The Annals of Applied Statistics

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

Xi Kathy Zhou, Merlise A. Clyde, James Garrett, Viridiana Lourdes, Michael O’Connell, Giovanni Parmigiani, David J. Turner, and Tim Wiles

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat., Volume 3, Number 2 (2009), 710-730.

Dates
First available in Project Euclid: 22 June 2009

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1245676192

Digital Object Identifier
doi:10.1214/08-AOAS217

Mathematical Reviews number (MathSciNet)
MR2750679

Zentralblatt MATH identifier
1166.62087

Keywords
Bayes BIC decision theory logistic regression model selection model uncertainty

Citation

Zhou, Xi Kathy; Clyde, Merlise A.; Garrett, James; Lourdes, Viridiana; O’Connell, Michael; Parmigiani, Giovanni; Turner, David J.; Wiles, Tim. Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves. Ann. Appl. Stat. 3 (2009), no. 2, 710--730. doi:10.1214/08-AOAS217. https://projecteuclid.org/euclid.aoas/1245676192


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