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; null More Authors

doi:10.3390/antibiotics9100644

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

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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 language	English
Article number	644
Pages (from-to)	1-8
Number of pages	8
Journal	Antibiotics
Volume	9
Issue number	10
DOIs	https://doi.org/10.3390/antibiotics9100644
Publication status	Published - 2020

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/antibiotics9100644

antibiotics-09-00644Final published version, 359 KBLicence: CC BY

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Bellelli, V., Siccardi , G., Conte, L., Celani, L., Congeduti, E., Borrazzo , C., Santinelli , L., Innocenti , G. P., Pinacchio, C., Ceccarelli , G., & More Authors (2020). Preliminary attempt to predict risk of invasive pulmonary aspergillosis in patients with influenza: Decision trees may help? Antibiotics, 9(10), 1-8. Article 644. https://doi.org/10.3390/antibiotics9100644

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

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.",

keywords = "Antifungal drugs, Decision trees, EORTC/MSG, Influenza, Invasive pulmonary aspergillosis, Italy, Machine learning, Risk score",

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

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Bellelli, V, Siccardi , G, Conte, L, Celani, L, Congeduti, E, Borrazzo , C, Santinelli , L, Innocenti , GP, Pinacchio, C, Ceccarelli , G & More Authors 2020, 'Preliminary attempt to predict risk of invasive pulmonary aspergillosis in patients with influenza: Decision trees may help?', Antibiotics, vol. 9, no. 10, 644, pp. 1-8. https://doi.org/10.3390/antibiotics9100644

TY - JOUR

T1 - Preliminary attempt to predict risk of invasive pulmonary aspergillosis in patients with influenza

T2 - Decision trees may help?

AU - Bellelli, Valeria

AU - Siccardi , Guido

AU - Conte, Livia

AU - Celani, Luigi

AU - Congeduti, Elena

AU - Borrazzo , Cristian

AU - Santinelli , Letizia

AU - Innocenti , Giuseppe Pietro

AU - Pinacchio, Claudia

AU - Ceccarelli , Giancarlo

AU - More Authors, null

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Antifungal drugs

KW - Decision trees

KW - EORTC/MSG

KW - Influenza

KW - Invasive pulmonary aspergillosis

KW - Italy

KW - Machine learning

KW - Risk score

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U2 - 10.3390/antibiotics9100644

DO - 10.3390/antibiotics9100644

M3 - Article

AN - SCOPUS:85091582549

SN - 2079-6382

VL - 9

SP - 1

EP - 8

JO - Antibiotics

JF - Antibiotics

IS - 10

M1 - 644

ER -

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

Abstract

Keywords

UN SDGs

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