A machine learning-based workflow for predicting transplant outcomes in patients with sickle cell disease.

Journal: British journal of haematology
PMID: 39439193

Abstract

Allogeneic haematopoietic cell transplantation (HCT) with HLA-matched sibling donor remains the most established curative therapeutic option for patients with sickle cell disease (SCD). However, it is not without risks, highlighting the need for a risk stratification system. Utilizing a machine learning (ML) approach that combines clinical and imaging variables, we identified red cell distribution width and renal organ damage as important risk factors for patients undergoing HCT. This ML-based algorithm, similar to an approach previously reported for predicting mortality in patients with SCD, should be applicable to risk factor discovery in similar studies.

Authors

  • Haiou Li
    Department of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, China.
  • Vandana Sachdev
  • Xin Tian
    Cancer Hospital Chinese Academy of Medical Sciences (Shenzhen Hospital), Shenzhen, 518000, China. Electronic address: 947952187@qq.com.
  • My-Le Nguyen
    Echocardiography Laboratory, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
  • Matthew Hsieh
    Cellular and Molecular Therapeutics Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
  • Courtney Fitzhugh
    Cellular and Molecular Therapeutics Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
  • Emily Limerick
    Cellular and Molecular Therapeutics Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
  • Wynona Coles
    Cellular and Molecular Therapeutics Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
  • Nancy Asomaning
    Sickle Cell Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
  • Anna Conrey
    Sickle Cell Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.
  • Colin O Wu
    From the Department of Radiology (B.A.-V.), Bloomberg School of Public Health (E.G.), and Department of Medicine, Cardiology and Radiology (J.A.C.L.), Johns Hopkins University, Baltimore, MD; George Washington University, DC (X.Y.); Office of Biostatistics, NHLBI, NIH, Bethesda, MD (C.O.W.); Department of Preventive Medicine, Northwestern University Medical School, Chicago, IL (K.L.); Department of Cardiology, Wake Forest University Health Sciences, Winston-Salem, NC (W.G.H.); Department of Biostatistics, University of Washington, Seattle (R.M.); Department of Radiology, UCLA School of Medicine, Los Angeles, CA (A.S.G.); Division of Epidemiology and Community Health, University of Minnesota, Minneapolis (A.R.F.); Departments of Medicine and Epidemiology, Columbia University, New York, NY (S.S.); and Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD (D.A.B.).
  • Swee Lay Thein
    Sickle Cell Branch, National Heart, Lung, and Blood Institute, NIH, Bethesda, Maryland, USA.

Keywords

External Resources

Popular Topics
Recent Journals