J. Guttmann

Breathing pattern and additional work of breathing in spontaneously breathing patients with different ventilatory demands during inspiratory pressure support and automatic tube compensation

Objective: We designed a new ventilatory mode to support spontaneously breathing, intubated patients and to improve weaning from mechanical ventilation. This mode, named Automatic Tube Compensation (ATC), compensates for the flow-dependent pressure drop across the endotracheal tube (ETT) and controls tracheal pressure to a constant value. In this study, we compared ATC with conventional patient-triggered inspiratory pressure support (IPS). Design: A prospective, interventional study. Setting: A medical intensive care unit (ICU) and an ICU for heart and thoracic surgery in a university hospital. Patients: We investigated two groups of intubated, spontaneously breathing patients: ten postoperative patients without lung injury, who had a normal minute ventilation (VE) of 7.6 ± 1.7 l/min, and six critically ill patients who showed increased ventilatory demand (VE = 16.8 ± 3.0 l/min). Interventions: We measured the breathing pattern [VE, tidal volume (VT), and respiratory rate (RR)] and additional work of breathing (WOBadd) due to ETT resistance and demand valve resistance. Measurements were performed under IPS of 5, 10, and 15 mbar and under ATC. Results: The response of VT, RR, and WOBadd to different ventilatory modes was different in both patient groups, whereas VE remained unchanged. In postoperative patients, ATC, IPS of 10 mbar, and IPS of 15 mbar were sufficient to compensate for WOBadd. In contrast, WOBadd under IPS was greatly increased in patients with increased ventilatory demand, and only ATC was able to compensate for WOBadd. Conclusions: The breathing pattern response to IPS and ATC is different in patients with differing ventilatory demand. ATC, in contrast to IPS, is a suitable mode to compensate for WOBadd in patients with increased ventilatory demand. When WOBadd was avoided using ATC, the patients did not need additional pressure support.

Volume-dependent compliance in ARDS: proposal of a new diagnostic concept

Objective: Adaptation of ventilator settings to the individuals respiratory system mechanics requires information about the pressure-volume relationship and the change of compliance which is dependent on inflated volume. Unfortunately, established methods of obtaining this information are invasive and time-consuming, and, therefore, not well suited for clinical routine. We propose a new standardized diagnostic concept based on the recently developed slice method. This multiple linear regression method (MLR) determines volume-dependent respiratory system compliance (CSLICE) within the tidal volume (VT) during ongoing mechanical ventilation. The impact of a ventilator strategy, recommended by a consensus conference, on the course of compliance within VT was investigated in patients with the acute respiratory distress syndrome (ARDS) or acute lung injury (ALI).¶Design: Prospective observational study.¶Setting: Intensive care unit of a university hospital.¶Patients: 14 ARDS patients, 2 patients with ALI.¶Interventions: None.¶Measurements and results: After measurement of flow and airway pressure and calculation of tracheal pressure, CSLICE was determined. The resulting course of CSLICE within VT was estimated using a mathematical algorithm. CSLICE data were compared to those obtained by standard MLR. We found decreasing CSLICE mainly in the upper part of VT in all patients. In 7 patients, we found an additional increasing CSLICE mainly in the lower part of VT.¶Conclusions: CSLICE was not constant in patients with ARDS/ALI whose lungs were ventilated according to consensus conference recommendations. The proposed diagnostic concept may serve as a new tool to obtain a standardized estimation of respiratory system compliance within VT non-invasively without interfering with ongoing mechanical ventilation.

Extubation after breathing trials with automatic tube compensation, T-tube, or pressure support ventilation

Background: Automatic tube compensation (ATC) is a new option to compensate for the pressure drop across the endotracheal or tracheostomy tube (ETT), especially during ventilator‐assisted spontaneous breathing. While several benefits of this mode have so far been documented, ATC has not yet been used to predict whether the ETT could be safely removed at the end of weaning, from mechanical ventilation.

Additional inspiratory work of breathing imposed by tracheostomy tubes and non-ideal ventilator properties in critically ill patients.

Objective: To determine the tracheostomy tube-related additional work of breathing (WOBadd) in critically ill patients and to show its reduction by different ventilatory modes. Design: Prospective, clinical study. Setting: Medical ICU of a university teaching hospital. Intervention: Standard tracheostomy due to prolonged respiratory failure. Measurements and results: Ten tracheostomized, spontaneously breathing patients were investigated. As the tube resistance depends on gas flow, patients were subdivided according to minute ventilation into a low ventilation group ( = 10 l/min; n = 5) and a high ventilation group ( > 10 l/min; n = 5). The WOBadd due to tube resistance and non-ideal ventilator properties was calculated on the basis of the tracheal pressure measured. Ventilatory modes investigated were: continuous positive airway pressure (CPAP), inspiratory pressure support (IPS) of 5, 10, and 15 cm H2O above PEEP, and automatic tube compensation (ATC). In the low ventilation group, WOBadd during CPAP was 0.382 ± 0.106 J/l. It was reduced to below 15 % of that value by ATC or IPS more than 5 cm H2O. In the high ventilation group WOBadd during CPAP increased to 0.908 ± 0.142 J/l. In this group, however, only ATC was able to reduce WOBadd below 15 % of the value observed in the CPAP mode. Conclusions: The results indicate that, depending on respiratory flow rate, (1) tracheostomy tubes can cause a considerable amount of WOBadd, and (2) ATC, in contrast to IPS, is a suitable mode to compensate for WOBadd at any ventilatory effort of the patient.

