Modeling and optimization of nebulizers' performance in non-invasive ventilation using different fill volumes: Comparative study between vibrating mesh and jet nebulizers. (June 2018)

• Record Type: Journal Article
• Authors: Saeed, Haitham; Ali, Ahmed M.A.; Elberry, Ahmed A.; Eldin, Abeer Salah; Rabea, Hoda; Abdelrahim, Mohamed E.A.
• Is Part Of: Pulmonary pharmacology & therapeutics. Volume 50(2018)
• Page Start: 62
• Page End: 71
• Publication Date: 2018-06
• Subjects: Non-invasive ventilation -- Modeling -- Nebulizer -- Neural networks -- Fill volume; Respiratory organs -- Diseases -- Chemotherapy -- Periodicals; 615.7205
• DOI: 10.1016/j.pupt.2018.04.005
• Languages: English
• ISSNs: 1094-5539

Abstract

Abstract: Backgrounds: Substituting nebulisers by another, especially in non-invasive ventilation (NIV), involves many process-variables, e.g. nebulizer-type and fill-volume of respirable-dose, which might affect patient optimum-therapy. The aim of the present work was to use neural-networks and genetic-algorithms to develop performance-models for two different nebulizers. Methods: In-vitro, ex-vivo and in-vivo models were developed using input-variables including nebulizer-type [jet nebulizer (JN) and vibrating mesh nebulizer (VMN)] fill-volumes of respirable dose placed in the nebulization chamber with an output-variable e.g. average amount reaching NIV patient. Produced models were tested and validated to ensure effective predictivity and validity in further optimization of nebulization process. Results: Data-mining produced models showed excellent training, testing and validation correlation-coefficients. VMN showed high nebulization efficacy than JN. JN was affected more by increasing the fill-volume. The optimization process and contour-lines obtained for in-vivo model showed increase in pulmonary-bioavailability and systemic-absorption with VMN and 2 mL fill-volumes. Conclusions: Modeling of aerosol-delivery by JN and VMN using different fill-volumes in NIV circuit was successful in demonstrating the effect of different variable on dose-delivery to NIV patient. Artificial neural networks model showed that VMN increased pulmonary-bioavailability and systemic-absorption compared to JN. VMN was less affected by fill-volume change compared to JN which should be diluted to increase delivery.