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

Abstract

Background: Nosocomial infections result in increased morbidity, length of hospital stays mortality and mortality. We assessed the nosocomial infections rate, infection sites, pathogens and risk factors of healthcare-associated infections in ICU of a tertiary care hospital. Materials and methods: In this retrospective study, all the patients admitted in intensive care unit over a period of 12 months during August 2018 to August 2019 were included in the study. Data collected for various healthcare-associated infections such as catheter-associated urinary tract infections (CAUTI), central-line-associated blood stream infections (CLABSI), bed sore infections and ventilator-associated pneumonias (VAP) was done by the intensive care unit in association with hospital infection control team through an infection surveillance proforma. Patients' records including infection surveillance proforma were analyzed using appropriate statistical tools like percentages and odds ratio. Odd's ratio was calculated to ascertain the strength of association of each risk factor. Results: Incidence rates of healthcare-associated infections were 3.40/1,000 urinary catheter days, 9.50/1,000 central venous pressure line days and 6.50/1,000 ventilator days. Most common organisms isolated from urine were E. coli (27.60%), Klebsiella pneumonia (15.67%) Pseudomonas aeruginosa (11.94%) and Candida species (15.00%). Similarly three topmost organisms isolated from blood were Klebsiella pneumoniae (25.98%), E. coli (21.25%), Staphylococcus (11.02%). Klebsiella pneumoniae (37.10%) Acinetobacter spp. (37.10%), Candida species (10.30%) and were most commonly responsible for tracheal infections. Presence of diabetes and COPD as well as length of ICU stay =7 days was significantly associated with healthcare-associated infections. Conclusion: Age =60 years, diabetes, COPD and ICU stay for =7 days were found to be significantly associated with device-associated infections in our ICU. The results generated can be used to plan and change the potential interventions in managing device-associated infections to improve the quality of critical care services.