ArticleBMJ open2025
Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Handling missingness in electronic health record laboratory data for secondary use: a decision-making process.BMJ open · 2026Review
- Development and a single-centre temporal validation of a machine learning-based model for early prediction of acute pancreatitis-associated liver injury.BMC gastroenterology · 2026Article
- Developing a prediction model to predict the incidence of metastasis in lung cancer patients using artificial intelligence techniques.BMC pulmonary medicine · 2026Article
- Transforming lung transplantation with artificial intelligence: a narrative review from organ allocation to post-transplant management.Journal of thoracic disease · 2026Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesIn lung transplantation (LTx), a priority is assigned to each candidate on the waiting list. Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques to enhance the process of prioritising patients.
designDeveloping a prediction model. SETTING AND
participantsOur data were retrieved from the United Network for Organ Sharing (UNOS) open-source database of transplant patients between 2005 and 2023.
interventionsAfter the preprocessing process, a feature engineering technique was employed to select the most relevant features. Then, six ML models with optimised hyperparameters including multiple linear regression, random forest regressor (RF), support vector machine regressor, XGBoost regressor, a multilayer perceptron model and a deep learning model were developed based on the UNOS dataset. PRIMARY AND SECONDARY OUTCOME MEASURES: The performance of each model was evaluated using R-squared (R
resultsThe raw dataset contains 196 270 records with 545 features in all organs. After preprocessing, 32 966 records with 15 features remain. Among various models, the RF model achieved a high R
conclusionThe deployment of the ML model has the potential to act as an automated tool to aid physicians in assessing the priority of lung transplants and identifying significant factors that play a role in patient survival.
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