Preliminary Attempt to Predict Risk of Invasive Pulmonary Aspergillosis in Patients with Influenza: Decision Trees May Help?
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|V|', 'Siccardi|Guido|G|', 'Conte|Livia|L|', 'Celani|Luigi|L|', 'Congeduti|Elena|E|', 'Borrazzo|Cristian|C|0000-0002-2346-898X', 'Santinelli|Letizia|L|0000-0002-5621-058X', 'Innocenti|Giuseppe Pietro|GP|', 'Pinacchio|Claudia|C|', 'Ceccarelli|Giancarlo|G|0000-0001-5921-3180', 'Venditti|Mario|M|', "d'Ettorre|Gabriella|G|0000-0002-3571-5677"]
invasive pulmonary aspergillosis influenza decision trees machine learning EORTC/MSG Italy antifungal drugs risk score