Detecting lung overdistention in newborns treated with high-frequency oscillatory ventilation
- 1 July 2000
- journal article
- research article
- Published by American Physiological Society in Journal of Applied Physiology
- Vol. 89 (1) , 364-372
- https://doi.org/10.1152/jappl.2000.89.1.364
Abstract
Positive airway pressure (Paw) during high-frequency oscillatory ventilation (HFOV) increases lung volume and can lead to lung overdistention with potentially serious adverse effects. To date, no method is available to monitor changes in lung volume (ΔVl) in HFOV-treated infants to avoid overdistention. In five newborn piglets (6–15 days old, 2.2–4.2 kg), we investigated the use of direct current-coupled respiratory inductive plethysmography (RIP) for this purpose by evaluating it against whole body plethysmography. Animals were instrumented, fitted with RIP bands, paralyzed, sedated, and placed in the plethysmograph. RIP and plethysmography were simultaneously calibrated, and HFOV was instituted at varying Paw settings before (6–14 cmH2O) and after (10–24 cmH2O) repeated warm saline lung lavage to induce experimental surfactant deficiency. Estimates of ΔVl from both methods were in good agreement, both transiently and in the steady state. Maximal changes in lung volume (ΔVlmax) from all piglets were highly correlated with ΔVl measured by RIP (in ml) = 1.01 × changes measured by whole body plethysmography − 0.35; r2= 0.95. Accuracy of RIP was unchanged after lavage. Effective respiratory system compliance (Ceff) decreased after lavage, yet it exhibited similar sigmoidal dependence on ΔVlmaxpre- and postlavage. A decrease in Ceff (relative to the previous Paw setting) as ΔVlmaxwas methodically increased from low to high Paw provided a quantitative method for detecting lung overdistention. We conclude that RIP offers a noninvasive and clinically applicable method for accurately estimating lung recruitment during HFOV. Consequently, RIP allows the detection of lung overdistention and selection of optimal HFOV from derived Ceff data.Keywords
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