Preliminary attempt to predict risk of invasive pulmonary aspergillosis in patients with influenza
Decision trees may help?
Valeria Bellelli (Sapienza University of Rome)
Guido Siccardi (Sapienza University of Rome)
Livia Conte (Sapienza University of Rome)
Luigi Celani (Sapienza University of Rome)
Elena Congeduti (TU Delft - Interactive Intelligence)
Cristian Borrazzo (Sapienza University of Rome)
Letizia Santinelli (Sapienza University of Rome)
Giuseppe Pietro Innocenti (Sapienza University of Rome)
Claudia Pinacchio (Sapienza University of Rome)
Giancarlo Ceccarelli (Sapienza University of Rome)
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Abstract
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.
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