Estimation Of Respiratory Mechanics Research Articles

Estimating the incidence of spontaneous breathing effort of mechanically ventilated patients using a non-linear auto regressive (NARX) model

Background and objectiveMechanical ventilation (MV) provides breathing support for acute respiratory distress syndrome (ARDS) patients in the intensive care unit, but is difficult to optimize. Too much, or too little of pressure or volume support can cause further ventilator-induced lung injury, increasing length of MV, cost and mortality. Patient-specific respiratory mechanics can help optimize MV settings. However, model-based estimation of respiratory mechanics is less accurate when patient exhibit un-modeled spontaneous breathing (SB) efforts on top of ventilator support. This study aims to estimate and quantify SB efforts by reconstructing the unaltered passive mechanics airway pressure using NARX model. MethodsNon-linear autoregressive (NARX) model is used to reconstruct missing airway pressure due to the presence of spontaneous breathing effort in mv patients. Then, the incidence of SB patients is estimated. The study uses a total of 10,000 breathing cycles collected from 10 ARDS patients from IIUM Hospital in Kuantan, Malaysia. In this study, there are 2 different ratios of training and validating methods. Firstly, the initial ratio used is 60:40 which indicates 600 breath cycles for training and remaining 400 breath cycles used for testing. Then, the ratio is varied using 70:30 ratio for training and testing data. Results and discussionThe mean residual error between original airway pressure and reconstructed airway pressure is denoted as the magnitude of effort. The median and interquartile range of mean residual error for both ratio are 0.0557 [0.0230 - 0.0874] and 0.0534 [0.0219 - 0.0870] respectively for all patients. The results also show that Patient 2 has the highest percentage of SB incidence and Patient 10 with the lowest percentage of SB incidence which proved that NARX model is able to perform for both higher incidence of SB effort or when there is a lack of SB effort. ConclusionThis model is able to produce the SB incidence rate based on 10% threshold. Hence, the proposed NARX model is potentially useful to estimate and identify patient-specific SB effort, which has the potential to further assist clinical decisions and optimize MV settings.

Convolutional Neural Network for Respiratory Mechanics Estimation during Pressure Support Ventilation

Abstract In mechanically ventilated patients, some lung injuries can be reduced or avoided with therapy individualization, while the lung function is evaluated continuously, breath by breath. However, obtaining information on respiratory mechanics (respiratory system resistance and compliance) in the presence of respiratory effort is challenging, even if using invasive and complex procedures. The contribution of this work is to predict both respiratory system resistance and compliance over time using a convolutional neural network (CNN) and estimate the respiratory effort profile using the respiratory dynamics. Therefore, the approach used in this work was to generate a large amount of simulated data to feed a CNN so it could learn how to predict the correct values of the respiratory system resistance and compliance. Then, the respiratory effort was estimated by solving a first-order linear model. The main results showed a normalized mean squared error of 5.7% for the respiratory system resistance and 11.56% for compliance from Bland-Altman plots derived from the computational simulator. Finally, the method was validated using real data from an active lung simulator within which respiratory mechanics varied, and some ventilator settings were adjusted to mimic actual patient situations. The active lung simulator effort profile was obtained with a normalized mean squared error of 8.31% considering the use of an active lung simulator. The results have shown that the simulated data were valuable for the CNN training, while the performance over the real data suggested that the network was generalized accordingly for estimating respiratory parameters and effort profile.

Advanced mechanical ventilation modes: design and computer simulations

In this paper, three different advanced mechanical ventilation modes, pressure regulated volume control ventilation (PRVC), proportional assist ventilation (PAV), and adaptive support ventilation (ASV) are designed and simulated on the computer via MATLAB/Simulink. In the algorithms of advanced ventilation modes, a closed-loop control structure is used and recursive least squares method is considered for the estimation of respiratory mechanics. The designed algorithms are then applied to the human respiratory system model for the active and/or passive patient cases. Simulation results show that such algorithms can be designed and simulated on the computer successfully. In addition, the simulation environment helps us to understand the working principles of the advanced modes and to see the results such as ventilator waveforms, the effect of the parameter changes. Moreover, it also allows us to improve and test the algorithms and strategies quickly and efficiently.

