Epidemiology and risk factors of healthcare associated infections from intensive care unit of a tertiary care hospital

Background: Nosocomial infections (NIs) result in increased morbidity, mortality and length of hospital stay. The Incidence of NIs, their risk factors and the antibiogram patterns vary across and within countries. We assessed the rates, infection sites, pathogens and risk factors of health-care-associated infections in ICU of a tertiary care hospital. Methods: In this retrospective study, all the patients admitted in Intensive Care Unit over a period of 6 months during August 2015 to January 2016 were included in the current study. Routine surveillance of various health-care-associated infections such as catheter-associated urinary tract infections (CAUTI), central-line-associated blood stream infections (CLABSI), and ventilator-associated pneumonias (VAP) was done by the Department of Microbiology through an Infection Surveillance Proforma. Patients’ records including Infection Surveillance Proforma served as study tools. Odd’s ratio was calculated to ascertain the strength of association of each risk factor. Results: Incidence rates of health-care-associated infections were 9.06/1000 urinary catheter days, 13.35/1000 central venous pressure line days and 5.42/1000 ventilator days. Most common organisms isolated from urine were Pseudomonas aeruginosa (34.48%), Enterococcus species (13.79%), Klebsiella pneumonia (13.79%) and Candida species (13.79%). Similarly three topmost organisms isolated from blood were Klebsiella pneumoniae (32.26%), Acinetobacter species (29.03%) and Pseudomonas aeruginosa (16.13%). Acinetobacter spp. (40.0%), Pseudomonas aeruginosa (33.33%) and Klebsiella pneumonia (13.33%) were most commonly responsible for tracheal infections. Presence of diabetes and COPD as well as length of ICU stay ≥8 days was significantly associated with health-care-associated infections. Conclusions: Diabetes, COPD and ICU stay for ≥8 days were found to be significantly associated with device-associated infections in our ICU. Data thus generated can be used to plan and modulate the potential intervention measures while managing device-associated infections.