters Kluwer Health)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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VIB-KU Leuven Center for Cancer Biology pays non établi dans la noticeStructure de recherche
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KU Leuven pays non établi dans la noticeUniversité ou école supérieure
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Khalifa University of Science and Technology Center for Biotechnology pays non établi dans la noticeUniversité ou école supérieure
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University Hospitals Leuven Department of Thoracic Surgery pays non établi dans la noticeUniversité ou école supérieure
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Laboratory of Angiogenesis and Vascular Metabolism pays non établi dans la noticeStructure de recherche
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Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE) pays non établi dans la noticeStructure de recherche
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Division Animal and Human Health Engineering pays non établi dans la noticeInstitution
VIB-KU Leuven Center for Cancer Biology, KU Leuven et Center for Biotechnology — Khalifa University of Science and Technology, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.