Inspiratory respiratory mechanics estimation by using expiratory data for reverse-triggered breathing cycles

Background and objectiveModel-based lung mechanics monitoring can provide clinically useful information for guiding mechanical ventilator treatment in intensive care. However, many methods of measuring lung mechanics are not appropriate for both fully and partially sedated patients, and are unable provide lung mechanics metrics in real-time. This study proposes a novel method of using lung mechanics identified during passive expiration to estimate inspiratory lung mechanics for spontaneously breathing patients. MethodsRelationships between inspiratory and expiratory modeled lung mechanics were identified from clinical data from 4 fully sedated patients. The validity of these relationships were assessed using data from a further 4 spontaneously breathing patients. ResultsFor the fully sedated patients, a linear relationship was identified between inspiratory and expiratory elastance, with slope 1.04 and intercept 1.66. The r value of this correlation was 0.94. No cohort-wide relationship was determined for airway resistance. Expiratory elastance measurements in spontaneously breathing patients were able to produce reasonable estimates of inspiratory elastance after adjusting for the identified difference between them. ConclusionsThis study shows that when conventional methods fail, typically ignored expiratory data may be able to provide clinicians with the information needed about patient condition to guide MV therapy.

Device for simulation of restrictive pathologies in healthy subjects with non-invasive mechanical ventilation

The respiratory mechanics assessment in patients with mechanical ventilation allows to adjust the treatment in intensive care units related to the ventilatory mode and parameters of mechanical ventilator settings. However, to estimate the compliance and respiratory resistance in spontaneous ventilation is only possible with obstructive maneuvers or invasive techniques. One of the most important limitations to develop new techniques for respiratory mechanics estimation is the non-stationary characteristic of the system and the variability of parameters according to the variability of the breathing pattern. The aim of this article is to present and evaluate a device that allows artificially modify the thoracic compliance of a healthy subject, which will make possible to register in the future a useful database for the development of techniques for estimating ventilatory mechanics. The device was formed by a cuirass, a pump and a controller that allows to vary the pressure inside the cuirass, which was placed in the chest and abdomen of the volunteers to change compliance in a controlled manner. 5 volunteers participated in the performance test of the device, achieving percentage changes of 34.5 ± 9.4% respecting their resting value for a pressure of 10 cmH2O and changes of 46.8 ± 5.7% for the maximum pressure of 20 cmH2O. It was possible to design a device that allowed to artificially modify thoracic compliance in a comparable way for any healthy subject.

Assessing mechanical ventilation asynchrony through iterative airway pressure reconstruction

Background and objectiveRespiratory mechanics estimation can be used to guide mechanical ventilation (MV) but is severely compromised when asynchronous breathing occurs. In addition, asynchrony during MV is often not monitored and little is known about the impact or magnitude of asynchronous breathing towards recovery. Thus, it is important to monitor and quantify asynchronous breathing over every breath in an automated fashion, enabling the ability to overcome the limitations of model-based respiratory mechanics estimation during asynchronous breathing ventilation. MethodsAn iterative airway pressure reconstruction (IPR) method is used to reconstruct asynchronous airway pressure waveforms to better match passive breathing airway waveforms using a single compartment model. The reconstructed pressure enables estimation of respiratory mechanics of airway pressure waveform essentially free from asynchrony. Reconstruction enables real-time breath-to-breath monitoring and quantification of the magnitude of the asynchrony (MAsyn). Results and discussionOver 100,000 breathing cycles from MV patients with known asynchronous breathing were analyzed. The IPR was able to reconstruct different types of asynchronous breathing. The resulting respiratory mechanics estimated using pressure reconstruction were more consistent with smaller interquartile range (IQR) compared to respiratory mechanics estimated using asynchronous pressure. Comparing reconstructed pressure with asynchronous pressure waveforms quantifies the magnitude of asynchronous breathing, which has a median value MAsyn for the entire dataset of 3.8%. ConclusionThe iterative pressure reconstruction method is capable of identifying asynchronous breaths and improving respiratory mechanics estimation consistency compared to conventional model-based methods. It provides an opportunity to automate real-time quantification of asynchronous breathing frequency and magnitude that was previously limited to invasively method only.

