A New Purpose for Proportional Assist Ventilation?*:

Oxygen Therapeutics and Mechanical Ventilation Advances

Advances in intensive care unit (ICU) therapeutics are plentiful and rooted in technological enhancements as well as recognition of patient care priorities. A plethora of new devices and modes are available for use to enhance patient safety and support liberation from mechanical ventilation while preserving oxygenation and carbon dioxide clearance. Increased penetrance of closed loop systems is one means to reduce care variation in appropriate populations. The intelligent design of the ICU space needs to integrate the footprint of that device and the data streaming from it into a coherent whole that supports patient, family, and caregivers.

Limited predictability of maximal muscular pressure using the difference between peak airway pressure and positive end-expiratory pressure during proportional assist ventilation (PAV)

Background

If the proportional assist ventilation (PAV) level is known, muscular effort can be estimated from the difference between peak airway pressure and positive end-expiratory pressure (PEEP) (ΔP) during PAV. We conjectured that deducing muscle pressure from ΔP may be an interesting method to set PAV, and tested this hypothesis using the oesophageal pressure time product calculation.

Methods

Eleven mechanically ventilated patients with oesophageal pressure monitoring under PAV were enrolled. Patients were randomly assigned to seven assist levels (20–80%, PAV20 means 20% PAV gain) for 15 min. Maximal muscular pressure calculated from oesophageal pressure (P_{mus, oes}) and from ΔP (P_{mus, aw}) and inspiratory pressure time product derived from oesophageal pressure (PTP_oes) and from ΔP (PTP_aw) were determined from the last minute of each level. P_{mus, oes} and PTP_oes with consideration of PEEPi were expressed as P_{mus, oes, PEEPi} and PTP_{oes, PEEPi}, respectively. Pressure time product was expressed as per minute (PTP_oes, PTP_{oes, PEEPi}, PTP_aw) and per breath (PTP_{oes, br}, PTP_{oes, PEEPi, br}, PTP_{aw, br}).

Results

PAV significantly reduced the breathing effort of patients with increasing PAV gain (PTP_oes 214.3 ± 80.0 at PAV20 vs. 83.7 ± 49.3 cmH₂O•s/min at PAV80, PTP_{oes, PEEPi} 277.3 ± 96.4 at PAV20 vs. 121.4 ± 71.6 cmH₂O•s/min at PAV80, p < 0.0001). P_{mus, aw} overestimates P_{mus, oes} for low-gain PAV and underestimates P_{mus, oes} for moderate-gain to high-gain PAV. An optimal P_{mus, aw} could be achieved in 91% of cases with PAV60. When the PAV gain was adjusted to P_{mus, aw} of 5–10 cmH₂O, there was a 93% probability of PTP_oes <224 cmH₂O•s/min and 88% probability of PTP_{oes, PEEPi} < 255 cmH₂O•s/min.

Conclusion

Deducing maximal muscular pressure from ΔP during PAV has limited accuracy. The extrapolated pressure time product from ΔP is usually less than the pressure time product calculated from oesophageal pressure tracing. However, when the PAV gain was adjusted to P_{mus, aw} of 5–10 cmH₂O, there was a 90% probability of PTP_oes and PTP_{oes, PEEPi} within acceptable ranges. This information should be considered when applying ΔP to set PAV under various gains.