Pre-transplant and transplant parameters predict long-term survival after hematopoietic cell transplantation using machine learning.

Journal: Transplant immunology
PMID: 40020790

Abstract

BACKGROUND: Allogeneic hematopoietic stem transplantation (allo-HSCT) constitutes a curative treatment for various hematological malignancies. However, various complications limit the therapeutic efficacy of this approach, increasing the morbidity and decreasing the overall survival of allo-HSCT recipients. In everyday clinical practice, various laboratory and clinical biomarkers and scorning systems have been developed and implemented focusing on the recognition of high-risk patients for organ dysfunction-related complications and those who might experience low overall survival. However, the predictive accuracy of developed scores has been reported deficient in some studies. The aim of the current retrospective study is to develop a machine learning (ML) model to predict the long-term survivorship of patients who receive allo-HSCT based on clinical pre- and post-allo-HSCT variables, and on transplantation-related characteristics.

Authors

  • Panagiotis G Asteris
    Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, 14121 Athens, Greece.
  • Amir H Gandomi
    Faculty of Engineering Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.
  • Danial J Armaghani
    Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
  • Ahmed Salih Mohammed
    Engineering Department, American University of Iraq, Sulaimani, Kurdistan-Region, Iraq.
  • Zoi Bousiou
    Hematology Department - BMT Unit, G Papanicolaou Hospital, Thessaloniki, Greece.
  • Ioannis Batsis
    Hematology Department - BMT Unit, G Papanicolaou Hospital, Thessaloniki, Greece.
  • Nikolaos Spyridis
    Hematology Department - BMT Unit, G Papanicolaou Hospital, Thessaloniki, Greece.
  • Georgios Karavalakis
    Hematology Department - BMT Unit, G Papanicolaou Hospital, Thessaloniki, Greece.
  • Anna Vardi
    Hematology Department - BMT Unit, G Papanicolaou Hospital, Thessaloniki, Greece.
  • Leonidas Triantafyllidis
    Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.
  • Evangelos I Koutras
    Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.
  • Nikos Zygouris
    Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece. Electronic address: nzigouris@aspete.gr.
  • Georgios A Drosopoulos
    Discipline of Civil Engineering, University of Central Lancashire, Preston, UK. Electronic address: gdrosopoulos@uclan.ac.uk.
  • Nikolaos A Fountas
    Department of Mechanical Engineering Educators, School of Pedagogical and Technological Education, Athens, Greece.
  • Nikolaos M Vaxevanidis
    Department of Mechanical Engineering Educators, School of Pedagogical and Technological Education, Athens, Greece. Electronic address: vaxev@aspete.gr.
  • Abidhan Bardhan
    Civil Engineering Department, National Institute of Technology Patna, Patna, India.
  • Pijush Samui
    Department of Civil Engineering, National Institute of Technology Patna, India.
  • George D Hatzigeorgiou
    Hellenic Open University, Greece. Electronic address: hatzigeorgiou@eap.gr.
  • Jian Zhou
    CTIQ, Canon Medical Research USA, Inc., Vernon Hills, 60061, USA.
  • Konstantina V Leontari
    National Kapodistrian University of Athens, Aretaieio Hospital, Athens, Greece.
  • Paschalis Evangelidis
    2nd Propedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece. Electronic address: pascevan@auth.gr.
  • Ioanna Sakellari
    Hematology Department, BMT Unit, G Papanicolaou Hospital, Thessaloniki, Greece.
  • Eleni Gavriilaki
    Hematology Department, BMT Unit, G Papanicolaou Hospital, Thessaloniki, Greece.

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