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The contribution of patients’ lung function to the inspiratory airflow rate achievable through a DPIs’ simulator reproducing different intrinsic resistance rates

BACKGROUND: The performance of DPIs depends on several physiological (patient-dependent) and technological (device-dependent) factors. The inspiratory airflow rate is the only active force generated and operating in the system for inducing the required pressure drop and eliciting the resistance-indu...

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Autores principales: Dal Negro, Roberto W., Turco, Paola, Povero, Massimiliano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PAGEPress Publications, Pavia, Italy 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8077610/
https://www.ncbi.nlm.nih.gov/pubmed/33953914
http://dx.doi.org/10.4081/mrm.2021.752
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author Dal Negro, Roberto W.
Turco, Paola
Povero, Massimiliano
author_facet Dal Negro, Roberto W.
Turco, Paola
Povero, Massimiliano
author_sort Dal Negro, Roberto W.
collection PubMed
description BACKGROUND: The performance of DPIs depends on several physiological (patient-dependent) and technological (device-dependent) factors. The inspiratory airflow rate is the only active force generated and operating in the system for inducing the required pressure drop and eliciting the resistance-induced turbulence needed to disaggregate the powder through the device. The present study aimed to investigate in the most prevalent respiratory disorders whether and at what extent the inspiratory airflow rate achievable when inhaling through a DPIs’ simulator reproducing different intrinsic resistance regimens (low, mid, and high resistance) is affected by peculiar changes in lung function and/or can be predicted by any specific lung function parameter. METHODS: The inspiratory airflow rate was assessed in randomized order by the In-Check DIAL G16 at low, mid, and high resistance regimens in a sample of consecutive subjects at recruitment. Independent predictors of the probability to achieve the expected inhalation airflow rate were investigated by means of a multivariate logistic regression model, specific to the disease. RESULTS: A total of 114 subjects were recruited (asthmatics n=30; COPD n=50, restrictive patients n=16, and normal subjects n=18). The mean values of the expected inspiratory airflow rate achieved proved significantly different within the groups (p<0.0001), independently of sex and age. In asthmatics and in COPD patients, the mid-resistance regimen proved highly associated with the highest mean values of airflow rates obtained. Low- and high-resistance regimens were significantly less likely to consent to achieve the expected level of inspiratory airflow rate (OR<1 in all comparisons). Restrictive patients performed the lowest airflow rates at the low-resistance regimen (p<0.01). Unlike FEV(1), RV in asthmatics (OR=1.008); RV and I(Raw) in COPD (OR=0.587 and OR=0.901, respectively), and FIF and TLC in restrictive patients (OR=1.041, and OR=0.962, respectively) proved the only sensitive predictors of the inspiratory airflow rate achievable at the different resistive regimens. CONCLUSIONS: The intrinsic resistive regimen of DPIs can play a critical role. The patients’ lung function profile also affects the extent of their inhalation airflow rate. Some specific lung function parameters (such as: FIF; RV; I(Raw); TLC, but not FEV(1)) may be regarded as specific predictors in real-life. In order to optimize the DPI choice, further to the device’s technology, also the current patients’ lung function should be properly investigated and carefully assessed.
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spelling pubmed-80776102021-05-04 The contribution of patients’ lung function to the inspiratory airflow rate achievable through a DPIs’ simulator reproducing different intrinsic resistance rates Dal Negro, Roberto W. Turco, Paola Povero, Massimiliano Multidiscip Respir Med Original Research Article BACKGROUND: The performance of DPIs depends on several physiological (patient-dependent) and technological (device-dependent) factors. The inspiratory airflow rate is the only active force generated and operating in the system for inducing the required pressure drop and eliciting the resistance-induced turbulence needed to disaggregate the powder through the device. The present study aimed to investigate in the most prevalent respiratory disorders whether and at what extent the inspiratory airflow rate achievable when inhaling through a DPIs’ simulator reproducing different intrinsic resistance regimens (low, mid, and high resistance) is affected by peculiar changes in lung function and/or can be predicted by any specific lung function parameter. METHODS: The inspiratory airflow rate was assessed in randomized order by the In-Check DIAL G16 at low, mid, and high resistance regimens in a sample of consecutive subjects at recruitment. Independent predictors of the probability to achieve the expected inhalation airflow rate were investigated by means of a multivariate logistic regression model, specific to the disease. RESULTS: A total of 114 subjects were recruited (asthmatics n=30; COPD n=50, restrictive patients n=16, and normal subjects n=18). The mean values of the expected inspiratory airflow rate achieved proved significantly different within the groups (p<0.0001), independently of sex and age. In asthmatics and in COPD patients, the mid-resistance regimen proved highly associated with the highest mean values of airflow rates obtained. Low- and high-resistance regimens were significantly less likely to consent to achieve the expected level of inspiratory airflow rate (OR<1 in all comparisons). Restrictive patients performed the lowest airflow rates at the low-resistance regimen (p<0.01). Unlike FEV(1), RV in asthmatics (OR=1.008); RV and I(Raw) in COPD (OR=0.587 and OR=0.901, respectively), and FIF and TLC in restrictive patients (OR=1.041, and OR=0.962, respectively) proved the only sensitive predictors of the inspiratory airflow rate achievable at the different resistive regimens. CONCLUSIONS: The intrinsic resistive regimen of DPIs can play a critical role. The patients’ lung function profile also affects the extent of their inhalation airflow rate. Some specific lung function parameters (such as: FIF; RV; I(Raw); TLC, but not FEV(1)) may be regarded as specific predictors in real-life. In order to optimize the DPI choice, further to the device’s technology, also the current patients’ lung function should be properly investigated and carefully assessed. PAGEPress Publications, Pavia, Italy 2021-04-15 /pmc/articles/PMC8077610/ /pubmed/33953914 http://dx.doi.org/10.4081/mrm.2021.752 Text en ©Copyright: the Author(s) https://creativecommons.org/licenses/by-nc/4.0/This work is licensed under a Creative Commons Attribution NonCommercial 4.0 License (CC BY-NC 4.0).
spellingShingle Original Research Article
Dal Negro, Roberto W.
Turco, Paola
Povero, Massimiliano
The contribution of patients’ lung function to the inspiratory airflow rate achievable through a DPIs’ simulator reproducing different intrinsic resistance rates
title The contribution of patients’ lung function to the inspiratory airflow rate achievable through a DPIs’ simulator reproducing different intrinsic resistance rates
title_full The contribution of patients’ lung function to the inspiratory airflow rate achievable through a DPIs’ simulator reproducing different intrinsic resistance rates
title_fullStr The contribution of patients’ lung function to the inspiratory airflow rate achievable through a DPIs’ simulator reproducing different intrinsic resistance rates
title_full_unstemmed The contribution of patients’ lung function to the inspiratory airflow rate achievable through a DPIs’ simulator reproducing different intrinsic resistance rates
title_short The contribution of patients’ lung function to the inspiratory airflow rate achievable through a DPIs’ simulator reproducing different intrinsic resistance rates
title_sort contribution of patients’ lung function to the inspiratory airflow rate achievable through a dpis’ simulator reproducing different intrinsic resistance rates
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8077610/
https://www.ncbi.nlm.nih.gov/pubmed/33953914
http://dx.doi.org/10.4081/mrm.2021.752
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