TY - JOUR
TI - Forecasting models of infections due to carbapenem-resistant Gram-negative bacteria in an intensive care unit in an endemic area
AU - Karampatakis, T.
AU - Tsergouli, K.
AU - Iosifidis, E.
AU - Antachopoulos, C.
AU - Mouloudi, E.
AU - Karyoti, A.
AU - Tsakris, A.
AU - Roilides, E.
JO - Journal of Global Antimicrobial Resistance
PY - 2020
VL - 20
TODO - null
SP - 214-218
PB - Elsevier Ireland Ltd
SN - 2213-7165
TODO - 10.1016/j.jgar.2019.06.019
TODO - Acinetobacter infection;  adolescent;  adult;  aged;  Article;  carbapenem resistant Acinetobacter baumannii;  carbapenem resistant Klebsiella pneumoniae;  carbapenem resistant Pseudomonas aeruginosa;  controlled study;  endemic disease;  female;  forecasting;  human;  incidence;  infection rate;  intensive care unit;  Klebsiella infection;  major clinical study;  male;  observational study;  priority journal;  Pseudomonas infection;  retrospective study;  validation process;  Acinetobacter baumannii;  antibiotic resistance;  Bayes theorem;  classification;  drug effect;  Gram negative infection;  Klebsiella pneumoniae;  length of stay;  microbial sensitivity test;  middle aged;  Pseudomonas aeruginosa;  season;  theoretical model;  very elderly;  young adult, carbapenem derivative, Acinetobacter baumannii;  Adolescent;  Adult;  Aged;  Aged, 80 and over;  Bayes Theorem;  Carbapenems;  Drug Resistance, Bacterial;  Female;  Gram-Negative Bacterial Infections;  Humans;  Intensive Care Units;  Klebsiella pneumoniae;  Length of Stay;  Male;  Microbial Sensitivity Tests;  Middle Aged;  Models, Theoretical;  Pseudomonas aeruginosa;  Retrospective Studies;  Seasons;  Young Adult
TODO - 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. © 2019 International Society for Antimicrobial Chemotherapy
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