Optimizing high-frequency-oscillation ventilation using acoustic parameters of the newborn lung: a feasibility study.
Ventilation using high-frequency oscillation (HFO) has become a standard care for the ventilatory management of critically ill newborns. In recent years, there has been growing recognition that maintenance of an optimal lung volume during high-frequency oscillation plays an important role in minimizing ventilator-induced lung injury. The primary variable affecting lung volume is the mean airway pressure (MAP). To effectively maintain lung recruitment and optimal gas exchange without overstretching (or collapsing) the lung, MAP should be set between two well defined points in the pressure-volume curve of the lung. To determine optimal MAP during high frequency ventilation, an acoustic monitoring system was developed and tested. The system was based on transmission of audible acoustic bursts and reception of echoes from the lungs. The results suggest that these acoustic measurements reflect the mechanical properties of the lungs. The acoustic measurements indicated an increase in lung volume following the administration of exogenous surfactant into the lungs as expected. Hysteresis in the amplitude of acoustic reflection was also measured as expected. Despite the fact that we had no "gold standard" to compare with, our results suggest that acoustic properties of the lung as measured by our system, have the potential to indicate the degree of lung recruitment during HFO and to define the optimal region of MAP.
Luria
Oded
O
Department of Biomedical Engineering, Tel Aviv University, Israel.
Kohelet
David
D
Barnea
Ofer
O
D000465:Algorithms; D001314:Auscultation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D006612:High-Frequency Ventilation / Q000379:methods*; D006801:Humans; D007231:Infant, Newborn; D008168:Lung / Q000503:physiopathology*; D011786:Quality Control; D015203:Reproducibility of Results; D012127:Respiratory Distress Syndrome, Newborn / Q000175:diagnosis* / Q000503:physiopathology*; D012135:Respiratory Sounds; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000379:methods; D013813:Therapy, Computer-Assisted / Q000379:methods*