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A Practical Approach to Predicting Surgical Site Infection Risk Among Patients Before Leaving the Operating Room.

Michael S Woods1, Valerie Ekstrom2, Jonathan D Darer3

  • 1Global Chief Medical Officer, Caresyntax Corp, Boston, USA.

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|August 21, 2023
PubMed
Summary

This study developed surgical site infection (SSI) prediction models using patient and procedure data. Both manual and automated models accurately identified patients at risk for SSI, aiding patient safety efforts.

Keywords:
intraoperative risk factorsoperative safetypre-operative risk factorspredictive modelsurgical site infection (ssi)

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Area of Science:

  • Surgical outcomes research
  • Predictive modeling in healthcare
  • Patient safety and infection control

Background:

  • Surgical site infections (SSIs) pose a significant threat to patient safety and increase healthcare costs.
  • Early identification of patients at high risk for SSIs is crucial for implementing timely preventive interventions.
  • Existing risk prediction tools may lack accuracy or real-time applicability in the operating room setting.

Purpose of the Study:

  • To develop and validate predictive models for surgical site infections (SSIs) using pre-operative and peri-operative data.
  • To compare the performance of a manual scoring algorithm with an automated logistic regression model for SSI risk prediction.
  • To identify key patient and procedure characteristics associated with increased SSI risk.

Main Methods:

  • A retrospective analysis of 3,440 general and oncologic surgical patients was conducted.
  • Two risk scoring algorithms were developed: a manual count of positive factors and an automated logistic regression model.
  • Models were trained and validated using pre-operative (e.g., procedure urgency) and peri-operative (e.g., operative time, open surgery) data.

Main Results:

  • The automated algorithm identified key risk factors including procedure urgency, delayed antibiotic administration, open surgery, high-risk procedure type, and operative time.
  • The automated model achieved a higher area under the curve (AUC) of 0.868 compared to the manual score (AUC: 0.831).
  • Open surgery, procedure risk, operative time, and procedure urgency were the most impactful predictors of SSI.

Conclusions:

  • Both manual and automated SSI risk prediction models accurately identify patients at elevated risk.
  • These models can be utilized before patient discharge from the operating room to guide proactive interventions and enhance patient safety.
  • The findings support the integration of evidence-based SSI risk assessment into routine surgical workflows.