Closed-loop Control of Respiratory Drive Using Pressure Support Ventilation: Target Drive Ventilation

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1 Closed-loop Control of Respiratory Drive Using Pressure Support Ventilation: Target Drive Ventilation Jadranka Spahija, Jennifer Beck, Michel de Marchie, Alain Comtois and Christer Sinderby Online Data Supplement 1

2 METHODS Subjects Eleven healthy individuals (3 females, 8 males) with a mean age of 40 years (range, 35 to 52) participated in the study. The study was approved by the Scientific and Ethical Committees of Sainte-Justine s Hospital, Montreal, Canada, and all subjects gave their informed consent. Nine of the subjects were naïve to the purpose of the study; none of the subjects were aware of the EAdi target range that was used during experimentation. Experimental protocol While seated on a bicycle ergometer, each subject performed maximum sniff inhalations from functional residual capacity with one nostril occluded, and inspiratory capacity maneuvers to determine maximum voluntary diaphragm activation (E1). Thereafter, with nasal passages occluded by a nose clip and seated on the bicycle, subjects breathed through a mouthpiece attached to a breathing circuit via a two-way nonrebreathing valve. The inspiratory line of the breathing circuit contained a non-linear flow resistance (18 cm H 2 O/L/sec at a flow of 1 L/sec), and the expiratory line contained a Starling resistor (expiratory flow limitation of L/sec). The former was used to increase the inspiratory EAdi in the healthy subjects as has been observed in patients with COPD (E1-E4), and the latter to limit expiratory flow and induce inspiratory muscle weakness secondary to dynamic hyperinflation (E5, E6), thus further increasing the EAdi (E7, E8). The breathing circuit, which had a dead space of 200 ml was connected to a modified Siemens Servo 300 ventilator (Siemens Elema, Solna, Sweden). The experiment consisted of two experimental runs. 2

3 During the first run, measurements were made with subjects breathing first for six minutes at rest, while seated on the bicycle ergometer. This was followed by 6 minutes each of constant workload bicycle exercise at 20 watts and at 40 watts, ending with 6 minutes of recovery following exercise. During this run there was no mechanical ventilatory support provided (control). After minutes of resting breathing off the apparatus, the preceding experimental sequence was repeated, except this time, TDV was set to target EAdi at a level of ~ 60% of the difference in mean inspiratory EAdi observed during the control period of resting breathing to 20 watts exercise. PEEP was set to 0 cm H 2 O during all conditions. Instrumentation Airflow was measured with a pneumotachograph and tidal volume was obtained by integrating flow. The crural EAdi was obtained via a multiple array esophageal electrode consisting of nine gold-plated rings (3 mm wide and 4 mm in diameter) placed 10 mm apart (ring-to-ring center), creating an array of eight sequential differential electrode pairs, and mounted on silicone tubing (diameter 3 mm). The electrode was inserted through the nose and positioned in the esophagus at the level of the crural diaphragm. Accurate positioning of the EAdi electrode was confirmed by online display of ECGs and correllograms of EAdi signals along the electrode array (E9, E10) (cross-correlation technique described below). Signals from each electrode pair were amplified (INA102, Burr-Brown) and high-pass filtered at 10 Hz (single-pole filter) with an antialiasing filter at 1 khz (D70L8L-1.00 khz, 8-pole Bessel filter, frequency Devices). The EAdi was acquired (DT 2821, Data Translation) at 2 khz (12-bit resolution). 3

4 Esophageal (Pes) and gastric (Pga) pressures were measured using two balloon-tipped catheters (internal diameter 0.75 mm) that were anchored within the lumen of the EAdi catheter and connected to differential pressure transducers. The latex balloons used were 5cm long and 1.5 cm in diameter and were mounted 4.5 cm cephalad to the most cephalad ring and 12 cm caudal to the most caudal ring for Pes and Pga measurement, respectively. The position of the balloons was verified by using the occlusion test (E11). Transdiaphragmatic pressure (Pdi) was obtained by subtracting Pes from Pga. Mouth pressure and ventilator pressure (Pvent) were measured via side ports in the mouthpiece and ventilator tubing (distal to the resistance circuit), respectively. The flow and pressures were acquired (DT 2801A, Data Translation) at a sampling frequency of 100 Hz (12-bit resolution). Minute ventilation and pulmonary gas exchange were measured breath-by-breath in eight of the eleven subjects, with a Vmax computerized system (Sensormedics, Yorba Linda, CA). A two-point calibration of the gas analyzers using reference gases was performed prior to each study. The measured O 2 consumption, CO 2 production, end-tidal PCO 2 and the minute ventilation were stored on a separate computer for later analysis. On-line automatic processing of EAdi Initial Filtering: To remove the influence of electrode motion artifacts, noise and ECG, the EAdi signals were filtered to give the highest possible signal-to-disturbance ratio determined from experimental data using Wiener filtering, implemented with individual filter links of lowpass, band-pass, high-pass, and notch filter characteristics (E1, E10, E12). Cross-correlation technique: The position of the electrically active region of the diaphragm (EARdi) was determined from the non-processed differentially recorded signal 4

