Carbapenem non-susceptibility of Klebsiella pneumoniae isolates in hospitals from 2011 to 2016, data from the German Antimicrobial Resistance Surveillance (ARS)

Abstract Background Carbapenem resistance in Klebsiella pneumoniae is of significant public health concern and recently spread across several countries. We investigated the extent of carbapenem non-susceptibility in K. pneumoniae isolates in Germany. Methods We analysed 2011–2016 data from the German Antimicrobial Resistance Surveillance (ARS) System, which contains routine data of antimicrobial susceptibility testing from voluntarily participating German laboratories. Klebsiella pneumoniae isolates tested resistant or intermediate against an antibiotic were classified as non-susceptible. Results We included 154,734 isolates from 655 hospitals in the analysis. Carbapenem non-susceptibility in K. pneumoniae isolates was low in Germany 0.63% (95% CI 0.51–0.76%). However, in continuously participating hospitals the number of K. pneumoniae isolates almost doubled and we found evidence for a slowly increasing trend for non-susceptibility (OR = 1.20 per year, 95% CI 1.09–1.33, p < 0.001). Carbapenem non-susceptibility was highest among isolates from patients aged 20–39 in men but not in women. Moreover, carbapenem non-susceptibility was more frequently reported for isolates from tertiary care, specialist care, and prevention and rehabilitation care hospitals as well as from intensive care units. Co-resistance of carbapenem non-susceptible isolates against antibiotics such as tigecycline, gentamicin, and co-trimoxazole was common. Co-resistance against colistin was 13.3% (95% CI 9.8–17.9%) in carbapenem non-susceptible isolates. Conclusion Carbapenem non-susceptibility in K. pneumoniae isolates in Germany is still low. However, it is slowly increasing and in the light of the strong increase of K. pneumoniae isolates over the last year this poses a significant challenge to public health. Continued surveillance to closely monitor trends as well as infection control and antibiotic stewardship activities are necessary to preserve treatment options

A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis

Accurate prediction of water quality parameters (WQPs) is an important task in the management of water resources. Artificial neural networks (ANNs) are frequently applied for dissolved oxygen (DO) prediction, but often only their interpolation performance is checked. The aims of this research, beside interpolation, were the determination of extrapolation performance of ANN model, which was developed for the prediction of DO content in the Danube River, and the assessment of relationship between the significance of inputs and prediction error in the presence of values which were of out of the range of training. The applied ANN is a polynomial neural network (PNN) which performs embedded selection of most important inputs during learning, and provides a model in the form of linear and non-linear polynomial functions, which can then be used for a detailed analysis of the significance of inputs. Available dataset that contained 1912 monitoring records for 17 water quality parameters was split into a "regular" subset that contains normally distributed and low variability data, and an "extreme" subset that contains monitoring records with outlier values. The results revealed that the non-linear PNN model has good interpolation performance (R-2 = 0.82), but it was not robust in extrapolation (R-2 = 0.63). The analysis of extrapolation results has shown that the prediction errors are correlated with the significance of inputs. Namely, the out-of-training range values of the inputs with low importance do not affect significantly the PNN model performance, but their influence can be biased by the presence of multi-outlier monitoring records. Subsequently, linear PNN models were successfully applied to study the effect of water quality parameters on DO content. It was observed that DO level is mostly affected by temperature, pH, biological oxygen demand (BOD) and phosphorus concentration, while in extreme conditions the importance of alkalinity and bicarbonates rises over pH and BOD