Automated respiratory cycles selection is highly specific and improves respiratory mechanics analysis

Selected optimal respiratory cycles should allow calculation of respiratory mechanic parameters focusing on patient-ventilator interaction. New computer software automatically selecting optimal breaths and respiratory mechanics derived from those cycles are evaluated.

Retrospective study.

University level III neonatal intensive care unit.

Ten mins synchronized intermittent mandatory ventilation and assist/control ventilation recordings from ten newborns.

The ventilator provided respiratory mechanic data (ventilator respiratory cycles) every 10 secs. Pressure, flow, and volume waves and pressure-volume, pressure-flow, and volume-flow loops were reconstructed from continuous pressure-volume recordings. Visual assessment determined assisted leak-free optimal respiratory cycles (selected respiratory cycles). New software graded the quality of cycles (automated respiratory cycles). Respiratory mechanic values were derived from both sets of optimal cycles. We evaluated quality selection and compared mean values and their variability according to ventilatory mode and respiratory mechanic provenance. To assess discriminating power, all 45 "t" values obtained from interpatient comparisons were compared for each respiratory mechanic parameter.

A total of 11,724 breaths are evaluated. Automated respiratory cycle/selected respiratory cycle selections agreement is high: 88% of maximal κ with linear weighting. Specificity and positive predictive values are 0.98 and 0.96, respectively. Averaged values are similar between automated respiratory cycle and ventilator respiratory cycle. C20/C alone is markedly decreased in automated respiratory cycle (1.27 ± 0.37 vs. 1.81 ± 0.67). Tidal volume apparent similarity disappears in assist/control: automated respiratory cycle tidal volume (4.8 ± 1.0 mL/kg) is significantly lower than for ventilator respiratory cycle (5.6 ± 1.8 mL/kg). Coefficients of variation decrease for all automated respiratory cycle parameters in all infants. "t" values from ventilator respiratory cycle data are two to three times higher than ventilator respiratory cycles.

Automated selection is highly specific. Automated respiratory cycle reflects most the interaction of both ventilator and patient. Improving discriminating power of ventilator monitoring will likely help in assessing disease status and following trends. Averaged parameters derived from automated respiratory cycles are more precise and could be displayed by ventilators to improve real-time fine tuning of ventilator settings.