The Evaluation of Nosocomial Infections Developing in Intensive Care Units of a Tertiary University Hospital

Abstract Introduction and Objective: Nosocomial infections (NIs) in intensive care units (ICUs) are serious problems because of high mortality and morbidity. Here, it was aimed to evaluate diagnoses, distribution and pathogens of NIs in two tertiary general ICUs (GICU) of a hospital and develop new infection control strategies based on the data. Material and Methods: NIs followed in the infectious diseases department and recorded by active prospective surveillance between January 2018-December 2019 in GICUs were retrospectively analyzed. Results: Ninety-five NI episodes were identified in 90 patients during 8468 hospitalization days of 1189 patients in a two-year period. While NI rate in GICUs was 7.98, incidence of NI density was determined as 11.21. Considering the distribution of NIs, lower respiratory tract infections (LRTI) (36.8%) were detected as highest and followed as specific laboratory findings by pneumonia (33.6%), ventilator-associated pneumonia (VAP) (10.5%), central venous catheter-related blood stream infections (CVCR-BSI) (9.4%), laboratory-proven BSI (6.3%) and catheter-associated urinary tract infections (CR-URI) (3.1%). Given the distribution of the factors concerning system infections, agents leading to LRTI other than pneumonia were as follows: Acinetobacter spp. (48.7%), Klebsiella pneumoniae (25.6%), Pseudomonas aeruginosa (12.8%), Serratia marcescens (5.1%), Escherichia coli (2.5%), Enterobacter cloacae (2.5%) ) and Candida albicans (2.5%), and 11.4% were polymicrobial. Conclusion: NIs are inevitable entities in hospitals, especially in ICUs, and one of the vital goals of hospitals is to control and manage such a situation. Timely and appropriate therapeutic interventions should be designed to reduce NI rates. If needed, catheters should be inserted with correct indication, and catheter application should be reduced. It is important that hospitals develop comprehensive antibiotherapy programs based on their own surveillance data.