Computer-controlled mechanical simulation of the artificially ventilated human respiratory system.

A mechanical lung simulator can be used to simulate specific lung pathologies, to test lung-function equipment, and in instruction. A new approach to mechanical simulation of lung behavior is introduced that uses a computer-controlled active mechatronic system. The main advantage of this approach is that the static and dynamic properties of the simulator can easily be adjusted via the control software. A nonlinear single-compartment mathematical model of the artificially ventilated respiratory system has been derived and incorporated into the simulator control system. This model can capture both the static and dynamic compliance of the respiratory system as well as nonlinear flow-resistance properties. Parameters in this model can be estimated by using data from artificially ventilated patients. It is shown that the simulation model fits patient data well. This mathematical model of the respiratory system was then matched to a model of the available physical equipment (the simulator, actuators, and the interface electronics) in order to obtain the desired lung behavior. A significant time delay in the piston motion control loop has been identified, which can potentially cause oscillations or even instability for high compliance values. Therefore, a feedback controller based on the Smith-predictor scheme was developed to control the piston motion. The control system, implemented on a personal computer, also includes a user-friendly interface to allow easy parameter setting.

Aerosol therapy and the fighting toddler: is administration during sleep an alternative?

Insufficient cooperation during administration of aerosols by pressurized metered dose inhaler (pMDI)/spacers is a problem in nearly 50% of treated children younger than 2 years. For these children, administration during sleep might be more efficient. However, it is unknown how much aerosol reaches the lungs during sleep. The aim of this study was to determine in vitro the lung dose in young children from a pMDI/spacer during sleep and while being awake. Breathing patterns were recorded by a pneumotachograph in 18 children (age 11 +/- 5.1 months) during sleep and wakefulness. Next, breathing patterns were replayed by a computer-controlled breathing simulator to which an anatomically correct nose-throat model of a 9-month-old child was attached. One puff of budesonide (200 microg) was administered to the model via a metal spacer. Aerosol was trapped in a filter placed between model and breathing simulator. The amount of budesonide on the filter (5 lung dose) was analyzed by HPLC. For each of the 36 breathing patterns, lung dose was measured in triplicate. The sleep breathing patterns had significantly lower respiratory rate and peak inspiratory flows, and smaller variability in respiratory rate, tidal volume, and peak inspiratory flows. Lung dose (mean +/- SD) was 6.5 +/- 3.2 and 11.3 +/- 3.9 microg (p = 0.004) for the wake and sleep breathing pattern, respectively. This infant model-study shows that the lung dose of budesonide by pMDI/spacer is significantly higher during sleep compared to inhalation during wake breathing. Administration of aerosols during sleep might, therefore, be an efficient alternative for uncooperative toddlers.

Estimation of expiratory time constants via fuzzy clustering.

OBJECTIVE: In mechanically ventilated patients the expiratory time constant provides information about respiratory mechanics. In the present study a new method, fuzzy clustering, is proposed to determine expiratory time constants. Fuzzy clustering differs from other methods since it neither interferes with expiration nor presumes any functional relationship between the variables analysed. Furthermore, time constant behaviour during expiration can be assessed, instead of an average time constant. The time constants obtained with fuzzy clustering are compared to time constants conventionally calculated from the same expirations. METHODS: 20 mechanically ventilated patients, including 10 patients with COPD, were studied. The data of flow, volume and pressure were sampled. From these data, four local linear models were detected by fuzzy clustering. The time constants (tau) of the local linear models (clusters) were calculated by a least-squares technique. Time constant behaviour was analysed. Time constants obtained with fuzzy clustering were compared to time constants calculated from flow-volume curves using a conventional method. RESULTS: Fuzzy clustering revealed two patterns of expiratory time constant behaviour. In the patients with COPD an initial low time constant was found (mean tau1: 0.33 s, SD 0.21) followed by higher time constants; mean tau2: 2.00 s (SD 0.91s), mean tau3: 3.45 s (SD 1.44) and mean tau4: 5.47 s (SD 2.93). In the other patients only minor changes in time constants were found; mean tau1: 0.74 s (SD 0.30), mean tau2: 0.90 s (SD 0.23), mean tau3: 1.04 s (SD 0.42) and mean tau4: 1.74 s (SD 0.78). Both the pattern of expiratory time constants, as well as the time constants calculated from the separate clusters, were significantly different between the patients with and without COPD. Time constants obtained with fuzzy clustering for cluster 2, 3 and 4 correlated well with time constants obtained from the flow-volume curves. CONCLUSIONS: In mechanically ventilated patients, expiratory time constant behaviour can be accurately assessed by fuzzy clustering. A good correlation was found between time constants obtained with fuzzy clustering and time constants obtained by conventional analysis. On the basis of the time constants obtained with fuzzy clustering, a clear distinction was made between patients with and without