Quantifying patient effort in spontaneously breathing patient using negative component of dynamic Elastance

Abstract Respiratory mechanics of fully sedated patients can be easily estimated as the ventilator has full control of patient’s work of breathing. However, in spontaneously breathing patients or patients whose work of breathing is only partially assisted, respiratory mechanics estimation is much more difficult. This difficulty is caused by un-modelled and variable patient effort introduced into the system. The Time-varying elastance model is a model that can estimate respiratory mechanics of spontaneously breathing patients with use of dynamic elastance. The model has a negative elastance component where measured airway pressure is decreasing, yet the air is flowing into the lungs due to patient effort. In this study, quantification of the negative elastance component is studied. Airway flow, pressure and Electrical Activity of Diaphragm signals from 22 invasively ventilated patients using Pressure Support mode are used for this analysis. Two methods have been used to quantify negative elastance, the zero-crossing method and trapezoidal method. The estimated median values of negative elastance using the zero-crossing method is: -3.289 [Interquartile range (IQR: -4.803~-2.504] cmH2O/L and for trapezoidal method is: -1.899 [IQR: -2.362-1.664] cmH2O/L. The correlation between electrical activity of the diaphragm and negative elastance is very weak as the R values across patients are: -0.0697 [IQR: -0.4972~ -0.0255] for zero-crossing method and 0.0939 [IQR: -0.0293-0.3003] for trapezoidal method. Negative elastance is a conceptual component of the model and can be used to quantify patient demand. However, in this study, quantifying patient effort using negative elastance has little similarity to electrical activity of the diaphragm, as negative elastance appears to capture more than only patient effort. Further research is needed to use this metric to observe patient effort.

Differences in respiratory mechanics estimation with respect to manoeuvres and mathematical models

The aim of mechanical ventilation (MV) is to provide sufficient breathing support for critically ill patients with respiratory failure in the intensive care unit (ICU). However, the application of inappropriate MV settings can result in ventilator induced lung injury (VILI) and exacerbate respiratory dysfunction. To prevent VILI, respiratory mechanics properties such as elastance and resistance can be estimated at the bedside to guide MV settings. Different models or methods provide different information and have unique advantages and disadvantages. In this study, the respiratory mechanics of 25 respiratory failure patients were determined using the first order model (FOM) and a viscoelastic model (VEM). The patients underwent different respiratory manoeuvres and their identified respiratory mechanics using these models are studied and compared with a standard clinical method in estimating respiratory mechanics. The results show that both models were able to capture patient-specific mechanics and responses. The FOM was able to provide higher correlation to the standard clinical method while the VEM provides a physiologically more plausible representation.

Estimating the true respiratory mechanics during asynchronous pressure controlled ventilation

Abstract Mechanical ventilation (MV) therapy partially or fully replaces the work of breathing in patients with respiratory failure. Respiratory mechanics during pressure controlled (PC) or pressure support (PS) are often not estimated due to variability induced by patient’s spontaneous breathing effort (SB) or asynchronous events (AEs). Thus for non-invasive model-based MV with PC/PS, there is a need for improved estimation of respiratory mechanics. An algorithm is proposed that allows for the improvement of respiratory system mechanics estimation during pressure controlled ventilation, while providing a means of quantifying AE magnitude as one indicator of patient-ventilator interaction, which may be valuable to clinicians to monitor patient response to care. For testing, 10 retrospective airway pressure and flow data samples were obtained from 6 MV patients, with each data sample containing 450–500 breaths. All data samples with AE present experienced a decrease in 5th to 95th range (Range90) and mean absolute deviation (MAD) for the estimated respiratory system elastance after reconstruction. These results suggested improved in respiratory mechanics estimation during pressure controlled ventilation. The median [maximum (max), minimum (min)] decrease in MAD was 29.4% (51%, 18.6%), and the median (max, min) decrease in Range90 divided by median respiratory system elastance was 30.7% (48.8%, 6.4%). The algorithm is robust to many different spontaneous breathing efforts, asynchrony shapes and types. The proposed algorithm demonstrates the potential to effectively improve respiratory mechanics and quantify the magnitude of AEs.

The computational model will see you now

Highly aggregate mathematical models can be used to estimate physiological parameters at the patient’s bedside.

A polynomial model of patient-specific breathing effort during controlled mechanical ventilation.

Patient breathing efforts occurring during controlled ventilation causes perturbations in pressure data, which cause erroneous parameter estimation in conventional models of respiratory mechanics. A polynomial model of patient effort can be used to capture breath-specific effort and underlying lung condition. An iterative multiple linear regression is used to identify the model in clinical volume controlled data. The polynomial model has lower fitting error and more stable estimates of respiratory elastance and resistance in the presence of patient effort than the conventional single compartment model. However, the polynomial model can converge to poor parameter estimation when patient efforts occur very early in the breath, or for long duration. The model of patient effort can provide clinical benefits by providing accurate respiratory mechanics estimation and monitoring of breath-to-breath patient effort, which can be used by clinicians to guide treatment.

