OBJECTIVES: To evaluate the impact of a high-risk antibiotic stewardship programme on reducing antibiotic use and on hospital Clostridium difficile infection (CDI) incidence rates. A secondary objective was to present the possible utility of time-series analysis as an antibiotic risk classification tool. METHODS: This was an interventional, retrospective, ecological investigation in a medium-sized hospital over 6.5 years (January 2004 to June 2010). The intervention was the restriction of high-risk antibiotics (second-generation cephalosporins, third-generation cephalosporins, fluoroquinolones and clindamycin). Amoxicillin/clavulanic acid and macrolides were classified as medium-risk antibiotics based on time-series analysis findings and their use was monitored. The intervention was evaluated by segmented regression analysis of interrupted time series. RESULTS: The intervention was associated with a significant change in level of use of high-risk antibiotics (coefficient -17.3, P < 0.0001) and with a borderline significant trend change in their use being reduced by 0.156 defined daily doses/100 bed-days per month (P = 0.0597). The reduction in the use of high-risk antibiotics was associated with a significant change in the incidence trend of CDI (P = 0.0081), i.e. the CDI incidence rate decreased by 0.0047/100 bed-days per month. Analysis showed that variations in the incidence of CDI were affected by the age-adjusted comorbidity index with a lag of 1 month (coefficient 0.137051, P = 0.0182). Significant decreases in slope (coefficient -0.414, P = 0.0309) post-intervention were also observed for the monitored medium-risk antibiotics. CONCLUSIONS: The restriction of the high-risk antibiotics contributed to both a reduction in their use and a reduction in the incidence of CDI in the study site hospital. Time-series analysis can be utilized as a risk classification tool with utility in antibiotic stewardship design and quality improvement programmes.
OBJECTIVES: To evaluate the impact of a high-risk antibiotic stewardship programme on reducing antibiotic use and on hospital Clostridium difficileinfection (CDI) incidence rates. A secondary objective was to present the possible utility of time-series analysis as an antibiotic risk classification tool. METHODS: This was an interventional, retrospective, ecological investigation in a medium-sized hospital over 6.5 years (January 2004 to June 2010). The intervention was the restriction of high-risk antibiotics (second-generation cephalosporins, third-generation cephalosporins, fluoroquinolones and clindamycin). Amoxicillin/clavulanic acid and macrolides were classified as medium-risk antibiotics based on time-series analysis findings and their use was monitored. The intervention was evaluated by segmented regression analysis of interrupted time series. RESULTS: The intervention was associated with a significant change in level of use of high-risk antibiotics (coefficient -17.3, P < 0.0001) and with a borderline significant trend change in their use being reduced by 0.156 defined daily doses/100 bed-days per month (P = 0.0597). The reduction in the use of high-risk antibiotics was associated with a significant change in the incidence trend of CDI (P = 0.0081), i.e. the CDI incidence rate decreased by 0.0047/100 bed-days per month. Analysis showed that variations in the incidence of CDI were affected by the age-adjusted comorbidity index with a lag of 1 month (coefficient 0.137051, P = 0.0182). Significant decreases in slope (coefficient -0.414, P = 0.0309) post-intervention were also observed for the monitored medium-risk antibiotics. CONCLUSIONS: The restriction of the high-risk antibiotics contributed to both a reduction in their use and a reduction in the incidence of CDI in the study site hospital. Time-series analysis can be utilized as a risk classification tool with utility in antibiotic stewardship design and quality improvement programmes.
Authors: Alla Aroutcheva; Julie Auclair; Martin Frappier; Mathieu Millette; Karen Lolans; Danielle de Montigny; Serge Carrière; Stephen Sokalski; William E Trick; Robert A Weinstein Journal: Probiotics Antimicrob Proteins Date: 2016-03 Impact factor: 4.609
Authors: Awad Al-Omari; Abbas Al Mutair; Saad Alhumaid; Samer Salih; Ahmed Alanazi; Hesham Albarsan; Maha Abourayan; Maha Al Subaie Journal: Antimicrob Resist Infect Control Date: 2020-06-29 Impact factor: 4.887
Authors: Barbara B Lambl; Nathan Kaufman; Janice Kurowski; W O'Neill; Frederick Buckley; Maureen Duram; Barbara Swartz; Duncan Phillips; Mitchell Rein; Marc Rubin Journal: J Am Med Inform Assoc Date: 2017-09-01 Impact factor: 4.497
Authors: Tamar F Barlam; Sara E Cosgrove; Lilian M Abbo; Conan MacDougall; Audrey N Schuetz; Edward J Septimus; Arjun Srinivasan; Timothy H Dellit; Yngve T Falck-Ytter; Neil O Fishman; Cindy W Hamilton; Timothy C Jenkins; Pamela A Lipsett; Preeti N Malani; Larissa S May; Gregory J Moran; Melinda M Neuhauser; Jason G Newland; Christopher A Ohl; Matthew H Samore; Susan K Seo; Kavita K Trivedi Journal: Clin Infect Dis Date: 2016-04-13 Impact factor: 9.079
Authors: K Chrysou; O Zarkotou; S Kalofolia; P Papagiannakopoulou; G Chrysos; K Themeli-Digalaki; A Tsakris; S Pournaras Journal: Eur J Clin Microbiol Infect Dis Date: 2017-11-19 Impact factor: 3.267