Related Experiment Video
Updated: Aug 18, 2025
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Development and validation of models for detection of postoperative infections using structured electronic health
Kathryn L Colborn1, Yaxu Zhuang2, Adam R Dyas3
1Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO; Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO.
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.
Insights
Automated surveillance using electronic health records can accurately track postoperative infections for all surgeries. This method is more efficient than manual review, enabling timely evaluation and reporting of infection rates.
Area of Science:
- Medical Informatics
- Public Health Surveillance
- Clinical Epidemiology
Background:
- Postoperative infections are a major complication, often exceeding 50% of all postoperative issues.
- Current surveillance relies on manual chart review, which is costly, time-consuming, and covers only 10-15% of operations.
- Automated surveillance systems are needed for comprehensive and timely evaluation of all surgical operations.
Purpose of the Study:
- To develop and validate accurate, interpretable models for postoperative infection surveillance.
- To utilize structured electronic health records (EHR) data for this automated surveillance.
- To improve the efficiency and scope of monitoring surgical site infections, urinary tract infections, sepsis, and pneumonia.
Main Methods:
- Retrospective analysis of 30,639 operations from five hospitals (2013-2019).
- Linking EHR data with American College of Surgeons National Surgical Quality Improvement Program outcomes.
- Applying the knockoff filter for variable selection in penalized regression models, validated on a held-out dataset.
Main Results:
- Seven percent of patients developed at least one postoperative infection.
- Developed models with 4-8 variables demonstrated high performance (AUROC >0.91).
- Achieved excellent metrics: specificity >81%, sensitivity >87%, negative predictive value >99%, and positive predictive value 10-15%.
Conclusions:
- Electronic health records data and simple linear regression models enable accurate surveillance of postoperative infection rates.
- This approach allows for the implementation of comprehensive surveillance and reporting across all operations.
- Automated surveillance significantly enhances the ability to monitor and manage postoperative complications effectively.
More Related Videos
Related Concept Videos
01:29Issues And Trends In Healthcare Delivery System
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
01:22Healthcare Associated Infections II: Preventive Measures
The best practices for preventing healthcare-associated infections include hand hygiene, patient risk...
01:26Healthcare Associated Infections I: Iatrogenic, Exogenic and Endogenic
HAIs significantly increase the cost of health care. Extended stays in healthcare institutions, increased disability, increased costs of medications, including specialized antibiotics, and prolonged recovery times add to the patient's expenses and the healthcare institution and funding bodies.
01:03Data Validation
Nursing assessment guides are generally based on holistic models rather than medical...
01:18Steps in Outbreak Investigation
01:30Methods of Documentation VII: EMR