Survival rate in patients with ICU-acquired infections and its related factors in Iran’s hospitals

Abstract

Abstract Background Hospital-acquired infections (HAIs) are a well-known cause of morbidity and mortality in hospitalized patients. This study aimed at investigating the survival rate in patients with ICU-acquired infections (ICU-AIs) and its related factors in Iran’s hospitals. Methods Data were obtained from the Iranian Nosocomial Infections Surveillance (INIS), which registers all necessary information on the main types of infection from different units of each included hospital. One thousand one hundred thirty-four duplicate cases were removed from the analysis using the variables of name, father’s name, age, hospital code, infection code, and bedridden date. From 2016 to 2019, 32,998 patients diagnosed with ICU-AI from about 547 hospitals. All patients were followed up to February 29, 2020. Results The median age of patients with ICU-AIs was 61 (IQR = 46) years. 45.5, 20.69, 17.63, 12.08, and 4.09% of infections were observed in general, surgical, internal, neonatal and pediatric ICUs, respectively. Acinetobacter (16.52%), E.coli (12.01%), and Klebsiella (9.93%) were the major types of microorganisms. From total, 40.76% of infected patients (13,449 patients) died. The 1, 3, 6-months and overall survival rate was 70, 25.72, 8.21 1.48% in ICU-AI patients, respectively. The overall survival rate was 5.12, 1.34, 0.0, 51.65, and 31.08% for surgical, general, internal, neonatal and pediatric ICU, respectively. Hazard ratio shows a significant relationship between age, hospitalization-infection length, infection type, and microorganism and risk of death in patients with ICU-AI. Conclusions Based on the results, it seems that the nosocomial infections surveillance system should be more intelligent. This intelligence should act differently based on related factors such as the age of patients, hospitalization-infection length, infection type, microorganism and type of ward. In other words, this system should be able to dynamically provide the necessary and timely warnings based on the factors affecting the survival rate of infection due to the identification, intervention and measures to prevent the spread of HAIs based on a risk severity system.