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Identifying surgical site infections in electronic health data using predictive models

Robert W Grundmeier1,2, Rui Xiao3, Rachael K Ross4

  • 1Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.

Journal of the American Medical Informatics Association : JAMIA
|July 9, 2018
PubMed

Insights

This study developed a prediction rule using electronic health record data to detect surgical site infections (SSI) in children after ambulatory surgery, achieving high sensitivity and positive predictive value for improved patient safety.

Area of Science:

  • Medical Informatics
  • Surgical Infections
  • Predictive Analytics

Background:

  • Surgical site infections (SSI) are a significant concern following ambulatory surgery.
  • Effective surveillance methods are needed to identify patients requiring further investigation.
  • Electronic Health Record (EHR) data offers a potential resource for SSI detection.

Purpose of the Study:

  • To prospectively derive and validate a prediction rule for detecting surgical site infections (SSI) after ambulatory surgery.
  • To assess the utility of EHR data, including free text, in developing an accurate SSI prediction model.
  • To achieve a sensitivity exceeding 80% and a positive predictive value (PPV) above 10%.

Main Methods:

  • Analysis of EHR data from 7910 ambulatory surgeries across 4 facilities.
  • Development of prediction rules using regularized logistic regression and random forests on a derivation dataset (30 months).
  • Validation of the prediction rule on a subsequent dataset (10 months), evaluating models with and without free text data.

Main Results:

  • An optimal random forest prediction rule incorporating free text data demonstrated a sensitivity of 0.9 and a PPV of 0.28.
  • Antibiotic prescription within 60 days showed improved sensitivity (0.84) and PPV (0.28) in the validation set.
  • 234 SSIs were identified among 7910 surgeries analyzed.

Conclusions:

  • EHR data, particularly when including free text, can effectively support SSI surveillance.
  • The derived prediction rule shows promise for identifying children at risk of SSI after ambulatory surgery.
  • This approach can enhance early detection and management of SSIs, improving patient outcomes.
Abstract

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