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*Department of Clinical Physiology, Uppsala University Hospital, Sweden; and
Department of Emergency and Transplantation, Bari University Hospital, Italy
Address correspondence and reprint requests to Gaetano Perchiazzi, MD, Centro di Rianimazione - Ospedale Policlinico, Piazza Giulio Cesare, 11, 70124 Bari, Italy. Address e-mail to gperchiazzi{at}rianima.uniba.it
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Abstract |
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IMPLICATIONS: We studied the application of artificial neural networks (ANN) to the estimation of respiratory compliance during mechanical ventilation. The study was performed on an animal model of acute lung injury, testing the performance of ANN in both healthy and diseased conditions of the lung.
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Introduction |
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The interrupter technique (IT) derives from the notion that after the inspiratory flow (V’) is stopped, the ratio between the inspired volume and the subsequent pressure change is related mainly to the static components of respiratory mechanics (5). IT, involving the stop of the flow, is considered the gold standard for respiratory mechanics measurement, and for this reason, it has been used in the present study.
Methods based on artificial neural networks (ANNs) enable another approach to the estimation of respiratory mechanics. ANNs are artificial intelligence systems based on the connectionism theory (6). They are universal function approximators (7) and can extract information from different classes of signals after having been trained to perform this specific task by learning from examples.
In the field of ANN application to respiratory mechanics, important contributions have come from Orr and Westenskow (8) on the alarms of anesthesia breathing circuits, Bright et al. (9) on the detection of upper airway obstruction, Léon et al. (10) concerning the detection of esophageal intubation, and Räsänen and Léon (11) on the assessment of lung injury in an animal model.
We demonstrated (12) in an animal model the possibility of assessing the static compliance of the respiratory system (CRS) by providing ANNs with respiratory tracings obtained during an end-inspiratory pause (EIP). In that experimental setup, the extraction of CRS was dependent on the presence of a flow interruption in the tracings to be analyzed by the ANN.
The underlying disease may not allow alterations of breathing pattern to enable the analysis of respiratory mechanics. In these cases, the availability of a tool not requiring such interventions could be beneficial. Considering that information regarding the CRS is present also in the slope of the pressure signal (during constant flow mechanical ventilation), we hypothesized that an EIP should not be required for the assessment of CRS.
Accordingly, the aim of the present experiments was to evaluate whether ANNs can estimate CRS using tracings of PAO and V’ versus time without any intervention of an inspiratory hold maneuver to produce an EIP during continuous mechanical ventilation. This was tested in an animal model during healthy and pathological conditions of the lung.
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Methods |
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The study was approved by the local IRB for the care of animal subjects. Twenty-four healthy pigs (weight, 29.6 ± 4.6 kg) were included in the study. The group used for training and testing the ANN was composed of 16 animals, whereas the remaining 8 animals comprised the prospective trial group.
After premedication with azaperone, anesthesia was induced by IM administration of atropine, tiletamine-zolazepam, and medetomidin. After oral intubation, total IV anesthesia was started with the administration of ketamine, pancuronium, and fentanyl. Invasive monitoring of central venous, pulmonary artery, and systemic arterial pressure were performed during the experiment; cardiac and urinary outputs were also measured. Fluid replacement strategy was aimed at maintaining a stable systemic arterial pressure. Arterial and mixed venous samples were taken to measure PO2, PCO2, and pH during the various phases of the experiment.
The animals were ventilated using volume-controlled constant-flow mechanical ventilation (VC-MV) (Servo 900 C, Siemens Elema, Solna, Sweden). Tidal volume was adjusted (8–9 mL/kg) to result in normocapnia (35–45 mm Hg) using blood gas samples. Extrinsic positive end-expiratory pressure (PEEPe) was set to 5 cm H2O. Inspiratory fraction of oxygen was 0.5. Inspiratory-to-expiratory ratio was set to 1:2 [s] for a respiratory rate of 20 breaths/min. Acute lung injury was induced by repeated injections of small boluses of OA into the central venous catheter, targeting a total dose of 0.1 mL/kg.