Noninvasive Estimation of Respiratory Mechanics in Spontaneously Breathing Ventilated Patients: A Constrained Optimization Approach.

This paper presents a method for breath-by-breath noninvasive estimation of respiratory resistance and elastance in mechanically ventilated patients. For passive patients, well-established approaches exist. However, when patients are breathing spontaneously, taking into account the diaphragmatic effort in the estimation process is still an open challenge. Mechanical ventilators require maneuvers to obtain reliable estimates for respiratory mechanics parameters. Such maneuvers interfere with the desired ventilation pattern to be delivered to the patient. Alternatively, invasive procedures are needed. The method presented in this paper is a noninvasive way requiring only measurements of airway pressure and flow that are routinely available for ventilated patients. It is based on a first-order single-compartment model of the respiratory system, from which a cost function is constructed as the sum of squared errors between model-based airway pressure predictions and actual measurements. Physiological considerations are translated into mathematical constraints that restrict the space of feasible solutions and make the resulting optimization problem strictly convex. Existing quadratic programming techniques are used to efficiently find the minimizing solution, which yields an estimate of the respiratory system resistance and elastance. The method is illustrated via numerical examples and experimental data from animal tests. Results show that taking into account the patient effort consistently improves the estimation of respiratory mechanics. The method is suitable for real-time patient monitoring, providing clinicians with noninvasive measurements that could be used for diagnosis and therapy optimization.

Visualisation of time-varying respiratory system elastance in experimental ARDS animal models.

BackgroundPatients with acute respiratory distress syndrome (ARDS) risk lung collapse, severely altering the breath-to-breath respiratory mechanics. Model-based estimation of respiratory mechanics characterising patient-specific condition and response to treatment may be used to guide mechanical ventilation (MV). This study presents a model-based approach to monitor time-varying patient-ventilator interaction to guide positive end expiratory pressure (PEEP) selection.MethodsThe single compartment lung model was extended to monitor dynamic time-varying respiratory system elastance, Edrs, within each breathing cycle. Two separate animal models were considered, each consisting of three fully sedated pure pietrain piglets (oleic acid ARDS and lavage ARDS). A staircase recruitment manoeuvre was performed on all six subjects after ARDS was induced. The Edrs was mapped across each breathing cycle for each subject.ResultsSix time-varying, breath-specific Edrs maps were generated, one for each subject. Each Edrs map shows the subject-specific response to mechanical ventilation (MV), indicating the need for a model-based approach to guide MV. This method of visualisation provides high resolution insight into the time-varying respiratory mechanics to aid clinical decision making. Using the Edrs maps, minimal time-varying elastance was identified, which can be used to select optimal PEEP.ConclusionsReal-time continuous monitoring of in-breath mechanics provides further insight into lung physiology. Therefore, there is potential for this new monitoring method to aid clinicians in guiding MV treatment. These are the first such maps generated and they thus show unique results in high resolution. The model is limited to a constant respiratory resistance throughout inspiration which may not be valid in some cases. However, trends match clinical expectation and the results highlight both the subject-specificity of the model, as well as significant inter-subject variability.

The Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management

BackgroundReal-time patient respiratory mechanics estimation can be used to guide mechanical ventilation settings, particularly, positive end-expiratory pressure (PEEP). This work presents a software, Clinical Utilisation of Respiratory Elastance (CURE Soft), using a time-varying respiratory elastance model to offer this ability to aid in mechanical ventilation treatment.ImplementationCURE Soft is a desktop application developed in JAVA. It has two modes of operation, 1) Online real-time monitoring decision support and, 2) Offline for user education purposes, auditing, or reviewing patient care. The CURE Soft has been tested in mechanically ventilated patients with respiratory failure. The clinical protocol, software testing and use of the data were approved by the New Zealand Southern Regional Ethics Committee.Results and discussionUsing CURE Soft, patient’s respiratory mechanics response to treatment and clinical protocol were monitored. Results showed that the patient’s respiratory elastance (Stiffness) changed with the use of muscle relaxants, and responded differently to ventilator settings. This information can be used to guide mechanical ventilation therapy and titrate optimal ventilator PEEP.ConclusionCURE Soft enables real-time calculation of model-based respiratory mechanics for mechanically ventilated patients. Results showed that the system is able to provide detailed, previously unavailable information on patient-specific respiratory mechanics and response to therapy in real-time. The additional insight available to clinicians provides the potential for improved decision-making, and thus improved patient care and outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/1475-925X-13-140) contains supplementary material, which is available to authorized users.

