Predicting early return to the operating room in early-onset scoliosis patients using machine learning techniques.

Journal: Spine deformity

PMID: 38530612

Abstract

PURPOSE: Surgical treatment of early-onset scoliosis (EOS) is associated with high rates of complications, often requiring unplanned return to the operating room (UPROR). The aim of this study was to create and validate a machine learning model to predict which EOS patients will go on to require an UPROR during their treatment course.

Authors

Brett R Lullo

Division of Orthopaedic Surgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA. blullo@luriechildrens.org.
Patrick J Cahill

Division of Orthopaedic Surgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
John M Flynn

Division of Orthopaedic Surgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Jason B Anari

Division of Orthopaedic Surgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA.

Keywords

Age of Onset Child Child, Preschool Female Humans Machine Learning Male Operating Rooms Postoperative Complications Reoperation Retrospective Studies Scoliosis Spinal Fusion

External Resources

View on PubMed Access via DOI PubMed (38530612)

Predicting early return to the operating room in early-onset scoliosis patients using machine learning techniques.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals