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 language | English (US) |
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Title of host publication | MEDINFO 2019 |
Subtitle of host publication | Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics |
Editors | Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi |
Publisher | IOS Press |
Pages | 398-402 |
Number of pages | 5 |
ISBN (Electronic) | 9781643680026 |
DOIs | |
State | Published - Aug 21 2019 |
Event | 17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France Duration: Aug 25 2019 → Aug 30 2019 |
Publication series
Name | Studies in Health Technology and Informatics |
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Volume | 264 |
ISSN (Print) | 0926-9630 |
ISSN (Electronic) | 1879-8365 |
Conference
Conference | 17th World Congress on Medical and Health Informatics, MEDINFO 2019 |
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Country/Territory | France |
City | Lyon |
Period | 8/25/19 → 8/30/19 |
Bibliographical note
Publisher Copyright:© 2019 International Medical Informatics Association (IMIA) and IOS Press.
Keywords
- Machine learning
- Postoperative complications