Methacholine responsiveness in mice from 2 to 8 wk of age

Many chronic human lung diseases have their origin in early childhood, yet most murine models used to study them utilize adult mice. An important component of the asthma phenotype is exaggerated airway responses, frequently modelled by methacholine (MCh) challenge. The present study was undertaken to characterize MCh responses in mice from 2 to 8 wk of age measuring absolute lung volume and volume-corrected respiratory mechanics as outcome variables. Female BALB/c mice aged 2, 3, 4, 6, and 8 wk were studied during cumulative intravenous MCh challenge. Following each MCh dose, absolute lung volume was measured plethysmographically at functional residual volume and during a slow inflation to 20-hPa transrespiratory pressure. Respiratory system impedance was measured continuously during the inflation maneuver and partitioned into airway and constant-phase parenchymal components by model fitting. Volume-corrected (specific) estimates of respiratory mechanics were calculated. Intravenous MCh challenge induced a predominantly airway response with no evidence of airway closure in any age group. No changes in functional residual volume were seen in mice of any age during the MCh challenge. The specific airway resistance MCh dose response curves did not show significant differences between the age groups. The results from the present study do not show systematic differences in MCh responsiveness in mice from 2 to 8 wk of age.

Ventilator assessment of respiratory mechanics in paediatric intensive care

Many modern "paediatric" mechanical ventilators have in-built features for estimation of respiratory mechanics which could be useful in the management of ventilated infants and children. The aim of this study was to determine if such measurements were reproducible and accurate. Ventilator (Draeger Evita 4) displayed compliance (Cvent) and resistance (Rvent) values were assessed and compared to the results of respiratory system mechanics (respiratory system compliance (Crs) and resistance (Rrs)) measurements obtained using a single breath occlusion technique. Seventeen children (median age 5.1; range 0.3 to 16 yrs) were studied on 24 occasions. The mean coefficients of variations for the techniques were similar (Cvent 13%; Crs 11%; Rvent 16%; Rrs 14%). The mean (SD) Crs (22.8 (12.3) ml/cmH2O) did not differ significantly from Cvent (22.1 (12.7) ml/cm H2O) but the mean Rrs 21.0 (12.7) cmH2O/l/s was significantly higher than the mean Rvent 32.0 (32.0) cmH2O/l/s (p = 0.03). Bland and Altman analysis demonstrated a mean difference of -10.94 cmH2O/l/s (SD 24.1) between Rrs and Rvent; the agreement between Rrs and Rvent decreased as Rrs increased (p = 0.008). Ventilator assessment of compliance, but not resistance, using the Evita 4 is reproducible and reliable.

Expiratory time constants in mechanically ventilated patients with and without COPD.

In mechanically ventilated patients, the expiratory time constant provides information about the respiratory mechanics and the actual time needed for complete expiration. As an easy method to determine the time constant, the ratio of exhaled tidal volume to peak expiratory flow has been proposed. This assumes a single compartment model for the whole expiration. Since the latter has to be questioned in patients with chronic obstructive pulmonary disease (COPD), we compared time constants calculated from various parts of expiration and related these to time constants assessed with the interrupter method. Prospective study. A medical intensive care unit in a university hospital. Thirty-eight patients (18 severe COPD, eight mild COPD, 12 other pathologies) were studied during mechanical ventilation under sedation and paralysis. Time constants determined from flow-volume curves at 100%, the last 75, 50, and 25% of expired tidal volume, were compared to time constants obtained from interrupter measurements. Furthermore, the time constants were related to the actual time needed for complete expiration and to the patient's pulmonary condition. The time constant determined from the last 75% of the expiratory flow-volume curve (RCfv75) was in closest agreement with the time constant obtained from the interrupter measurement, gave an accurate estimation of the actual time needed for complete expiration, and was discriminative for the severity of COPD. In mechanically ventilated patients with and without COPD, a time constant can well be calculated from the expiratory flow-volume curve for the last 75% of tidal volume, gives a good estimation of respiratory mechanics, and is easy to obtain at the bedside.

