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Predicting postoperative surgical site infection with administrative data: a random forests algorithm.

Yelena Petrosyan1, Kednapa Thavorn2,3,4,5, Glenys Smith6

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Summary
This summary is machine-generated.

Routinely collected health administrative data can effectively identify surgical site infections (SSIs) using machine learning algorithms. This approach offers an efficient alternative to primary data collection for monitoring postoperative SSIs.

Keywords:
Administrative dataData miningMachine learningPredictive modelingRandom forestsSurgical site infection

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Surgical Outcomes Research

Background:

  • Primary data collection for surgical site infections (SSIs) is resource-intensive.
  • Routinely collected administrative data presents a potential alternative for SSI monitoring.
  • Developing efficient algorithms is key to leveraging administrative data.

Purpose of the Study:

  • To derive and validate algorithms for identifying SSIs using health administrative data.
  • To assess the feasibility of using machine learning for SSI detection.
  • To develop a risk score system for predicting SSIs.

Main Methods:

  • Utilized machine learning algorithms (Random Forests, logistic regression) on linked administrative and surgical quality datasets.
  • Developed parsimonious models to predict SSI status within 30 days post-surgery.
  • Validated models using risk score methodology and internal datasets.

Main Results:

  • A final model incorporating diagnostic and procedure codes demonstrated high accuracy (C-statistic 0.91).
  • The model showed excellent discrimination and calibration.
  • Identified 5.5% of 14,351 patients with SSIs.

Conclusions:

  • Health administrative data is effective for identifying SSIs.
  • Machine learning algorithms accurately predict postoperative SSIs.
  • External validation is necessary before routine implementation.