5 segments of 32 ms duration obtained via the electrode array using the cross-correlation technique (E8, E12). The most negative correlation coefficient between any two pairs of electrodes indicates that the respective signals are the most reversed in polarity. The electrode pair that is located between these two most negatively correlated pairs is the electrode pair closest to the center of the EARdi. This algorithm was applied continuously to the multiple array signals in order to account for movement of the catheter and/or the diaphragm during breathing. Double-subtraction technique: After determination of the EARdi center position, the last 16 ms signal segments obtained from the two electrode pairs located on either side of the EARdi center, i.e 10 mm caudal and 10 mm cephalad, are subtracted from each other. This algorithm yields a new signal, the "double subtracted signal", which is less influenced by electrode filtering, and enhanced in signal to noise ratio (E10). The root mean square (RMS) was then calculated for the double subtracted signal as well as for the center signal (i.e. the electrode pair located in between the double subtracted signals) and their RMS values are then summed (E8) to form the EAdi signal used to control the ventilator. Signal segments with residual disturbances due to cardiac electric activity or common mode signals were evaluated via specific detectors, and could be replaced by a predicted value e.g. the previously accepted value (E1, E8, E14). The processed signal was used to control the mechanical ventilator and stored in the computer for later analysis. With this technology, changes in muscle to electrode distance are accounted for (E9, E10) and processing eliminates cardiac activity, esophageal peristalsis, and electrode motion artefacts, with optimized EAdi signal to noise ratio (E1, E8, E10, E14). The EAdi is not artefactually influenced by changes in muscle length, flow rate, chest wall configuration and/or lung volume during voluntary contractions (E7, E13, E15, E16, E17). 5

6 Automatic adjustment of PSV by targeting EAdi The processed EAdi signal was averaged for each inspiration and a five-breath moving average (EAdiMEAN) was computed to stabilize breath-by-breath variability. This signal was displayed on-line and simultaneously transformed into a calibrated analogue output signal, which was transferred to the mechanical ventilator for use as the feedback source of ventilator control (Figure 1E). Once a target level of EAdi had been set (with a ± 10% range), PSV was maintained at a constant level when EAdiMEAN remained within the targeted range. The level of PSV was increased by 0.5 cm H 2 O per breath when EAdiMEAN was above the targeted range, and decreased by 0.5 cm H 2 O when EAdiMEAN was below the targeted range. Method of neural triggering and cycling-off The ventilator was equipped with an external analog input for triggering of PSV using EAdi in combination with the built-in flow/pressure triggering of the Siemens Servo 300 ventilator, on a first-come-first-serve basis. Neural triggering and cycling-off algorithms were implemented using the EAdi signals that were processed and filtered to eliminate signal disturbances (see above). A recursive time domain filter was additionally applied to further smoothen the processed EAdi signal. Ventilatory assist was initiated when the EAdi exceeded a threshold increment. Given that the variability of the noise level was low, the trigger threshold could be set to a fixed level, permitting early detection of increased diaphragm activation without causing auto-triggering when the diaphragm was inactive. The trigger level could be adjusted if necessary. Ventilator cycling-off was accomplished by using the EAdi and occurred when EAdi dropped to 80% of the peak activity for a given breath. 6

7 For the purpose of external triggering, the Siemens Servo 300 ventilator samples the external analog signal at a frequency of 500Hz. There is an internal processing delay of about 10 ms and valve response delay of about 5 ms before the assist is delivered. With regards to processing external to the Servo 300 ventilator, the signal sample size used is of 16 ms duration and it takes about 5 ms to process. Analysis Off-line signal analysis Flow, as well as the mouth, esophageal, gastric and ventilator pressures were acquired simultaneously with the EAdi data. Subsequently, with the signals displayed on the computer screen, the onset of each inspiration and expiration was determined from the flow signal, identified with a cursor and stored as a reference file. Using the reference file, timing parameters, including inspiratory duration (Ti), total breath duration (Tt), duty cycle (Ti/Tt) and breathing frequency (f B ) were thus determined breath-by breath. Mean Pdi swings were calculated between the onset of EAdi and the end of inspiratory flow. The pressure-time product of the Pdi (PTPdi) was obtained breath-by-breath by multiplying the area subtended by the Pdi signal in each breath by the respiratory frequency (60/Tt). The level of PSV delivered by the ventilator breath-bybreath was identified as the plateau pressure from the ventilator pressure signal. EAdiMEAN was expressed as a percentage of the voluntary maximum EAdi (obtained from inspiratory capacity maneuvers). Minute ventilation, tidal volume, timing parameters, mean Pdi, PTPdi and the PSV level were averaged for each minute, in all conditions, for all subjects. Because steady-state conditions are generally established only after the 4 th minute, the stored gas exchange variables 7

8 recorded in the last two minutes of resting breathing, 20 watts and 40 watts exercise were averaged in the subsequent analysis. Statistical analysis Variables were compared between control and TDV runs during resting breathing and the two levels of exercise using two-way repeated measures ANOVA and post hoc contrasts of significant effects were performed using the Student-Newman-Keuls test (SPSS version 12.0). Values in text and figures are means ± SD unless otherwise indicated. The level of significance for all statistical tests was set to P<