Breathing pattern associated with respiratory comfort during automatic tube compensation and pressure support ventilation in normal subjects

Background: Automatic tube compensation (ATC) is a new option to support spontaneously breathing tracheally intubated patients. We have previously demonstrated an increased respiratory comfort compared to pressure support ventilation (PSV) in volunteers. Here we characterized the breathing pattern during ATC associated with respiratory comfort in comparison to PSV. Furthermore, we studied whether ATC can be substituted by a simple modification of PSV.

Accuracy of automatic tube compensation in new-generation mechanical ventilators.

ObjectiveTo compare performance of flow-adapted compensation of endotracheal tube resistance (automatic tube compensation, ATC) between the original ATC system and ATC systems incorporated in commercially available ventilators. DesignBench study. SettingUniversity research laboratory. SubjectsThe original ATC system, Dräger Evita 2 prototype, Dräger Evita 4, Puritan-Bennett 840. InterventionsThe four ventilators under investigation were alternatively connected via different sized endotracheal tubes and an artificial trachea to an active lung model. Test conditions consisted of two ventilatory modes (ATC vs. continuous positive airway pressure), three different sized endotracheal tubes (inner diameter 7.0, 8.0, and 9.0 mm), two ventilatory rates (15/min and 30/min), and four levels of positive end-expiratory pressure (0, 5, 10, and 15 cm H2O). Measurements and Main ResultsPerformance of tube compensation was assessed by the amount of tube-related (additional) work of breathing (WOBadd), which was calculated on the basis of pressure gradient across the endotracheal tube. Compared with continuous positive airway pressure, ATC reduced inspiratory WOBadd by 58%, 68%, 50%, and 97% when using the Evita 4, the Evita 2 prototype, the Puritan-Bennett 840, and the original ATC system, respectively. Depending on endotracheal tube diameter and ventilatory pattern, inspiratory WOBadd was 0.12–5.2 J/L with the original ATC system, 1.5–28.9 J/L with the Puritan-Bennett 840, 10.4–21.0 J/L with the Evita 2 prototype, and 10.1–36.1 J/L with the Evita 4 (difference between each ventilator at identical test situations, p < .025). Expiratory WOBadd was reduced by 5%, 26%, 1%, and 70% with the Evita 4, the Evita 2 prototype, the Puritan-Bennett 840, and the original ATC system, respectively. The expiratory WOBadd caused by an endotracheal tube of 7.0 mm inner diameter was 5.5–42.2 J/L at a low ventilatory rate and 19.6–82.3 J/L at a high ventilatory rate. It was lowest with the original ATC system and highest with the Evita 4 ventilator (p < .025). ConclusionsFlow-adapted tube compensation by the original ATC system significantly reduced tube-related inspiratory and expiratory work of breathing. The commercially available ATC modes investigated here may be adequate for inspiratory but probably not for expiratory tube compensation.

Detection of endotracheal tube obstruction by analysis of the expiratory flow signal.

Objective: Acute obstruction of endotracheal tubes (ETT) increases airway pressure, decreases tidal volume, increases the risk of dynamic hyperinflation by prolonging the duration of passive expiration, and prevents reliable calculation of tracheal pressure. We propose a computer-assisted method for detecting ETT obstruction during controlled mechanical ventilation. The method only requires measurement of the expiratory flow. Design: Computer simulation; prospective study in two cases; retrospective study in one case and in seven patients with the adult respiratory distress syndrome (ARDS). Setting: Laboratory of the Section of Experimental Anaesthesiology (University of Freiburg); surgical adult intensive care units in a university hospital (University of Basel) and in a university affiliated hospital (Zentralklinikum Augsburg). Patients: 3 patients with partial ETT or bronchial obstructions and 7 ARDS patients. Measurements and results: Expiratory flow was measured using a pneumotachograph and integrated to obtain expiratory volume. The time-constant of passive expiration (τE) as a function of expired volume [τE(VE) function] was calculated from the expiratory volume/flow curve. We investigated the τE(VE) function of data obtained from: (1) computer simulation of mechanically ventilated homogeneous and inhomogeneous lungs intubated with ETTs of different sizes; (2) one patient with an artificial ETT obstruction of 7.5 and 25 % of the cross-sectional area of the ETT (case 1); (3) one patient with ETT obstruction due to secretions (case 2); (4) one patient with acute bronchial constriction (case 3); (5) seven ARDS patients who showed an increase in airway resistance of more than 2 cm H2O · s/l. It was found that an ETT obstruction caused an increase in τE in early expiration (at high flow), whereas τE in late expiration was virtually unchanged. The reason for this is the flow dependency of the increase in ETT resistance produced by ETT obstruction. Unlike ETT obstruction, an increase in pure airway resistance produced an increase in τE throughout expiration. Conclusions: An ETT obstruction can be reliably distinguished from an increase in pure airway resistance by a characteristic pattern change in the τE(VE) function, which can be detected easily even by an automated pattern recognition system.

