Introduction: The diversity of allergic rhinitis (AR) phenotypes is particularly evident in childhood, suggesting the need to analyze and identify new approaches to capture such clinical heterogeneity. Nasal cytology (NC) is a very useful diagnostic tool for identifying and quantifying nasal inflammation. Data-driven approaches such as latent class analysis (LCA) assign subjects to classes based on their characteristics. We hypothesized that LCA based on NC, including the assessment of neutrophils, eosinophils, and mast cells, may be helpful for identifying AR endotypes in children. Methods: A total of 168 children were enrolled. Sociodemographic characteristics and detailed medical history were obtained from their parents. All children performed NC and skin prick tests. LCA was applied for identifying AR endotypes based on NC, using the R package poLCA. All the statistical analyses were performed using R 4.0.5 software. Statistical significance was set at p ≤.05. Results: LCA identified two classes: Class 1 (n = 126, 75%): higher frequency of children with moderate/large number of neutrophils (31.45%); almost all the children in this class had no mast cells (91.27%) and Class 2 (n = 42, 25%): higher frequency of children with moderate/large number of eosinophils (45.24%) and moderate/large number of mast cells (50%). Conclusions: The present study used a machine learning approach for endotyping childhood AR, which may contribute to improve the diagnostic accuracy and to deliver personalized health care in the context of precision medicine.

Malizia V., Cilluffo G., Fasola S., Ferrante G., Landi M., Montalbano L., et al. (2022). Endotyping allergic rhinitis in children: A machine learning approach. PEDIATRIC ALLERGY AND IMMUNOLOGY, 33(Suppl. 27), 18-21 [10.1111/pai.13620].

Endotyping allergic rhinitis in children: A machine learning approach

Cilluffo G.
Co-primo
Formal Analysis
;
Montalbano L.;
2022-01-01

Abstract

Introduction: The diversity of allergic rhinitis (AR) phenotypes is particularly evident in childhood, suggesting the need to analyze and identify new approaches to capture such clinical heterogeneity. Nasal cytology (NC) is a very useful diagnostic tool for identifying and quantifying nasal inflammation. Data-driven approaches such as latent class analysis (LCA) assign subjects to classes based on their characteristics. We hypothesized that LCA based on NC, including the assessment of neutrophils, eosinophils, and mast cells, may be helpful for identifying AR endotypes in children. Methods: A total of 168 children were enrolled. Sociodemographic characteristics and detailed medical history were obtained from their parents. All children performed NC and skin prick tests. LCA was applied for identifying AR endotypes based on NC, using the R package poLCA. All the statistical analyses were performed using R 4.0.5 software. Statistical significance was set at p ≤.05. Results: LCA identified two classes: Class 1 (n = 126, 75%): higher frequency of children with moderate/large number of neutrophils (31.45%); almost all the children in this class had no mast cells (91.27%) and Class 2 (n = 42, 25%): higher frequency of children with moderate/large number of eosinophils (45.24%) and moderate/large number of mast cells (50%). Conclusions: The present study used a machine learning approach for endotyping childhood AR, which may contribute to improve the diagnostic accuracy and to deliver personalized health care in the context of precision medicine.
2022
Malizia V., Cilluffo G., Fasola S., Ferrante G., Landi M., Montalbano L., et al. (2022). Endotyping allergic rhinitis in children: A machine learning approach. PEDIATRIC ALLERGY AND IMMUNOLOGY, 33(Suppl. 27), 18-21 [10.1111/pai.13620].
File in questo prodotto:
File Dimensione Formato  
Pediatric Allergy Immunology - 2022 - Malizia - Endotyping allergic rhinitis in children A machine learning approach (1).pdf

Solo gestori archvio

Tipologia: Versione Editoriale
Dimensione 395.13 kB
Formato Adobe PDF
395.13 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/533560
Citazioni
  • ???jsp.display-item.citation.pmc??? 3
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 5
social impact