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Automated detection of external ventricular and lumbar drain-related meningitis using laboratory and microbiology

Maaike S M van Mourik1, Rolf H H Groenwold, Jan Willem Berkelbach van der Sprenkel

  • 1Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, The Netherlands. m.s.m.vanmourik-2@umcutrecht.nl

Plos One
|August 11, 2011
PubMed
Abstract

Insights

A new prediction model can accurately detect drain-related meningitis (DRM) using routine clinical data, improving infection surveillance. This automated approach offers a feasible alternative to manual methods for monitoring nosocomial infections.

Area of Science:

  • Medical Informatics
  • Infectious Diseases
  • Epidemiology

Background:

  • Healthcare-associated infections (HAIs) require robust monitoring for effective infection control and hospital benchmarking.
  • Manual surveillance for infections like drain-related meningitis (DRM) is labor-intensive and prone to errors.
  • Developing automated methods for detecting HAIs is crucial for improving efficiency and accuracy.

Purpose of the Study:

  • To develop and validate a prediction model for the retrospective detection of drain-related meningitis (DRM).
  • To utilize routinely collected data from a clinical data warehouse for automated HAI detection.
  • To establish a feasible and accurate method for monitoring DRM rates.

Main Methods:

  • A logistic regression model was developed using data from patients with external ventricular or lumbar drains (2004-2009).
  • Exclusion criteria were applied to ensure data quality and relevance for DRM prediction.
  • Multiple imputation for missing data and bootstrapping were used to enhance model generalizability and accuracy.

Main Results:

  • The final model included variables such as drain characteristics, blood leukocyte count, C-reactive protein, CSF analysis, and antibiotic use.
  • The prediction model demonstrated excellent discriminatory power (AUC 0.97), with high sensitivity (98.8%) and specificity (87.9%).
  • Predicted yearly DRM rates closely aligned with observed rates, validating the model's performance.

Conclusions:

  • A prediction model utilizing multi-source data from clinical data warehouses can accurately quantify DRM rates.
  • Automated detection of DRM is feasible and statistically sound.
  • This approach can be extended to monitor other types of nosocomial infections effectively.

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