The two sampling ports of a D-Lite connector (Datex Ohmeda, Helsinki, Finland) mounted to the endotracheal tube were connected to a differential pressure transducer (Sensym, SensorTechnics, Pucheim, Germany). At the beginning of each experimental session, the transducer was calibrated for static pressures and for flow measurements. Data were sampled at 200 Hz from the transducer by the Carina 2.4.0 acquisition program (C-O Sjöberg Engineering AB, Upplands-Väsby, Sweden). After real-time collection, traces of V’, PAO, and tidal volume were stored on a personal computer. In both reference and prospective groups of animals, respiratory tracings were recorded at fixed time intervals: after a stabilization period of 60 min after instrumentation and 5, 20, 35, 50, 65, 95, and 125 min after the administration of OA. Each recording session consisted of the simultaneous collection of PAO and V’ coming from 10 or more consecutive breaths in VC-MV, followed by a breath with an inspiratory pause. The pause was held until a stable plateau pressure was reached and was never <2.5 s (Fig. 1).
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To introduce a further source of variability during ANN training, during the interval between 65 and 95 min after the administration of OA, PEEPe was randomly changed to 0 or 10 cm H2O. Of the recorded sequence of breaths, the last breath, i.e., the one having the EIP, was used for the manual calculation of CRS by applying the IT (5).
The inspiratory limb of the volume-pressure loop (V-P Loop) of the breath immediately preceding the breath with an inspiratory pause was given to the ANN (Fig. 2).
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To determine the best architecture, i.e., the number of intermediate neurons that provided the best performance for the required task, we used an eight-fold cross-validation with early stopping (more details are given in the on-line data supplement). We tested architectures with a number of intermediate neurons between 2 and 30. The pool of curves composing the reference group of pigs was randomly divided into 2 new subsets in the ratio of 80:20. The biggest subset was used for training the ANN and the smaller one to validate the learning process.
The prospective trial consisted of presenting the tracings coming from the prospective group of 8 pigs (57 recordings) to the chosen ANN. ANN performance was studied by calculating the linear regression between the results yielded by the ANN and the manual measure of CRS (using IT) and analyzing the measurement error according to Bland and Altman (13). The measurement error was calculated over the entire prospective pool of curves and also in healthy and sick conditions of the lung separately. Linear regression was used to analyze whether the error by the ANN was dependent from the absolute level of compliance.
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Results |
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In the reference group of pigs, 125 min after the injection of OA, CRS decreased from 22.9 ± 5.5 (at baseline) to 14.8 ± 3.9 mL/cm H2O (Fig. 4). Baseline data and values obtained 5 min after OA injection did not differ and were used as healthy lung (HL) data. CRS was significantly reduced from 20 min after OA injection (P < 0.01), and results from 20 to 125 min after OA injection were used as sick lung (SL) data.
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Applying the previously trained ANN to the prospective group of data, the ANN performance in assessing CRS was expressed by (using the same notation as above) y = 0.94x + 1.69 with r = 0.97 (r2 = 0.94) (Fig. 5).
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Linear regression of ANN error in assessing CRS (y) versus the average measure of CRS by ANN and IT (x) showed y = 0.03x - 1.23 with r = 0.12 (r2 = 0.01), which was not significant. Thus, the severity in lung pathology, as assessed by CRS, had no influence on the relationship between ANN and IT.
Moreover, the error in estimating CRS from recordings made at PEEPe = 0 and PEEPe = 10 is not different from the error obtained from recordings at PEEPe = 5.
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Discussion |
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The tracings coming from the animal were presented to the ANN as V-P Loops. The reason for this choice is that the CRS function is the derivative (i.e., the instantaneous slope) of a V-P curve, and it is not influenced by any re-scaling procedure that affects the absolute values of volume and pressure provided that the ratio between them is maintained. We have used this property of derivative functions for applying one of the known heuristic methods to obtain better performances by ANNs. We have separately scaled all curves to make all the pairs of coordinates range between 0 and 1. Each V-P Loop had its maximum of pressure as its own scaling factor. In each loop, all the pairs of coordinates were divided by this scaling factor. This procedure made each curve retain its values of instantaneous compliance but, at the same time, hastened the ANN convergence towards a solution because of the use of smaller numbers (14).
