Population distributions of minimum inhibitory concentration – increasing accuracy and utility

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To generate continuous minimum inhibitory concentration (MIC) data that describes the discrete nature of experimentally derived population MIC data.

Methods and Results

A logistic model was fitted to experimentally derived MIC population cumulative distributions from clinical isolates of Haemophilus influenzae, Moraxella catarrhalis, Streptococcus pneumoniae and Staphylococcus aureus (European Committee on Antimicrobial Susceptibility Testing, BSAC and MYSTIC population susceptibility databases). From the model continuous distributions of population susceptibility were generated. The experimentally observed population distributions based on discrete MIC could be reproduced from this underlying continuous distribution. Monte Carlo (MC) simulation was used to confirm findings. Where the discrete experimental data contained few or no isolates with MIC greater or less than the antimicrobial concentration range tested, the true mean MIC was a factor of 0·707 times that normally reported and may be of little clinical significance. Where data contained isolates beyond the range of concentration used, the true MIC was dependent on the SD and the number of isolates and could be clinically significant. Subpopulations of differing susceptibilities could be modelled successfully using a modified logistic equation: this allows a more accurate examination of the data from these databases.


The mean MIC and SD of population data currently reported are incorrect as the method of obtaining such parameters relies on normally distributed data which current MIC population data are not.

Significance and Impact of the Study

Obtaining the distribution parameters from the underlying continuous distribution of MIC can be carried out using a simple logistic equation. MC simulation using these values allows easy visualization of the discrete data. The analyses of subpopulations within the data should increase the usefulness of horizontal studies.

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