Machine learning adjusted sequential CUSUM-analyses are superior to cross-sectional analysis of excess mortality after surgery.

Journal: International journal of medical informatics
Published Date: Nov 9, 2024

Abstract

BACKGROUND: The assessment of clinical outcome quality, particularly in surgery, is crucial for healthcare improvement. Traditional cross-sectional analyses often fall short in timely and systematic identification of clinical quality issues. This study explores the efficacy of machine learning adjusted sequential CUSUM (Cumulative Sum) analyses in monitoring post-surgical mortality.

Authors

  • Florian Bösch
    Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, Germany. Electronic address: florian.boesch@med.uni-goettingen.de.
  • Stina Schild-Suhren
    Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, Germany.
  • Elif Yilmaz
    Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, Germany.
  • Michael Ghadimi
    Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, Germany.
  • Athanasios Karampalis
    Mathematics and Computer Science, University of Uppsala, Uppsala, Sweden.
  • Nikolaus Börner
    Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, Germany; Transplantation Center Munich, Hospital of the LMU, Campus Grosshadern, Munich, Germany.
  • Markus Bo Schoenberg
    Department of General, Visceral and Transplantation Surgery, Ludwig-Maximilians-University, Munich, Germany.

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