Preliminary attempt to predict risk of invasive pulmonary aspergillosis in patients with influenza: Decision trees may help?

Valeria Bellelli, Guido Siccardi , Livia Conte, Luigi Celani, Elena Congeduti, Cristian Borrazzo , Letizia Santinelli , Giuseppe Pietro Innocenti , Claudia Pinacchio, Giancarlo Ceccarelli , More Authors

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
Article number644
Pages (from-to)1-8
Number of pages8
JournalAntibiotics
Volume9
Issue number10
DOIs
Publication statusPublished - 2020

Keywords

  • Antifungal drugs
  • Decision trees
  • EORTC/MSG
  • Influenza
  • Invasive pulmonary aspergillosis
  • Italy
  • Machine learning
  • Risk score

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