Analysis of respiratory pressure-volume curves in intensive care medicine using inductive machine learning

We present a case study of machine learning and data mining in intensive care medicine. In the study, we compared different methods of measuring pressure-volume curves in artificially ventilated patients suffering from the adult respiratory distress syndrome (ARDS). Our aim was to show that inductive machine learning can be used to gain insights into differences and similarities among these methods. We defined two tasks: the first one was to recognize the measurement method producing a given pressure-volume curve. This was defined as the task of classifying pressure-volume curves (the classes being the measurement methods). The second was to model the curves themselves, that is, to predict the volume given the pressure, the measurement method and the patient data. Clearly, this can be defined as a regression task. For these two tasks, we applied C5.0 and CUBIST, two inductive machine learning tools, respectively. Apart from medical findings regarding the characteristics of the measurement methods, we found some evidence showing the value of an abstract representation for classifying curves: normalization and high-level descriptors from curve fitting played a crucial role in obtaining reasonably accurate models. Another useful feature of algorithms for inductive machine learning is the possibility of incorporating background knowledge. In our study, the incorporation of patient data helped to improve regression results dramatically, which might open the door for the individual respiratory treatment of patients in the future.

Early detection of upper airway obstructions by analysis of acoustical respiratory input impedance

Abstract. Repetitive occurrence of partial or total upper airway obstruction characterizes several respiratory dysfunctions such as the obstructive sleep apnea syndrome (OSAS). In OSAS patients, pharyngeal collapses are linked to a decrease in upper airway muscle activity during sleep which causes decreased upper airway wall stiffness. Continuous positive airway pressure (CPAP) is recommended as the treatment of choice. Advancements in CPAP therapy require early detection of respiratory events in real time to adapt the level of the applied pressure to airway collapsibility. The forced oscillation technique (FOT) is a noninvasive method which reflects patients airway patency by measuring respiratory impedance. The aim of this study was to evaluate by a mathematical model of the respiratory system if FOT can provide an early detection index of total or partial upper airway obstruction. Furthermore, the simulation should suggest which characteristic features are relevant for early apnea detection in measured clinical data. The respiratory system has been treated as a series of cylindrical segments. The oropharynx analog of the model allows simulation of upper airway collapse, mimicking the situation in patients with OSAS. We calculated the input impedance for different degrees of upper airway obstruction ranging from unobstructed airways to total occlusion. Furthermore, we simulated different upper airway wall compliances. We compared the simulation with real data. The results of the study suggest that FOT is a valuable tool for assessing the degree of upper airway obstruction in patients with OSAS. Especially, the phase angle of the impedance seems to be a potentially useful tool for early apnea detection by assessing the upper airway wall collapsibility.

Detection of Sleep Apnea with the Forced Oscillation Technique Compared to Three Standard Polysomnographic Signals

Background: The forced oscillation technique (FOT) allows analysis of the upper airway impedance and, hence, detection of obstructive sleep apnea. Objective: To evaluate FOT with respect to sensitivity and to specificity in online detection of sleep-disordered breathing patterns and to compare algorithmic onset detection time with manual onset time markers of staff physicians. Methods: We compared the absolute value ∣Z∣ of the impedance with three routinely obtained polysomnographic signals – nasal airflow v̇nasal, thoracic excursion Thox and esophageal pressure Pes – by retrospective analysis of the diagnostic polysomnograms of 51 patients. For each signal we evaluated algorithms for online detection of respiratory events. For each out of five apnea classes, 50 respiratory events marked by staff physicians were drawn randomly from the 51 polysomnograms to optimize the online detection algorithms (learning set). The algorithm analyzes relative changes of signal baseline and amplitude. Again 50 respiratory events were drawn randomly for each apnea class to examine to what extent it is possible to detect event onsets with the algorithms (test set). Results: The sensitivity of the signals varied between 56 and 94% and was on average 74%. The specificity was 96 ± 1.5% on average. The onset was detected 4–6 s after the initially evaluated onset of the staff physicians. Conclusion: We conclude that nasal airflow and FOT are equivalent sensitive measurands for detection of respiratory events.