Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model.

Journal: PloS one
Published Date: Aug 19, 2025

Abstract

BACKGROUND: Postoperative pulmonary complications (POPC) are common after general anaesthesia and are a major cause of increased morbidity and mortality in surgical patients. However, prevention and treatment methods for POPC that are considered effective tie up human and technical resources. Therefore, the planned research project aims to create a prediction model that enables the reliable identification of high-risk patients immediately after surgery based on a tailored machine learning algorithm.

Authors

  • Britta Trautwein
    University Hospital Ulm, Ulm, Germany.
  • Meinrad Beer
    Diagnostic and Interventional Radiology, University Hospital Ulm, Germany.
  • Manfred Blobner
    Technical University Munich, School of Medicine, Klinikum Rechts der Isar, Department of Anaesthesiology & Intensive Care Medicine, Munich, Germany.
  • Bettina Jungwirth
    Department of Anesthesiology and Intensive Care Medicine, Ulm University, Ulm, Germany.
  • Simone Maria Kagerbauer
    Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany. simone.kagerbauer@uni-ulm.de.
  • Michael Götz
    Medical Image Analysis, Division Medical Image Computing, DKFZ Heidelberg, Germany; National Center for Tumor Diseases (NCT), Heidelberg, Germany. Electronic address: m.goetz@dkfz-heidelberg.de.

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