TY - GEN
T1 - The value of aggregated high-resolution intraoperative data for predicting post-surgical infectious complications at two independent sites
AU - Tourani, Roshan
AU - Murphree, Dennis H.
AU - Melton-Meaux, Genevieve
AU - Wick, Elizabeth
AU - Kor, Daryl J.
AU - Simon, Gyorgy J.
N1 - Funding Information:
This work was supported in part by NIGMS award R01 GM 120079, AHRQ award R01 HS024532, and the NCATS University of Minnesota CTSA UL1 TR002494. The views expressed in this manuscript are those of the authors and do not necessarily reflect the views of the funding agencies.
Publisher Copyright:
© 2019 International Medical Informatics Association (IMIA) and IOS Press.
PY - 2019/8/21
Y1 - 2019/8/21
N2 - 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.
AB - 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.
KW - Machine learning
KW - Postoperative complications
UR - http://www.scopus.com/inward/record.url?scp=85071482643&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071482643&partnerID=8YFLogxK
U2 - 10.3233/SHTI190251
DO - 10.3233/SHTI190251
M3 - Conference contribution
C2 - 31437953
AN - SCOPUS:85071482643
T3 - Studies in Health Technology and Informatics
SP - 398
EP - 402
BT - MEDINFO 2019
A2 - Seroussi, Brigitte
A2 - Ohno-Machado, Lucila
A2 - Ohno-Machado, Lucila
A2 - Seroussi, Brigitte
PB - IOS Press
T2 - 17th World Congress on Medical and Health Informatics, MEDINFO 2019
Y2 - 25 August 2019 through 30 August 2019
ER -