Estimation of risk-adjusted postoperative infection outcomes using interpretable machine learning and electronic
Kathryn L Colborn1, Yizhou Fei2, William G Henderson3
1Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Adult and Child Center for Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO.
Background:
This study compared risk-adjusted postoperative infection outcomes estimated by statistical models applied to electronic health record (EHR) data ("automated") to gold-standard manual chart review outcomes estimated by the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP).
Methods:
307,335 adult patients who underwent 441,047 operations in nine surgical specialties at five large hospitals between 2013-2019 were included. Records from 30,603 patients were linked to the local ACS-NSQIP database (97% linkage). Previously published models for estimating preoperative risk and occurrence of postoperative infections were used to estimate observed-to-expected event ratios (O/E) for surgical site infections, urinary tract infections, sepsis/septic shock, and pneumonia.
Results:
O/E ratios were similar when comparing automated methods to ACS-NSQIP across 5 hospitals and 4 infection types. The Pearson correlation coefficient of the hospital O/E ratios was 0.77, mean absolute difference was 0.13%, and 100% of the confidence intervals were overlapping. The correlations and mean absolute differences for individual infection types improved as incidence rates increased.
Discussion:
Parsimonious statistical models applied to EHR data can be used to accurately estimate hospital risk-adjusted postoperative infection outcomes.
Conclusions:
These models could be used to augment postoperative infection surveillance for hospital quality monitoring.
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