×

Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves. (English) Zbl 1166.62087

Summary: Determination of the minimum inhibitory concentration (MIC) of a drug that prevents microbial growth is an important step for managing patients with infections. 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.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
65C60 Computational problems in statistics (MSC2010)
62C10 Bayesian problems; characterization of Bayes procedures
62F15 Bayesian inference
PDFBibTeX XMLCite
Full Text: DOI arXiv

References:

[1] Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory (B. Petrox and F. Caski, eds.) 267-281. Akadémiai Kiado, Budapest. · Zbl 0283.62006
[2] Akaike, H. (1983a). Information measures and model selection (STMA V25 937). Bulletin of the International Statistical Institute 50 277-290. · Zbl 0578.62059
[3] Akaike, H. (1983b). On minimum information prior distributions. Ann. Inst. Statist. Math. 35 139-149. · Zbl 0525.62005 · doi:10.1007/BF02480970
[4] Barenfanger, J., Drake, C. and Kacich, G. (1999). Clinical and financial benefits of rapid bacterial identification and antimicrobial susceptibility testing. Journal of Clinical Microbiology 37 1415-1418.
[5] CLSI (2006). Methods for Dilution Antimicrobial Susceptibility Testing for Bacteria that Grow Aerobically; Approved Standard M7-A7, 7th ed. Clinical and Laboratory Standards Institute, Wayne, PA.
[6] CLSI (2008). Performance Standards for Antimicrobial Susceptibility Testing; Eightenenth Informational Supplement M100-S18, 18th ed. Clinical and Laboratory Standards Institute, Wayne, PA.
[7] Clyde, M. and George, E. I. (2004). Model uncertainty. Statist. Sci. 19 81-94. · Zbl 1062.62044 · doi:10.1214/088342304000000035
[8] Deal, M., Votta, M., Turng, S. H. B., Wiles, T. and Reuben, J. (2002). Detection of glycopeptide intermediate or resistant staphylococcus aureus strains using BD Phoenix tm automated microbiology system. In 101st General Meeting of the American Society for Microbiology . Salt Lake City, Utah. Poster C-119.
[9] Donay, J.-L., Mathieu, D., Fernandes, P., Prégermain, C., Bruel, P., Wargnier, A., Casin, I., Weill, F. X., Lagrange, P. H. and Herrmann, J. L. (2004). Evaluation of the automated Phoenix system for potential routine use in the clinical microbiology laboratory. Journal of Clinical Microbiology 42 1542-1546.
[10] Fahr, A.-M., Eigner, U., Armbrust, M., Caganic, A., Dettori, G., Chezzi, C., Bertoncini, L., Benecchi, M. and Menozzi, M. G. (2003). Two-center collaborative evaluation of the performance of the BD Phoenix automated microbiology system for identification and antimicrobial susceptibility testing of Enterococcus spp. and Staphylococcus spp. Journal of Clinical Microbiology 41 1135-1142.
[11] FDA (2007). Class II Special Controls Guidance Document: Antimicrobial Susceptibility Test (AST) System; Guidance for Industry and FDA . Center for Devices and Radiological Health, Food and Drug Administration, U.S. Department of Health and Human Services, Washington, DC.
[12] Ferraro, M. J. and Jorgensen, J. H. (2003). Susceptibility testing instrumentation and computerized expert systems for data analysis and interpretation. In Manual of Clinical Microbiology (P. R. Murray, E. J. Baron, J. H. Jorgensen, M. A. Pfaller and R. H. Yolken, eds.) 208-217. Am. Soc. Microbiol., Washington, DC.
[13] Hoeting, J. A., Madigan, D., Raftery, A. E. and Volinsky, C. T. (1999). Bayesian model averaging: A tutorial (with discussion). Statist. Sci. 14 382-417. Corrected version at http://www.stat.washington.edu/www/research/online/hoeting1999.pdf. · Zbl 1059.62525 · doi:10.1214/ss/1009212519
[14] Horstkotte, M. A., Knobloch, J. K.-M., Rohde, H., Dobinsky, S. and Mack, D. (2004). Evaluation of the BD Phoenix automated microbiology system for detection of methicillin resistance in coagulase-negative staphylococci. Journal of Clinical Microbiology 42 5041-5046.
[15] Jorgensen, J. H. and Turnidge, J. D. (2003). Susceptibility test methods: Dilution and disk diffusion methods. In Manual of Clinical Microbiology (P. R. Murray, E. J. Baron, J. H. Jorgensen, M. A. Pfaller and R. H. Yolken, eds.) 1108-1127. Am. Soc. Microbiol., Washington, DC.
[16] Kass, R. E. and Raftery, A. E. (1995). Bayes factors. J. Amer. Statist. Assoc. 90 773-795. · Zbl 0846.62028 · doi:10.2307/2291091
[17] Schwarz, G. (1978). Estimating the dimension of a model. Ann. Statist. 6 461-464. · Zbl 0379.62005 · doi:10.1214/aos/1176344136
[18] Tenover, F. C., Kalsi, R. K., Williams, P. P., Carey, R. B., Stocker, S., Lonsway, D., Rasheed, J. K., Biddle, J. W., J. E. McGowan, Jr. and Hanna, B. (2006). Carbapenem resistance in klebsiella pneumoniae not detected by automated susceptibility testing. Emerging Infectious Diseases 12 1209-1213.
[19] Turnidge, J. D., Ferraro, M. J. and Jorgensen, J. H. (2003). Susceptibility test methods: General considerations. In Manual of Clinical Microbiology (P. R. Murray, E. J. Baron, J. H. Jorgensen, M. A. Pfaller and R. H. Yolken, eds.) 1102-1107. Am. Soc. Microbiol., Washington, DC.
[20] Wheat, P. F. (2001). History and development of antimicrobial susceptibility testing methodology. Journal of Antimicrobial Chemotherapy 48 1-4.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.