Forecasting models of infections due to carbapenem-resistant Gram-negative bacteria in an intensive care unit in an endemic area

Abstract

Objectives: The aim of this study was to forecast the monthly incidence rates of infections [infections/1000 bed-days (IBD)] due to carbapenem-resistant Klebsiella pneumoniae (CRKP), carbapenem-resistant Pseudomonas aeruginosa (CRPA), carbapenem-resistant Acinetobacter baumannii (CRAB) and total carbapenem-resistant Gram-negative bacteria (CRGNB) in an endemic intensive care unit (ICU) during the subsequent year (December 2016–December 2017) following the observational period. Methods: A 52-month observational period (August 2012–November 2016) was used. Two forecasting models, including a simple seasonal model for CRGNB, CRKP and CRPA and Winters’ additive model for CRAB infections, were applied. Results: The models predicted the highest infection rates for CRKP, CRAB and CRGNB in January and September 2017 (23.8/23.4, 24.6/28.5 and 46.8/46.7 IBD, respectively) and for CRPA in February and March 2017 (8.3 and 7.9, respectively). The highest observed rates for CRKP, CRAB and CRGNB were indeed in January and September 2017 (25.6/19.0, 34.2/23.8 and 59.8/42.8 IBD, respectively); and for CRPA in February and March of the same year (15.2 and 12.7, respectively). The increased rates may be associated with personnel’s annual work programme and behavioural factors. Conclusion: Forecasting models in endemic ICUs may assist in implementation strategies for infection control measures.

Keywords

• Forecasting model
• Carbapenem-resistant Gram-negative bacteria
• Klebsiella pneumoniae
• Pseudomonas aeruginosa
• Acinetobacter baumannii
• Intensive care unit