9 REFERENCES ONLINE REPOSITORY E1. Sinderby C, Beck J, Weinberg J, Spahija J, Grassino A. Voluntary activation of the human diaphragm in health and disease. J Appl Physiol 1998; 85: E2. Aubier M, Murciano D, Fournier M, Milic-Emili J, Pariente R and Derenne J-P. Central respiratory drive in acute respiratory failure of patients with chronic obstructive pulmonary disease. Am Rev Respir Dis 1980; 122: E3. Murciano D, Aubier M, Bussi S, Derenne JP, Pariente R, R, Milic-Emili J. Comparison of esophageal, tracheal, and mouth occlusion pressure in patients with chronic obstructive pulmonary disease in acute respiratory failure. Am Rev Respir Dis 1982; 126: E4. Murciano D, Boczkowski J, Lecocguic Y, Emili JM, Pariente R, Aubier M. Tracheal occlusion pressure: a simple index to monitor respiratory muscle fatigue during acute respiratory failure in patients with chronic obstructive pulmonary disease. Ann Intern Med 1988; 108: E5. Yan S, Kayser B. Differential inspiratory muscle pressure contributions to breathing during dynamic hyperinflation. Am J Respir Crit Care Med 1997; 156: E6. Yan S, Kayser B, Tobiasz M, Sliwinski P. Comparison of static and dynamic intrinsic positive end-expiratory pressure using the Campbell diagram. Am J Respir Crit Care Med 1996; 154: E7. Beck J, Sinderby C, Lindström L, Grassino A. Effects of lung volume on diaphragm EMG signal strength during voluntary contractions. J Appl Physiol 1998; 85,

10 E8. Sinderby C, Spahija J, Beck J, Kaminski D, Yan S, Sliwinski P. Diaphragm activation during exercise in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2001; 163: E9. Beck J, Sinderby C, Lindström L, Grassino A. Influence of bipolar electrode positioning on measurements of human crural diaphragm EMG. J Appl Physiol 1996; 81: E10. Sinderby C, Beck JC, Lindström L, Grassino A. Enhancement of signal quality in esophageal recordings of diaphragm EMG. J Appl Physiol 1997; 82: E11. Baydur A, Cha EJ, Sassoon CS. Validation of esophageal balloon technique at different lung volumes and postures. J Appl Physiol 1987; 62: E12. Sinderby C, Lindström L, Grassino A. Automatic assessment of electromyogram quality. J Appl Physiol 1995; 79: E13. Beck J, Sinderby C, Weinberg J, Grassino A. Effects of muscle-to-electrode distance on the human diaphragm electromyogram. J Appl Physiol 1995; 79: E14. Sinderby C, Navalesi P, Beck J, Skrobik Y, Comtois N, Friberg S, Gottfried SB, Lindström L. Neural control of mechanical ventilation. Nat Med 1999; 5: E15. Sinderby C, Lindstrom L, Comtois N, Grassino AE. Effects of diaphragm shortening on the mean action potential conduction velocity in canines. J Physiol (Lond) 1996; 490: E16. Beck J, Sinderby C, Lindstrom L, Grassino A. Diaphragm interference pattern EMG and compound muscle action potentials: effects of chest wall configuration. J Appl Physiol 1997; 82: E17. Beck J, Sinderby C, Lindstrom L, Grassino A. Crural diaphragm activation during dynamic contractions at various inspiratory flow rates. J Appl Physiol 1998; 85:

11 Figure 1E: Sensory Inputs Motor outputs Cortex RHYTHM GENERATOR Motor discharge (drive, timing) Spinal motoneuron action potential propagation Neuromuscular transmission Muscle/Joint mechanoreceptors Lung/Airway receptors Muscle excitation accessory intercostal abdominal diaphragm Muscle contraction (tension) Respiratory pressures EAdi Muscle strength, length, velocity Configuration/ mechanical advantage Resistance / Compliance Signal processing unit Ventilator unit Chest wall and lung expansion (V I,V T,f B ) V D /V T Cardiovascular receptors Alveolar ventilation Chemoreceptors Acid-base balance Gas exchange Illustration of the ventilatory control system showing the pathways for sensory input and motor output, to and from the central rhythm generator, respectively. Shown also are the factors involved in the transformation of the electrical signal into alveolar ventilation (neuroventilatory coupling). Target Drive Ventilation uses the measured diaphragm electrical activity as the signal to regulate the ventilator assist. 11

RESPIRATORY PHYSIOLOGY, PHYSICS AND PATHOLOGY IN RELATION TO ANAESTHESIA AND INTENSIVE CARE

Course n : Course 3 Title: RESPIRATORY PHYSIOLOGY, PHYSICS AND PATHOLOGY IN RELATION TO ANAESTHESIA AND INTENSIVE CARE Sub-category: Intensive Care for Respiratory Distress Topic: Pulmonary Function and