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In Antibiotics (Basel, Switzerland)

Invasive pulmonary aspergillosis (IPA) is typically considered a disease of immunocompromised patients, but, recently, many cases have been reported in patients without typical risk factors. The aim of our study is to develop a risk predictive model for IPA through machine learning techniques (decision trees) in patients with influenza. We conducted a retrospective observational study analyzing data regarding patients diagnosed with influenza hospitalized at the University Hospital "Umberto I" of Rome during the 2018-2019 season. We collected five IPA cases out of 77 influenza patients. Although the small sample size is a limit, the most vulnerable patients among the influenza-infected population seem to be those with evidence of lymphocytopenia and those that received corticosteroid therapy.

Bellelli Valeria, Siccardi Guido, Conte Livia, Celani Luigi, Congeduti Elena, Borrazzo Cristian, Santinelli Letizia, Innocenti Giuseppe Pietro, Pinacchio Claudia, Ceccarelli Giancarlo, Venditti Mario, d’Ettorre Gabriella


EORTC/MSG, Italy, antifungal drugs, decision trees, influenza, invasive pulmonary aspergillosis, machine learning, risk score