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STATISTICAL METHODS FOR AUTOMATED DRUG SUSCEPTIBILITY TESTING: BAYESIAN MINIMUM INHIBITORY CONCENTRATION PREDICTION FROM GROWTH CURVES

• Xi Zhou, Division of Biostatistics and Epidemiology, Department of Public Health, Weill Medical College of Cornell University
• Merlise A. Clyde, Department of Statistical Science, Duke University
• James Garrett, Manager, Bioinformatics, Algorithms and Non-Clinical Statistics Group, Becton-Dickinson Diagnostic Systems
• Viridiana Lourdes, Vice-President, Investment Management, Morgan Stanley
• Michael O'Connell, Director of Life Sciences, Insightful Corporation
• Giovanni Parmigiani, The Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University & Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
• David J. Turner, Senior Scientist, ID/AST Research and Development, Becton-Dickinson Diagnostic Systems
• Tim Wiles, Senior Scientist, ID/AST Research and Development, Becton-Dickinson Diagnostic Systems

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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 micro-organisms in clinical samples. Over seventy-five studies to date have shown this new method provides improved estimation of MICs over existing approaches.

Subject Area:

Laboratory and Basic Science Research

Suggested Citation:

Xi Zhou, Merlise A. Clyde, James Garrett, Viridiana Lourdes, Michael O'Connell, Giovanni Parmigiani, David J. Turner, and Tim Wiles, "STATISTICAL METHODS FOR AUTOMATED DRUG SUSCEPTIBILITY TESTING: BAYESIAN MINIMUM INHIBITORY CONCENTRATION PREDICTION FROM GROWTH CURVES" (January 2008). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 163.
http://www.bepress.com/jhubiostat/paper163