Two new algorithms for tracking arterial parameters in nonstationary noise conditions

On-line monitoring of intrinsic PEEP in ventilator-dependent patients

Measurement of the intrinsic positive end-expiratory pressure (PEEP(i)) is important in planning the management of ventilated patients. Here, a new recursive least squares method for on-line monitoring of PEEP(i) is proposed for mechanically ventilated patients. The procedure is based on the first-order model of respiratory mechanics applied to experimental measurements obtained from eight ventilator-dependent patients ventilated with four different ventilatory modes. The model PEEP(i) (PEEP(i,mod)) was recursively constructed on an inspiration-by-inspiration basis. The results were compared with two well-established techniques to assess PEEP(i): end-expiratory occlusion to measure static PEEP(i) (PEEP(i, st)) and change in airway pressure preceding the onset of inspiratory airflow to measure dynamic PEEP(i) (PEEP(i,dyn)). PEEP(i, mod) was significantly correlated with both PEEP(i,dyn) (r = 0.77) and PEEP(i,st) (r = 0.90). PEEP(i,mod) (5.6 +/- 3.4 cmH(2)O) was systematically >PEEP(i,dyn) and PEEP(i,st) (2.7 +/- 1.9 and 8.1 +/- 5.5 cmH(2)O, respectively), in all the models without external PEEP. Focusing on the five patients with chronic obstructive pulmonary disease, PEEP(i,mod) was significantly correlated with PEEP(i,st) (r = 0.71), whereas PEEP(i,dyn) (r = 0.22) was not. When PEEP was set 5 cmH(2)O above PEEP(i,st), all the methods correctly estimated total PEEP, i.e., 11.8 +/- 5.3, 12.5 +/- 5.0, and 12.0 +/- 4.7 cmH(2)O for PEEP(i,mod), PEEP(i,st), and PEEP(i,dyn), respectively, and were highly correlated (0.97-0.99). We interpreted PEEP(i,mod) as the lower bound of PEEP(i,st) and concluded that our method is suitable for on-line monitoring of PEEP(i) in mechanically ventilated patients.

A new approach for tracking respiratory mechanical parameters in real-time

A new recursive least-squares procedure for on-line tracking of changes in viscoelastic properties of respiratory mechanics is proposed and applied to artificially ventilated patients. Classical least-squares methods based on simple first-order linear models with time-constant parameters generally provide systematic residuals that hardly satisfy standard statistical tests for model validation in terms of residuals. On the other hand, high order and/or nonlinear models introduce parameters whose estimates are of difficult interpretation in a clinical context. The present procedure overcomes these limitations by using the well-known first-order model of respiratory mechanics, wherein variability of resistance and elastance during the breathing cycle is allowed to take into account nonlinear and high-order behavior. Mean and standard deviation of resistance and elastance estimates, relative to a respiratory cycle, are then determined recursively. Feasibility of the method is evaluated by applying it both to experimental and simulated pressure-airflow signals measured in an intensive care unit during mechanical ventilation of patients recovering from heart surgery. Results demonstrate that the proposed procedure provides data description satisfying statistical tests, such as residual whiteness, and reliable estimates of viscoelastic lung parameters even during substantial and fast variations in the respiratory status. In addition, unlike classical methods, the new technique provides the means for on-line evaluation of parameter variability during each respiratory cycle, by the estimate of their standard deviations. This is important in clinical practice, because only the knowledge of reliable parameter values and standard deviations enables significant changes in the respiratory viscoelastic characteristics, and thus in patient status, to be assessed.