Whereas in the reference group of animals we collected two recordings per time interval, in the prospective group of animals, we collected only one per time interval. This decision depended on the fact that in the training phase, we needed to collect a number of examples as big as possible and also with very slight difference between them. The prospective phase was oriented to eliciting performance error caused by biological variability; it was required to test only one condition per time interval per animal to avoid adding artifacts to the estimation of ANN performance because of multiple similar examples.
The basic advance of this paper over our previous one (12) is the possibility of assessing lung compliance without having to stop V’. This achievement may make the ANN-based method a valuable monitoring tool.
The IT has the advantage of being based on static or semistatic conditions, such as those obtained after flow interruption. After a transient, pressure reaches a plateau that is the expression of the static properties of the respiratory system. During the plateau, the impact of transient phenomena, such as stress-relaxation or gas redistribution, can be considered negligible (15). Thus, to obtain the best results from IT, it is required to maintain the occlusion for some time. The EIP, if it exists at all, may be too short to allow pressure equilibration and the calculation of a true CRS. A deliberate prolongation of the breath hold can raise concerns about the potential for harmful consequences in circulatory unstable patients. Neumann et al. (16) showed, in an animal model of acute lung injury by OA administration, that alveolar recruitment is a continuous event during inspiration, and an EIP may have an impact on the efficiency of such recruitment. Moreover, an end-inspiratory hold maneuver for measuring respiratory mechanics may yield results that are different from the ones obtained during ventilation without EIP.
The necessity of monitoring respiratory mechanics without interfering with the actual pattern of breathing required investigators to apply inverse modeling techniques. One of the most frequently used is the MLF method (1). Application of MLF is based on the assumption that the mechanical properties of the respiratory system are constant and time-invariant over the analyzed breath. This introduces an error in estimating respiratory variables that becomes more important during pathologic conditions. In fact, in these conditions, it is possible to verify that respiratory mechanics may no longer be linear (17), and the accuracy of the method becomes decreased, thus requiring corrections of the algorithm (18) or limiting the application of MLF to specific segments of the breathing cycle (19).
The ANN-based method does not require any assumption regarding the model to be fitted because the type of ANN used in this paper (multilayer perceptrons) are universal function approximators (7,20,21) and can reproduce models of high complexity without limitations to the degree of nonlinearity.
The ANN-based method, as described here, computes only one value of CRS (the one that is referred to as the actual pattern of breathing). It may be disputable whether one value is sufficient for the assessment of the elastic properties of the respiratory system. If the objective is the speculative analysis of the mechanical properties of the lung, the best choice still remains to draw a V-P curve, although it has been affirmed to be difficult in some contingencies (22).
When the objective is to monitor a variable for the control of a machine, one needs a method that is not only accurate, but also robust. Several working definitions of robustness have been proposed; in the case of estimation methods, the adjective "robust" is applied to those methods that "work well not only under ideal conditions, but also under conditions representing a departure from an assumed distribution or model (23)."
MLF can maintain a certain stability in the presence of random noise with Gaussian distribution (24), but in the analysis of multicompartmental models, a variable amount of error will affect the MLF (18). MLF is thus robust only when the respiratory system is in healthy conditions or presents a pathology that uniformly affects the lungs, thus modifying its mechanical properties in a homogeneous way.
ANN-based methods are robust because when extracting information from a curve, they do not require a preconceived model to be fitted. Having no limitation of model equation degree, by definition they are not affected by changes from linear to nonlinear behaviors provided that they have been trained on examples of both situations. This paper does not address the problem of quantification of the robustness of the method; however, there is strong mathematical support in computer science literature on the qualities of ANN in this respect (25).
ANN-based technology, although theoretically applicable to different clinical conditions, in this paper has been tested on a limited number of conditions. In future studies, it will be required to measure ANN performance in other settings, such as different breathing patterns, or other lung pathologies not considered here. The advantage of ANN technology, as emphasized here, is that it does not require explicit programming. The disadvantage is that ANN requires examples to be trained on.
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Acknowledgments |
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The authors wish to thank Karin Fagerbrink, Eva-Maria Hedin, and Agneta Roneus, laboratory assistants at the Department of Clinical Physiology, Uppsala University, for their invaluable contribution to the success of these experiments.
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