Influence of inertance on respiratory mechanics measurements in mechanically ventilated puppies

The complete equation of motion for a single compartment model (SCM) includes an inertance term to describe pressure changes in phase with acceleration, as well as terms for resistance and elastance. Inertance has traditionally been excluded from the model when measuring respiratory mechanics at conventional ventilatory frequencies in mature respiratory systems. However, this omission has been questioned recently for measurements of respiratory mechanics in intubated infants where higher ventilation frequencies and smaller tracheal tubes are the norm. We investigated 1) the significance of inertance in an immature respiratory system during mechanical ventilation, and 2) the effect of omitting it from the model on estimates of respiratory mechanics. Six anesthetised, paralysed and mechanically ventilated puppies (2.6-3.9 kg) were studied. A SCM, including an inertance term was fitted to measurements of flow and airway opening (P(AO)) or transpulmonary (P(TP)) pressure using multiple linear regression to estimate respiratory system and lung resistance (R(RS), R(L)), elastance (E(RS), E(L)) and inertance (I(RS), I(L)) respectively, at various ventilation frequencies (0.2-2 Hz). Data obtained at each ventilation frequency were also fitted with a similar model without the inertance term. Inertance contributed significantly to the model at frequencies greater than approximately 0.3-0.5 Hz (20-30 breaths per minute), with I(RS) dominated by the lung. The importance of including the inertance term in the model increased as ventilation frequency increased. Exclusion of inertance from the model led to underestimation of E(RS) and E(L), but no errors in estimates of R(RS) or R(L). The errors increased with ventilation frequency to approximately 10-20% for E(RS) and approximately 10-40% for E(L) at 2 Hz. While inertance contributed significantly to the SCM at ventilation frequencies typically required to maintain normal gas exchange in puppies, the errors from excluding this term were small: <3% for E(RS) and <9% for E(L).

Estimating respiratory mechanics in the presence of flow limitation.

Dynamic collapse of the pulmonary airways, leading to flow limitation, is a significant event in a number of respiratory pathologies, including obstructive sleep apnea syndrome and chronic obstructive pulmonary disease. Quantitative evaluation of the mechanical status of the respiratory system in these conditions provides useful insights into airway caliber and tissue stiffness, which are hallmarks of such abnormalities. However, assessing respiratory mechanics in the presence of flow limitation is problematic because the single-compartment linear model on which most assessment methods are based is not valid over the entire breath. Indeed, even deciding which parts of a breath are flow limited from measurement of mouth flow and pleural pressure often proves to be difficult. In this study, we investigated the use of two approaches to assessing the overall mechanical properties of the respiratory system in the presence of inspiratory flow limitation. The first method is an adaptation of the classic Mead-Whittenberger method, and the second method is based on information-weighted histograms obtained from recursively estimated signals of respiratory resistance and elastance. We tested the methods on data simulated by using a computer model of the respiratory system and on data collected from obese sleeping pigs. We found that the information-weighted histograms provided the more robust overall estimates of respiratory mechanics.

Ventilatory depression by halothane in infants and children.

The purpose of this study was to extend previous observations of a greater decrease in tidal volume in infants than in children during halothane anaesthesia. We analyzed the inspiratory flow waveform recorded during spontaneous ventilation in: infants, two to six months of age, and children, one to five years of age. In addition we analyzed the CO2 signal and the pressure waveform during an occluded inspiration. The pressure generated during the initial 100 msec of inspiratory occlusion, an index of respiratory drive, was analyzed to give some insight into the aetiology of the age-related differences. In 15 infants and 15 children, Flow (V), pressure (Pao) and PCO2 were recorded at three concentrations of inspired halothane (FIH): 0%, 1% and 2% which correspond to an endtidal halothane concentration of about 0.3%, 0.9% and 1.3% respectively. Data were analyzed for minute ventilation (Vi) and parameters of timing (Total time (Ttot), Inspiratory time (Ti)), the amplitude of the neural output (mean inspiratory flow (VT/Ti), tidal volume (VT)) and the shape of the inspiratory breath profile (the inspiratory centroid flow (Ci/Ti), the inspiratory duty cycle (Ti/Ttot)). In some, the airway was occluded at end expiration and the slope of the initial 100 msec of occlusion (dP/dt) together with the maximal negative pressure (PMAX) were measured. Estimates of respiratory mechanics E'rs (PMAX/VT) and (VT/Ti)/(dP/dt) were obtained. The VT and Ttot decreased with increasing FIH in both infants and children (P < 0.05). The PETCO2 increased in both groups and the % increase was greater in infants.(ABSTRACT TRUNCATED AT 250 WORDS)