Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves

TL;DR

This paper introduces a Bayesian probabilistic model for accurately estimating antimicrobial MICs from bacterial growth curves, enhancing automation and precision in clinical susceptibility testing.

Contribution

A novel Bayesian approach for MIC prediction that incorporates multiple growth features and provides probabilistic assessments, improving over existing methods.

Findings

Improved MIC estimation accuracy over traditional methods.

Automated integration into clinical susceptibility systems.

Validated on over seventy-five clinical studies.

Abstract

Determination of the minimum inhibitory concentration (MIC) of a drug that prevents microbial growth is an important step for managing patients with infections. In this paper we present a novel probabilistic approach that accurately estimates MICs based on a panel of multiple curves reflecting features of bacterial growth. We develop a probabilistic model for determining whether a given dilution of an antimicrobial agent is the MIC given features of the growth curves over time. Because of the potentially large collection of features, we utilize Bayesian model selection to narrow the collection of predictors to the most important variables. In addition to point estimates of MICs, we are able to provide posterior probabilities that each dilution is the MIC based on the observed growth curves. The methods are easily automated and have been incorporated into the Becton--Dickinson PHOENIX automated susceptibility system that rapidly and accurately classifies the resistance of a large number of microorganisms in clinical samples. Over seventy-five studies to date have shown this new method provides improved estimation of MICs over existing approaches.