Predicting and Diagnosing Pneumonia in Patients Undergoing Elective Cardiac Surgery via Machine Learning Analysis of Exhaled Volatile Carbonyl Compounds.

Journal: The Journal of thoracic and cardiovascular surgery
Published Date: Jul 2, 2025

Abstract

OBJECTIVE: Pneumonia remains one of the most common post-operative complications after elective cardiac surgery. Early intervention could lead to improved patient outcomes, including lower rates of ICU admissions, and shorter hospital stays. Volatile organic compounds (VOCs) in exhaled breath have shown promise in diagnosis and classification for various lung-related conditions. The study aims to diagnose and predict the onset of pneumonia in patients undergoing elective cardiac surgery via machine learning (ML) analysis of VOCs.

Authors

  • Toyokazu Endo
    Department of Cardiovascular and Thoracic Surgery, University of Louisville School of Medicine.
  • Kevin Tran
    Department of Chemical Engineering , Carnegie Mellon University , Pittsburgh , Pennsylvania 15217 , United States.
  • Dylan A Goodin
    Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
  • Gianna Katsaros
    Department of Cardiovascular and Thoracic Surgery, University of Louisville School of Medicine.
  • Zhenzhen Xie
    Department of Chemical Engineering, University of Louisville Speed School of Engineering.
  • Xiao-An Fu
    Department of Chemical Engineering, University of Louisville Speed School of Engineering.
  • George Pantalos
    Department of Cardiovascular and Thoracic Surgery, University of Louisville School of Medicine.
  • Hermann B Frieboes
    Dept. of Bioengineering University of Louisville Louisville, KY.
  • Victor Van Berkel
    Department of Cardiovascular and Thoracic Surgery, University of Louisville School of Medicine, Louisville, KY, USA.

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