58 Chapter 4 median=7 mm) as compared with the AmpC-negative group (Figure S3). The boxplots of the D68C test illustrate that there was no clear separation between hyperproducer (D68C C-A median=15 mm; D68C D-B median=14 mm) and pampC-positive strains (D68C C-A median=15 mm; D68C D-B median=15 mm) based on zone inhibition differences (Figure S4). MICs in relation to the presence of different ampC genes A ridge plot was generated to visualize the Etest MICs for each genotype for all 172 E. coli strains (Figure 2). The plot reveals that negative strains showed MICs of ceftazidime of ≤4 mg/L and of cefotaxime of ≤3 mg/L. For hyperproducers, MICs of ceftazidime were predominantly in the range of 0.75–12 mg/L and cefotaxime MICs were in the range of 0.38–4 mg/L. Isolates that harboured blaCMY showed ceftazidime MICs of 1.5– 256 mg/L and cefotaxime MICs of 1.5–32 mg/L. In contrast, blaDHA-1-positive strains showed lower MICs of 3GCs (ceftazidime 2–8 mg/L and cefotaxime 1–4 mg/L), which overlapped with MIC ranges for hyperproducing strains. Performance of susceptibility tests to predict ampC type Training of the RPART model and the cross-validation on the training set (n=84) were performed to predict whether strains have a negative, hyperproducer or pampC genotype. The model indicated that training with Etest MICs performed best (Figure 3). It had the highest average accuracy (0.83) and the performance was significantly better than the AmpC Confirm Kit (0.73) and the D68C test (0.67). Furthermore, cross-validation using Etest MICs resulted in the smallest quartile, implying that the model could be extra stable when applied to other datasets. Therefore, we selected the decision tree trained on Etest MICs as the final decision tree model to test performance on training and validation sets.
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