Abstract

Background: Achieving and maintaining asthma control in children is the primary goal recommended by current guidelines.Aim: To identify risk factors associated with Asthma control and severity, as well as their relative weight.Methods: Within a consecutive series of outpatients visited in a three years period at the IBIM pediatric clinic, we selected 128 persistent asthmatics. A standardized medical interview was carried out to collect information on environmental risk factors, symptoms and comorbidities. Spirometry was performed using Pony FX, Cosmed, Italy; spirometric values were expressed as %pred using GLI-2012equation. Statistical analyses were performed by using R.Results: The identifies a statistical model in which green nodes indicate response variables and light blue nodes indicate covariates. A link between two nodes suggests a strong relation between the corresponding variables whereas a missing link indicates no statistically significant relationship. To test predictive capacities of nodes we use ROC curves. AUC for GINA asthma control, asthma severity, and FEV1 were 0.68, 0.81 and 0.91, respectively. CONCLUSION Through a network analysis we were able to identify risk factors for asthma control, asthma severity, FEF2575 and FEV1. While Gina Severity, FEF2575 and FEV1 can be predicted quite well, Gina control is more difficult to be predicted to and further investigation seems to be necessary.
Lingua originaleEnglish
pagine (da-a)PA4515-
Numero di pagine0
RivistaDefault journal
Volume46
Stato di pubblicazionePublished - 2015

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Asthma
Lung
Spirometry
Statistical Models
ROC Curve
Italy
Area Under Curve
Comorbidity
Outpatients
Guidelines
Interviews
Pediatrics
Weights and Measures

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@article{cf2587f6464e4ef7bfac9baa4b87c024,
title = "Asthma control, severity and lung function impairment through network analysis in children",
abstract = "Background: Achieving and maintaining asthma control in children is the primary goal recommended by current guidelines.Aim: To identify risk factors associated with Asthma control and severity, as well as their relative weight.Methods: Within a consecutive series of outpatients visited in a three years period at the IBIM pediatric clinic, we selected 128 persistent asthmatics. A standardized medical interview was carried out to collect information on environmental risk factors, symptoms and comorbidities. Spirometry was performed using Pony FX, Cosmed, Italy; spirometric values were expressed as {\%}pred using GLI-2012equation. Statistical analyses were performed by using R.Results: The identifies a statistical model in which green nodes indicate response variables and light blue nodes indicate covariates. A link between two nodes suggests a strong relation between the corresponding variables whereas a missing link indicates no statistically significant relationship. To test predictive capacities of nodes we use ROC curves. AUC for GINA asthma control, asthma severity, and FEV1 were 0.68, 0.81 and 0.91, respectively. CONCLUSION Through a network analysis we were able to identify risk factors for asthma control, asthma severity, FEF2575 and FEV1. While Gina Severity, FEF2575 and FEV1 can be predicted quite well, Gina control is more difficult to be predicted to and further investigation seems to be necessary.",
author = "Antonino Abbruzzo and Giovanna Cilluffo and Giuliana Ferrante and Laura Montalbano and {La Grutta}, Stefania and Roberta Antona and Velia Malizia",
year = "2015",
language = "English",
volume = "46",
pages = "PA4515--",
journal = "Default journal",

}

TY - JOUR

T1 - Asthma control, severity and lung function impairment through network analysis in children

AU - Abbruzzo, Antonino

AU - Cilluffo, Giovanna

AU - Ferrante, Giuliana

AU - Montalbano, Laura

AU - La Grutta, Stefania

AU - Antona, Roberta

AU - Malizia, Velia

PY - 2015

Y1 - 2015

N2 - Background: Achieving and maintaining asthma control in children is the primary goal recommended by current guidelines.Aim: To identify risk factors associated with Asthma control and severity, as well as their relative weight.Methods: Within a consecutive series of outpatients visited in a three years period at the IBIM pediatric clinic, we selected 128 persistent asthmatics. A standardized medical interview was carried out to collect information on environmental risk factors, symptoms and comorbidities. Spirometry was performed using Pony FX, Cosmed, Italy; spirometric values were expressed as %pred using GLI-2012equation. Statistical analyses were performed by using R.Results: The identifies a statistical model in which green nodes indicate response variables and light blue nodes indicate covariates. A link between two nodes suggests a strong relation between the corresponding variables whereas a missing link indicates no statistically significant relationship. To test predictive capacities of nodes we use ROC curves. AUC for GINA asthma control, asthma severity, and FEV1 were 0.68, 0.81 and 0.91, respectively. CONCLUSION Through a network analysis we were able to identify risk factors for asthma control, asthma severity, FEF2575 and FEV1. While Gina Severity, FEF2575 and FEV1 can be predicted quite well, Gina control is more difficult to be predicted to and further investigation seems to be necessary.

AB - Background: Achieving and maintaining asthma control in children is the primary goal recommended by current guidelines.Aim: To identify risk factors associated with Asthma control and severity, as well as their relative weight.Methods: Within a consecutive series of outpatients visited in a three years period at the IBIM pediatric clinic, we selected 128 persistent asthmatics. A standardized medical interview was carried out to collect information on environmental risk factors, symptoms and comorbidities. Spirometry was performed using Pony FX, Cosmed, Italy; spirometric values were expressed as %pred using GLI-2012equation. Statistical analyses were performed by using R.Results: The identifies a statistical model in which green nodes indicate response variables and light blue nodes indicate covariates. A link between two nodes suggests a strong relation between the corresponding variables whereas a missing link indicates no statistically significant relationship. To test predictive capacities of nodes we use ROC curves. AUC for GINA asthma control, asthma severity, and FEV1 were 0.68, 0.81 and 0.91, respectively. CONCLUSION Through a network analysis we were able to identify risk factors for asthma control, asthma severity, FEF2575 and FEV1. While Gina Severity, FEF2575 and FEV1 can be predicted quite well, Gina control is more difficult to be predicted to and further investigation seems to be necessary.

UR - http://hdl.handle.net/10447/221783

M3 - Book/Film/Article review

VL - 46

SP - PA4515-

JO - Default journal

JF - Default journal

ER -