Abstract
Background
Postoperative infections constitute more than half of all postoperative complications.
Surveillance of these complications is primarily done through manual chart review,
which is time consuming, expensive, and typically only covers 10% to 15% of all operations.
Automated surveillance would permit the timely evaluation of and reporting of all
operations.
Methods
The goal of this study was to develop and validate parsimonious, interpretable models
for conducting surveillance of postoperative infections using structured electronic
health records data. This was a retrospective study using 30,639 unique operations
from 5 major hospitals between 2013 and 2019. Structured electronic health records
data were linked to postoperative outcomes data from the American College of Surgeons
National Surgical Quality Improvement Program. Predictors from the electronic health
records included diagnoses, procedures, and medications. Infectious complications
included surgical site infection, urinary tract infection, sepsis, and pneumonia within
30 days of surgery. The knockoff filter, a penalized regression technique that controls
type I error, was applied for variable selection. Models were validated in a chronological
held-out dataset.
Results
Seven percent of patients experienced at least one type of postoperative infection.
Models selected contained between 4 and 8 variables and achieved >0.91 area under
the receiver operating characteristic curve, >81% specificity, >87% sensitivity, >99%
negative predictive value, and 10% to 15% positive predictive value in a held-out
test dataset.
Conclusion
Surveillance and reporting of postoperative infection rates can be implemented for
all operations with high accuracy using electronic health records data and simple
linear regression models.
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Article info
Publication history
Published online: December 02, 2022
Accepted:
October 26,
2022
Identification
Copyright
Published by Elsevier Inc.
