Emma Montella
ABSTRACT
Surgical infections (SSIs) are among the most common type of healthcare associated infections (HAIs) and a major cause of morbidity among surgical patients, increase of hospitalization days and of healthcare expenditure In this work, we present a logistic regression model to study the impact that different clinical, demographic and organizational factors have on the risk of occurrence of HAIs in a surgery department. The proposed model regression model is based on the Firth's penalized maximum likelihood logistic regression, a well-suited methodology for the analysis of unbalanced datasets, such as those related to events with a low occurrence rate, which is often the case of hospital infections. The model proved to be able to identify the factors most influencing the risk of SSIs and offers a promising tool for the systematic study of SSIs.
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- Investigation of the risk of surgical infections at the “Federico II” University Hospital by regression analysis using the Firth method
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