Impact of hospital infections in the clinical medicine area of Federico II University Hospital of Naples assessed by means of statistical analysis and logistic regression

Healthcare Associated Infections (HAIs) has significant consequences both on the quality and the economy of the nation's healthcare system. Numerous factors influence the HAIs contraction during hospitalization. Is it possible to identify the principal risk factors leading to HAIs and try to avoid its contraction? In this work we answer this question by correlating patients’ gender, age, McCabe score and the eventual use of urinary catheter, central intravascular catheter and peripheral intravenous catheter with the probability to contract HAIs, by using the machine learning technique. Data of 226 patients hospitalized in 2019 were collected at the University Hospital “Federico II” in Naples in the clinical medicine area. Descriptive statistics was performed and logistic regression was used to test the association between HAIs, and the different risk factors under study. Results show that the variables influencing HAIs contraction were the McCabe score, the clinical use of a central intravascular catheter and the hospitalization at the infectious diseases department.