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

## TL;DR

CarbaDetector is a machine learning tool that quickly and accurately detects carbapenemase-producing bacteria using antibiotic test results, improving detection efficiency.

## Contribution

The novel contribution is a high-performance machine learning model for rapid detection of carbapenemase-producing Enterobacterales using disk diffusion data.

## Key findings

- CarbaDetector achieved 96.6% sensitivity and 84.4% specificity on the training dataset.
- The model outperformed EUCAST and CA-SFM algorithms in specificity for detecting carbapenemase production.
- CarbaDetector is available as a web-app, reducing the need for confirmatory testing and saving time.

## Abstract

Carbapenemase-producing Enterobacterales (CPE) are considered among the highest threats to global health by WHO. Their detection is difficult and time-consuming. We developed a random-forest machine learning (ML) model, CarbaDetector, to predict carbapenemase production from inhibition zone diameters of eight antibiotics, using 385 isolates for training with whole genome sequencing as reference. Validation on two external datasets (A = 282, B = 518 isolates) shows high performance: sensitivity/specificity are 96.6%/84.4% (training), 96.3%/86.1% (A), and 91.2%/87.0% (B, five antibiotics). In contrast, the algorithms of EUCAST and the Antibiogram Committee of the French Society of Microbiology (CA-SFM) exhibit lower specificity (8.2% and 40.1%, respectively on the training dataset). In this work, we show that CarbaDetector, available as a web-app, reduces unnecessary confirmatory testing and accelerates the time to result. This approach offers high sensitivity and improved specificity compared to standard algorithms and has the potential to improve CPE detection, especially in resource-limited settings.

Carbapenems are last-resort antibiotics, but resistance is rising due to hydrolyzing enzymes called carbapenemases. The authors present a machine learning algorithm and web-app to rapidly predict carbapenemase-production in Enterobacterales.