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Utilization of vector autoregressive and linear transfer models to follow up the antibiotic resistance spiral in Gram-negative bacteria from cephalosporin consumption to colistin resistance.
Clinical Infectious Diseases 2018 December 19
Background: Increasing antibiotic resistance may reciprocally affect consumption and lead to using broader-spectrum alternatives; a vicious cycle that may gradually limit therapeutic options. The study aims at demonstrating the abovementioned vicious cycle in Gram-negative bacteria and at showing the utility of vector autoregressive (VAR) models for analysis of time-series in explanatory and dependent roles simultaneously.
Methods: Monthly drug consumption data in defined daily doses per 100 bed-days and incidence densities of Gram-negative bacteria (Escherichia coli, Klebsiella spp., Pseudomonas aeruginosa and Acinetobacterbaumannii) resistant to cephalosporins or to carbapenems were analysed using VAR models. These were compared to linear transfer models used earlier.
Results: In case of all Gram-negative bacteria, cephalosporin consumption led to increasing cephalosporin resistance, which provoked carbapenem use and consequent carbapenem resistance, and finally increased colistin consumption, exemplifying the vicious cycle. Different species were involved in different manner, e.g. cephalosporin resistant Klebsiella spp. provoked carbapenem use less than E. coli or association between carbapenem resistance of P. aeruginosa and colistin use was weaker than in case of A. baumannii. Colistin use led to decreased carbapenem use and decreased carbapenem resistance of P. aeruginosa but not of A. baumannii.
Conclusions: Vector autoregressive models allow analysis of consumption and resistance series in a bidirectional manner. The reconstructed resistance spiral involved cephalosporin use augmenting cephalosporin resistance primarily in E. coli. This led to increased carbapenem use, provoking spread of carbapenem resistant A. baumannii and consequent colistin use. Emergence of panresistance is fuelled by such antibiotic resistance spirals.
Methods: Monthly drug consumption data in defined daily doses per 100 bed-days and incidence densities of Gram-negative bacteria (Escherichia coli, Klebsiella spp., Pseudomonas aeruginosa and Acinetobacterbaumannii) resistant to cephalosporins or to carbapenems were analysed using VAR models. These were compared to linear transfer models used earlier.
Results: In case of all Gram-negative bacteria, cephalosporin consumption led to increasing cephalosporin resistance, which provoked carbapenem use and consequent carbapenem resistance, and finally increased colistin consumption, exemplifying the vicious cycle. Different species were involved in different manner, e.g. cephalosporin resistant Klebsiella spp. provoked carbapenem use less than E. coli or association between carbapenem resistance of P. aeruginosa and colistin use was weaker than in case of A. baumannii. Colistin use led to decreased carbapenem use and decreased carbapenem resistance of P. aeruginosa but not of A. baumannii.
Conclusions: Vector autoregressive models allow analysis of consumption and resistance series in a bidirectional manner. The reconstructed resistance spiral involved cephalosporin use augmenting cephalosporin resistance primarily in E. coli. This led to increased carbapenem use, provoking spread of carbapenem resistant A. baumannii and consequent colistin use. Emergence of panresistance is fuelled by such antibiotic resistance spirals.
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