Developing machine learning models to predict primary graft dysfunction after lung transplantation.
lung transplantation
machine learning
predictive modeling
primary graft dysfunction
Journal
American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
ISSN: 1600-6143
Titre abrégé: Am J Transplant
Pays: United States
ID NLM: 100968638
Informations de publication
Date de publication:
17 Jul 2023
17 Jul 2023
Historique:
received:
23
12
2022
revised:
21
06
2023
accepted:
04
07
2023
pubmed:
20
7
2023
medline:
20
7
2023
entrez:
19
7
2023
Statut:
aheadofprint
Résumé
Primary graft dysfunction (PGD) is the leading cause of morbidity and mortality in the first 30 days after lung transplantation. Risk factors for the development of PGD include donor and recipient characteristics, but how multiple variables interact to impact the development of PGD and how clinicians should consider these in making decisions about donor acceptance remain unclear. This was a single-center retrospective cohort study to develop and evaluate machine learning pipelines to predict the development of PGD grade 3 within the first 72 hours of transplantation using donor and recipient variables that are known at the time of donor offer acceptance. Among 576 bilateral lung recipients, 173 (30%) developed PGD grade 3. The cohort underwent a 75% to 25% train-test split, and lasso regression was used to identify 11 variables for model development. A K-nearest neighbor's model showing the best calibration and performance with relatively small confidence intervals was selected as the final predictive model with an area under the receiver operating characteristics curve of 0.65. Machine learning models can predict the risk for development of PGD grade 3 based on data available at the time of donor offer acceptance. This may improve donor-recipient matching and donor utilization in the future.
Identifiants
pubmed: 37468109
pii: S1600-6135(23)00580-4
doi: 10.1016/j.ajt.2023.07.008
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2023 American Society of Transplantation & American Society of Transplant Surgeons. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ramsey Hachem, Andrew Michelson, Inez Oh, Aditi Gupta, Philip Payne reports financial support was provided by Mid-America Transplant Foundation. Ramsey Hachem reports a relationship with TransMedics Inc that includes: consulting or advisory. Daniel Kreisel is on the Scientific Advisory Board of Sana Biotechnology