The value of aggregated high-resolution intraoperative data for predicting post-surgical infectious complications at two independent sites

Roshan Tourani, Dennis H. Murphree, Genevieve Melton-Meaux, Elizabeth Wick, Daryl J Kor, Gyorgy J. Simon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Surgical procedures carry the risk of postoperative infectious complications, which can be severe, expensive, and morbid. A growing body of evidence indicates that high-resolution intraoperative data can be predictive of these complications. However, these studies are often contradictory in their findings as well as difficult to replicate, suggesting that these predictive models may be capturing institutional artifacts. In this work, data and models from two independent institutions, Mayo Clinic and University of Minnesota-affiliated Fairview Health Services, were directly compared using a common set of definitions for the variables and outcomes. We built perioperative risk models for seven infectious post-surgical complications at each site to assess the value of intraoperative variables. Models were internally validated. We found that including intraoperative variables significantly improved the models' predictive performance at both sites for five out of seven complications. We also found that significant intraoperative variables were similar between the two sites for four of the seven complications. Our results suggest that intraoperative variables can be related to the underlying physiology for some infectious complications.

Original languageEnglish (US)
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Pages398-402
Number of pages5
ISBN (Electronic)9781643680026
DOIs
StatePublished - Aug 21 2019
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: Aug 25 2019Aug 30 2019

Publication series

NameStudies in Health Technology and Informatics
Volume264
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019
CountryFrance
CityLyon
Period8/25/198/30/19

Keywords

  • Machine learning
  • Postoperative complications

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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  • Cite this

    Tourani, R., Murphree, D. H., Melton-Meaux, G., Wick, E., Kor, D. J., & Simon, G. J. (2019). The value of aggregated high-resolution intraoperative data for predicting post-surgical infectious complications at two independent sites. In B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 398-402). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190251