Validation of a Post-Transplant Lymphoproliferative Disorder Risk Prediction Score and Derivation of a New Prediction Score Using a National Bone Marrow Transplant Registry Database.
Chien-Chang LeeTzu-Chun HsuChia-Chih KuoMichael A LiuAhmed M AbdelfattahChia-Na ChangMing YaoChi-Cheng LiKang-Hsi WuTsung-Chih ChenJyh-Pyng GauPo-Nan WangYi-Chang LiuLun-Wei ChiouMing-Yang LeeSin-Syue LiTsu-Yi ChaoShiann-Tarng JouHsiu-Hao ChangPublished in: The oncologist (2021)
This study validated the Fujimoto score for the prediction of post-transplant lymphoproliferative disorder (PTLD) development following hematopoietic cell transplant (HCT) in an external, independent, and nationally representative population. This study also developed a more comprehensive model with enhanced discrimination for better risk stratification of patients receiving HCT, potentially changing clinical managements in certain risk groups. Previously unreported risk factors associated with the development of PTLD after HCT were identified using the machine learning algorithm, least absolute shrinkage and selection operator, including pre-HCT medical history of mechanical ventilation and the chemotherapy agents used in conditioning regimen.
Keyphrases
- bone marrow
- machine learning
- mechanical ventilation
- epstein barr virus
- cell cycle arrest
- healthcare
- intensive care unit
- single cell
- stem cells
- cell death
- deep learning
- mesenchymal stem cells
- artificial intelligence
- cell therapy
- radiation therapy
- squamous cell carcinoma
- quality improvement
- locally advanced
- diffuse large b cell lymphoma
- respiratory failure