Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset.

Journal: Anesthesia and analgesia
Published Date: Jan 1, 2021

Abstract

BACKGROUND: Craniosynostosis is the premature fusion of ≥1 cranial sutures and often requires surgical intervention. Surgery may involve extensive osteotomies, which can lead to substantial blood loss. Currently, there are no consensus recommendations for guiding blood conservation or transfusion in this patient population. The aim of this study is to develop a machine-learning model to predict blood product transfusion requirements for individual pediatric patients undergoing craniofacial surgery.

Authors

  • Ali Jalali
    Section of Biomedical Informatics, Department of Anesthesiology & Critical Care Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
  • Hannah Lonsdale
  • Lillian V Zamora
    Department of Anesthesia, Perioperative and Pain Medicine, Johns Hopkins All Children's Hospital, St Petersburg, Florida.
  • Luis Ahumada
    Enterprise Analytics and Reporting, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
  • Anh Thy H Nguyen
    Predictive Analytics Core, Johns Hopkins All Children's Hospital, St Petersburg, Florida.
  • Mohamed Rehman
    Department of Anesthesia and Pain Medicine at Johns Hopkins All Children's Hospital, St. Petersburg, FL, 33701, USA.
  • James Fackler
  • Paul A Stricker
    Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Allison M Fernandez
    Department of Anesthesia, Perioperative and Pain Medicine, Johns Hopkins All Children's Hospital, St Petersburg, Florida.

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