1. Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study
- Author
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Dong Tian, Yu-Jie Zuo, Hao-Ji Yan, Heng Huang, Ming-Zhao Liu, Hang Yang, Jin Zhao, Ling-Zhi Shi, and Jing-Yu Chen
- Subjects
Airway stenosis ,Lung transplantation ,Machine learning ,Logistic regression ,Prediction model ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Abstract Background Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical intervention in patients after LTx. Methods Patients who underwent LTx between January 2017 and December 2019 were reviewed. The conventional logistic regression (LR) model was fitted by the independent risk factors which were determined by multivariate LR. The optimal ML model was determined based on 7 feature selection methods and 8 ML algorithms. Model performance was assessed by the area under the curve (AUC) and brier score, which were internally validated by the bootstrap method. Results A total of 381 LTx patients were included, and 40 (10.5%) patients developed AS. Multivariate analysis indicated that male, pulmonary arterial hypertension, and postoperative 6-min walking test were significantly associated with AS (all P
- Published
- 2024
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