64 Chapter 4 blaCMY-2 is the predominant pampC gene in Enterobacterales in the Netherlands, which is consistent with the number of blaCMY-2-positive strains in our datasets (E. Ascelijn Reuland et al. 2015; E. Den Drijver et al. 2018). The CMY group, including blaCMY-2, is the most prevalent pampC gene (Jacoby 2009). It should be noted that a higher prevalence of other pampC genes could influence algorithm outcomes. For example, blaACC will be omitted because it has a cefoxitin-susceptible phenotype (Philippon, Arlet, and Jacoby 2002). Moreover, blaDHA-1 was included in our panels and use of the decision tree model resulted in a lower discriminatory value for this pampC variant. So, our decision tree is probably most optimal in settings with relatively high amounts of blaCMY. Strains from validation set 1 were only sequenced when D68C was positive for AmpC. Analyses of the MICs for D68C-negative samples illustrate that MICs of cefotaxime are <6 mg/L (Figure S5). Moreover, it seems unlikely that these strains would have contained pampC, as previous studies have shown high sensitivity and specificity with the D68C test for the detection of AmpC production (Ingram et al. 2011; Nourrisson et al. 2015). This present study focused on E. coli, being the most common and well-studied pathogen (Weinstein, Gaynes, and Edwards 2005). Nonetheless, there are other species with inducible expression of campC, such as Enterobacter spp., Citrobacter freundii and Pseudomonas aeruginosa (Jacoby 2009). Our study outcomes may not be extrapolated to these other species. In conclusion, the use of a cefotaxime MIC test is an inexpensive and relatively quick method to detect pAmpC-producing E. coli. Therefore, the proposed decision tree could serve as a good alternative to EUCAST guidelines, which include cloxacillin synergy testing in combination with PCR. A comparison between the two algorithms in a clinical setting may be of interest for future studies. WGS combined with machine learning algorithms is useful to improve laboratory and infection control methods (Quainoo et al. 2017; Nguyen et al. 2019). We used a simplified version of machine learning, which is directly applicable in current settings. Results show great potential for further optimization of present microbiological methods. Future work may use an extensive amount of data and state-of-the-art machine learning to improve accuracy of beta-lactamase detection.
RkJQdWJsaXNoZXIy MTk4NDMw