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A machine learning approach to predicting early and late postoperative reintubation

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Abstract

Accurate estimation of surgical risks is important for informing the process of shared decision making and informed consent. Postoperative reintubation (POR) is a severe complication that is associated with postoperative morbidity. Previous studies have divided POR into early POR (within 72 h of surgery) and late POR (within 30 days of surgery). Using data provided by American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP), machine learning classification models (logistic regression, random forest classification, and gradient boosting classification) were utilized to develop scoring systems for the prediction of combined, early, and late POR. The risk factors included in each scoring system were narrowed down from a set of 37 pre and perioperative factors. The scoring systems developed from the logistic regression models demonstrated strong performance in terms of both accuracy and discrimination across the different POR outcomes (Average Brier score, 0.172; Average c-statistic, 0.852). These results were only marginally worse than prediction using the full set of risk variables (Average Brier score, 0.145; Average c-statistic, 0.870). While more work needs to be done to identify clinically relevant differences between the early and late POR outcomes, the scoring systems provided here can be used by surgeons and patients to improve the quality of care overall.

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Funding

MJK: University of Vermont College of Engineering and Mathematical Sciences Summer REU Award; RDU: funding and/or fees from Merck, Covidien/Medtronic, AcelRx and Pfizer.

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Authors and Affiliations

  1. College of Engineering and Mathematical Sciences, University of Vermont, 82 University Place, Burlington, VT, 05405, USA

    Mathew J. Koretsky

  2. Department of Anesthesiology and Perioperative Medicine, Tufts University School of Medicine, 800 Washington Street, Boston, MA, 02111, USA

    Ethan Y. Brovman

  3. Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA

    Richard D. Urman

  4. Department of Orthopedics and Rehabilitation, Department of Surgery, University of Vermont Larner College of Medicine, 111 Colchester Avenue, Burlington, VT, 05401, USA

    Mitchell H. Tsai

  5. Department of Computer Science, University of Vermont, 82 University Place, Burlington, VT, 05405, USA

    Nick Cheney

Authors
  1. Mathew J. Koretsky
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  2. Ethan Y. Brovman
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  3. Richard D. Urman
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  4. Mitchell H. Tsai
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  5. Nick Cheney
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Contributions

All authors contributed to the study conception and design. Data analysis was performed by Mathew Koretsky and Dr. Nick Cheney. The first draft of the manuscript was written by Mathew Koretsky and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mathew J. Koretsky.

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Given the deidentified nature of the data, the research was deemed Not Human Subjects Research by the Institutional Review Board (Mass General Brigham protocol #2015P002706 and Tufts University protocol described in Supplementary Document 1).

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Koretsky, M.J., Brovman, E.Y., Urman, R.D. et al. A machine learning approach to predicting early and late postoperative reintubation. J Clin Monit Comput 37, 501–508 (2023). https://doi.org/10.1007/s10877-022-00908-z

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  • DOI: https://doi.org/10.1007/s10877-022-00908-z

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