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Forecasting carbapenem resistance from antimicrobial consumption surveillance: Lessons learnt from an OXA-48-producing Klebsiella pneumoniae outbreak in a West London renal unit.

Gharbi M, Moore LS, Gilchrist M, Thomas CP, Bamford K, Brannigan ET, Holmes AH - Int. J. Antimicrob. Agents (2015)

Bottom Line: Analysis of alternative antimicrobials showed a significant increase in amikacin consumption post-intervention from 0.54 to 3.41 DDD/100OBD/year (slope +0.72, 95% CI 0.29-1.15; P=0.01).Total antimicrobials significantly decreased from 176.21 to 126.24 DDD/100OBD/year (P=0.05).Further validation using real-time data is needed.

View Article: PubMed Central - PubMed

Affiliation: The National Centre for Infection Prevention and Management, Imperial College London, Du Cane Road, London W12 ONN, UK. Electronic address: m.gharbi@imperial.ac.uk.

No MeSH data available.


Related in: MedlinePlus

Cross-correlation between meropenem consumption lag −1 (the preceding year) and the incidence rate of OXA-48-producing Klebsiella pneumoniae in a West London renal unit from 2008–2009 to 2013–2014.
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fig0005: Cross-correlation between meropenem consumption lag −1 (the preceding year) and the incidence rate of OXA-48-producing Klebsiella pneumoniae in a West London renal unit from 2008–2009 to 2013–2014.

Mentions: The interrelationship among both times series, meropenem drug consumption and incidence of resistant isolates was identified using cross-correlations at different time lags (in increments of 1 year). After ensuring stationarity, absence of residual autocorrelation and a normal distribution of disturbances, meropenem consumption at lag −1 (i.e. meropenem consumption the preceding year) was the most positively correlated with the incidence of OXA-48-producing K. pneumoniae (Fig. 1) (Pearson correlation r = 0.71; P = 0.005). The ARIMA model including meropenem consumption at lag −1 as a predictor was fitted to the incidence of OXA-48-producing K. pneumoniae [meropenem coefficient in the model = 1.07, 95% confidence interval (CI) 0.10–2.05; P = 0.03]. This increased the model's accuracy in estimating the incidence of OXA-48-producing K. pneumoniae compared with the univariate ARIMA model without any predictors (RMSE = 1.41 and R2 = 79% for the multivariate model versus RMSE = 2.92 and R2 = 15% for the univariate model). The difference between the two RMSE were statistically different with Wilcoxon signed-rank test (P < 0.001). One-step (year)-ahead forecasting for 2014–2015 was then applied to the incidence rate of resistant cases using the multiple time series model identified previously. The number of cases/100,000 OBD for 2014–2015 was estimated to be 4.96 (95% CI 2.53–7.39) (Fig. 2).


Forecasting carbapenem resistance from antimicrobial consumption surveillance: Lessons learnt from an OXA-48-producing Klebsiella pneumoniae outbreak in a West London renal unit.

Gharbi M, Moore LS, Gilchrist M, Thomas CP, Bamford K, Brannigan ET, Holmes AH - Int. J. Antimicrob. Agents (2015)

Cross-correlation between meropenem consumption lag −1 (the preceding year) and the incidence rate of OXA-48-producing Klebsiella pneumoniae in a West London renal unit from 2008–2009 to 2013–2014.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4526541&req=5

fig0005: Cross-correlation between meropenem consumption lag −1 (the preceding year) and the incidence rate of OXA-48-producing Klebsiella pneumoniae in a West London renal unit from 2008–2009 to 2013–2014.
Mentions: The interrelationship among both times series, meropenem drug consumption and incidence of resistant isolates was identified using cross-correlations at different time lags (in increments of 1 year). After ensuring stationarity, absence of residual autocorrelation and a normal distribution of disturbances, meropenem consumption at lag −1 (i.e. meropenem consumption the preceding year) was the most positively correlated with the incidence of OXA-48-producing K. pneumoniae (Fig. 1) (Pearson correlation r = 0.71; P = 0.005). The ARIMA model including meropenem consumption at lag −1 as a predictor was fitted to the incidence of OXA-48-producing K. pneumoniae [meropenem coefficient in the model = 1.07, 95% confidence interval (CI) 0.10–2.05; P = 0.03]. This increased the model's accuracy in estimating the incidence of OXA-48-producing K. pneumoniae compared with the univariate ARIMA model without any predictors (RMSE = 1.41 and R2 = 79% for the multivariate model versus RMSE = 2.92 and R2 = 15% for the univariate model). The difference between the two RMSE were statistically different with Wilcoxon signed-rank test (P < 0.001). One-step (year)-ahead forecasting for 2014–2015 was then applied to the incidence rate of resistant cases using the multiple time series model identified previously. The number of cases/100,000 OBD for 2014–2015 was estimated to be 4.96 (95% CI 2.53–7.39) (Fig. 2).

Bottom Line: Analysis of alternative antimicrobials showed a significant increase in amikacin consumption post-intervention from 0.54 to 3.41 DDD/100OBD/year (slope +0.72, 95% CI 0.29-1.15; P=0.01).Total antimicrobials significantly decreased from 176.21 to 126.24 DDD/100OBD/year (P=0.05).Further validation using real-time data is needed.

View Article: PubMed Central - PubMed

Affiliation: The National Centre for Infection Prevention and Management, Imperial College London, Du Cane Road, London W12 ONN, UK. Electronic address: m.gharbi@imperial.ac.uk.

No MeSH data available.


Related in: MedlinePlus