Open AccessMachine 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
Author(s) -
Ali Jalali,
Hannah Lonsdale,
Lillian Zamora,
Luis Ahumada,
Anh Thy Nguyen,
Mohamed A. Rehman,
James C. Fackler,
Paul A. Stricker,
Allison M. Fernández
Publication year - 2020
Publication title -
anesthesia and analgesia/anesthesia and analgesia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.404
H-Index - 201
eISSN - 1526-7598
pISSN - 0003-2999
DOI - 10.1213/ane.0000000000004988
Subject(s) - medicine , perioperative , craniofacial surgery , receiver operating characteristic , craniosynostosis , machine learning , population , adaboost , hematocrit , artificial intelligence , surgery , craniofacial , support vector machine , computer science , environmental health , psychiatry
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.