Noninvasive method for measuring respiratory system compliance during pressure support ventilation.

PURPOSE: To date, few methods have been accepted for assessing the respiratory system compliance (C(rs)) in patients under assisted ventilation at the bedside. The aim of this study was to evaluate our adaptive time slice method (ATSM) to continuously calculate the C(rs). METHODS: One breath is divided into several time periods (slices). For each slice, a compliance value C(i) is calculated. The slice width is adapted according to the confidence interval of C(i). After all C(i) values are obtained and the outliers are eliminated, the C(rs) of this breath is calculated as the mean value of the remainder of C(i)'s. Seven patients with Chronic Obstructive Pulmonary Disease were evaluated during pressure support ventilation. The results are compared with the values calculated with the transdiaphragmatic pressure (P(di)). RESULTS: 95 ± 4% of the recorded data could be analyzed with ATSM. In 6 patients out of 7, the results delivered with ATSM and with P(di) had similar variation (standard deviation) and accuracy (difference<20%). They were strongly correlated (weighted correlation coefficient = 0.86, p<10(-5)) with a mean difference of 3.22 ml/mbar. CONCLUSIONS: The ATSM is a robust method and able to provide accurate C(rs) in spontaneously breathing patients during pressure support ventilation noninvasively without extra instrumentation or complicated maneuvers.

A novel adaptive control system for noisy pressure-controlled ventilation: a numerical simulation and bench test study.

PURPOSE: There is growing interest in the use of both variable and pressure-controlled ventilation (PCV). The combination of these approaches as "noisy PCV" requires adaptation of the mechanical ventilator to the respiratory system mechanics. Thus, we developed and evaluated a new control system based on the least-mean-squares adaptive approach, which automatically and continuously adjusts the driving pressure during PCV to achieve the desired variability pattern of tidal volume (V (T)). METHODS: The controller was tested during numerical simulations and with a physical model reproducing the mechanical properties of the respiratory system. We applied step changes in respiratory system mechanics and mechanical ventilation settings. The time needed to converge to the desired V (T) variability pattern after each change (t (c)) and the difference in minute ventilation between the measured and target pattern of V (T) (DeltaMV) were determined. RESULTS: During numerical simulations, the control system for noisy PCV achieved the desired variable V (T) pattern in less than 30 respiratory cycles, with limited influence of the dynamic elastance (E*) on t (c), except when E* was underestimated by >25%. We also found that, during tests in the physical model, the control system converged in <60 respiratory cycles and was not influenced by airways resistance. In all measurements, the absolute value of DeltaMV was <25%. CONCLUSION: The new control system for noisy PCV can prove useful for controlled mechanical ventilation in the intensive care unit.