Eyre, DW;
De Silva, D;
Cole, K;
Peters, J;
Cole, MJ;
Grad, YH;
Demczuk, W;
... Paul, J; + view all
(2017)
WGS to predict antibiotic MICs for Neisseria gonorrhoeae.
Journal of Antimicrobial Chemotherapy
, 72
(7)
pp. 1937-1947.
10.1093/jac/dkx067.
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Abstract
Background: Tracking the spread of antimicrobial-resistant Neisseria gonorrhoeae is a major priority for national surveillance programmes. / Objectives: We investigate whether WGS and simultaneous analysis of multiple resistance determinants can be used to predict antimicrobial susceptibilities to the level of MICs in N. gonorrhoeae. / Methods: WGS was used to identify previously reported potential resistance determinants in 681 N. gonorrhoeae isolates, from England, the USA and Canada, with phenotypes for cefixime, penicillin, azithromycin, ciprofloxacin and tetracycline determined as part of national surveillance programmes. Multivariate linear regression models were used to identify genetic predictors of MIC. Model performance was assessed using leave-one-out cross-validation. / Results: Overall 1785/3380 (53%) MIC values were predicted to the nearest doubling dilution and 3147 (93%) within ±1 doubling dilution and 3314 (98%) within ±2 doubling dilutions. MIC prediction performance was similar across the five antimicrobials tested. Prediction models included the majority of previously reported resistance determinants. Applying EUCAST breakpoints to MIC predictions, the overall very major error (VME; phenotypically resistant, WGS-prediction susceptible) rate was 21/1577 (1.3%, 95% CI 0.8%–2.0%) and the major error (ME; phenotypically susceptible, WGS-prediction resistant) rate was 20/1186 (1.7%, 1.0%–2.6%). VME rates met regulatory thresholds for all antimicrobials except cefixime and ME rates for all antimicrobials except tetracycline. Country of testing was a strongly significant predictor of MIC for all five antimicrobials. / Conclusions: We demonstrate a WGS-based MIC prediction approach that allows reliable MIC prediction for five gonorrhoea antimicrobials. Our approach should allow reasonably precise prediction of MICs for a range of bacterial species.
| Type: | Article |
|---|---|
| Title: | WGS to predict antibiotic MICs for Neisseria gonorrhoeae |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/jac/dkx067 |
| Publisher version: | http://dx.doi.org/10.1093/jac/dkx067 |
| Language: | English |
| Additional information: | Copyright © The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | antibiotics, phenotype, azithromycin, penicillin, ciprofloxacin, gonococcal infection, Canada, cefixime, neisseria gonorrhoeae, genetics, tetracycline, antimicrobials, surveillance, medical, linear regression, dilution technique, dilute (action), malnutrition-inflammation-cachexia syndrome |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1540335 |
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