62 Chapter 4 Model performance on validation sets To perform a more extensive evaluation of the decision tree, the model was tested by using two validation sets as input, as described in the Materials and methods section. The performance of the algorithm on validation set 1 (n=47) resulted in an accuracy of 0.79 (95% CI=0.64–0.89) (Table 2). Using validation set 2 (n=41), the decision tree achieved an accuracy of 0.85 (95% CI=0.71–0.94) (Table 2). More details on the performance of the two- and three-class models can be found in the confusion matrix in Table S2 and the confusion matrix in Table S3. Discussion To the best of our knowledge, this is the first study that combined susceptibility testing, WGS and simple supervised machine learning to develop a user-friendly algorithm to determine the likelihood of pampC in cefoxitin-resistant and ESBL-negative E. coli strains (Figure 4). The decision tree requires a single cefotaxime Etest as input, is easy to apply in most laboratory settings and results in an accurate detection of pampCpositive strains. Timely and more accurate identification of pampC isolates improves infection control practices and minimizes unnecessary and costly isolation measures. In the current setting a genotypic confirmation is recommended to differentiate between pAmpC and cAmpC production in cefoxitin-resistant E. coli as phenotypic confirmation is not reliable (Martinez and Simonsen 2017). Our comparison of the AmpC Confirm Kit, D68C test and Etests shows that disc-diffusion zone differences are useful to detect AmpC production in general, but are inadequate to differentiate between pAmpC and cAmpC production (Figures S2–S4). Therefore, rapid and accurate differentiation is needed to further improve infection control policies. Introducing an Etest in combination with the proposed algorithm illustrates that accurate phenotypic detection and identification of pampC harbouring E. coli is feasible. A relationship between 3GC resistance and the presence of pAmpC has been reported in the literature (Polsfuss et al. 2011; Edquist et al. 2013; Aarestrup et al. 2010). Although pAmpC-producing E. coli isolates in this present study showed higher MICs of 3GCs than isolates without pampC genes, the distributions of MIC between pAmpC and hyperproducing cAmpC isolates overlap. This overlap was mainly caused by the E. coli strains that produced DHA-1 enzymes. Edquist et al also concluded that clinical resistance to cefotaxime and/or ceftazidime as a screening criterion for
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