AI Article Synopsis
- Invasive pulmonary aspergillosis (IPA) is increasingly being observed in patients without typical immunocompromised conditions, prompting a need for better risk assessment.
- The study aimed to create a predictive model for IPA in influenza patients using machine learning techniques, specifically decision trees.
- Among 77 hospitalized influenza patients, five cases of IPA were identified, indicating that those with lymphocytopenia or who received corticosteroid therapy are notably more at risk.
Article 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7600971 | PMC |
| http://dx.doi.org/10.3390/antibiotics9100644 | DOI Listing |
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