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CarbaDetector: a machine learning model for detecting carbapenemase-producing Enterobacterales from disk diffusion

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Carbapenemase-producing Enterobacterales (CPE) are a global health threat. A new machine learning model, CarbaDetector, accurately predicts CPE from antibiotic zone diameters, improving detection speed and specificity over current methods.

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Area of Science:

  • Medical Microbiology
  • Computational Biology
  • Infectious Diseases

Background:

  • Carbapenemase-producing Enterobacterales (CPE) represent a critical global health threat.
  • Current methods for CPE detection are often slow and challenging.
  • Accurate and rapid detection is crucial for effective infection control and treatment.

Purpose of the Study:

  • To develop and validate a machine learning model (CarbaDetector) for predicting carbapenemase production in Enterobacterales.
  • To compare the performance of CarbaDetector against existing standard algorithms.
  • To assess the potential of CarbaDetector in reducing confirmatory testing and accelerating time to result.

Main Methods:

  • Development of a random-forest machine learning model (CarbaDetector).
  • Training dataset comprised 385 isolates with whole genome sequencing as reference.
  • Validation on two independent external datasets (282 and 518 isolates).
  • Prediction based on inhibition zone diameters of eight antibiotics.

Main Results:

  • CarbaDetector demonstrated high performance: 96.6% sensitivity and 84.4% specificity on the training set.
  • External validation showed excellent performance: 96.3% sensitivity/86.1% specificity (Dataset A) and 91.2% sensitivity/87.0% specificity (Dataset B).
  • CarbaDetector significantly outperformed EUCAST and CA-SFM algorithms in specificity (8.2% and 40.1% respectively on training data).

Conclusions:

  • CarbaDetector offers a sensitive and specific method for predicting carbapenemase production.
  • The model, available as a web-app, can reduce unnecessary confirmatory tests and speed up results.
  • CarbaDetector has the potential to enhance CPE detection, particularly in resource-limited settings.