Application of mid-frequency ventilation in an animal model of lung injury: A pilot study

BACKGROUND: Mid-frequency ventilation (MFV) is a mode of pressure control ventilation based on an optimal targeting scheme that maximizes alveolar ventilation and minimizes tidal volume (V_T). This study was designed to compare the effects of conventional mechanical ventilation using a lung-protective strategy with MFV in a porcine model of lung injury. Our hypothesis was that MFV can maximize ventilation at higher frequencies without adverse consequences. We compared ventilation and hemodynamic outcomes between conventional ventilation and MFV. METHODS: This was a prospective study of 6 live Yorkshire pigs (10 ± 0.5 kg). The animals were subjected to lung injury induced by saline lavage and injurious conventional mechanical ventilation. Baseline conventional pressure control continuous mandatory ventilation was applied with V_T= 6 mL/kg and PEEP determined using a decremental PEEP trial. A manual decision support algorithm was used to implement MFV using the same conventional ventilator. We measured P_aCO2, P_aO2, end-tidal carbon dioxide, cardiac output, arterial and venous blood oxygen saturation, pulmonary and systemic vascular pressures, and lactic acid. RESULTS: The MFV algorithm produced the same minute ventilation as conventional ventilation but with lower V_T (-1 ± 0.7 mL/kg) and higher frequency (32.1 ± 6.8 vs 55.7 ±15.8 breaths/min, P <.002). There were no differences between conventional ventilation and MFV for mean airway pressures (16.1 ± 1.3 vs 16.4 ± 2 cmH₂O, P =75) even when auto-PEEP was higher (0.6 ± 0.9 vs 2.4 ± 1.1 cm H₂O, P =.02). There were no significant differences in any hemodynamic measurements, although heart rate was higher during MFV. CONCLUSIONS: In this pilot study, we demonstrate that MFV allows the use of higher breathing frequencies and lower V_T than conventional ventilation to maximize alveolar ventilation. We describe the ventilatory or hemodynamic effects of MFV. We also demonstrate that the application of a decision support algorithm to manage MFV is feasible.