Confirmation and subsequent monitoring of ventilation during endotracheal anesthesia has been a significant factor in reducing anesthesia-associated morbidity and mortality. HFJV poses a vexing problem for the anesthesiologist, because many of the usual monitors are difficult to duplicate. There is no “gold standard” for monitoring of HFJV. One of the most important monitors of conventional ventilation is the disconnect alarm, which tells us if we are no longer ventilating the patient's lungs. Similarly, during endotracheal anesthesia, changes in phasic pressure, tidal volume, and exhaled CO2 provide robust confirmation of continued ventilation.
Although several authors have described approaches to intermittent assessment of end-tidal CO2 during tubeless surgery, less attention has been devoted to detection of HFJV failure that may occur by disconnect, catheter malposition, device failure, or high chest wall resistance/low pulmonary compliance.12,13 Intermittent measurement of CO2 in the airway may give an indication of long-term ventilation trends but is not a real-time monitor of HFJV.
Assessment of jet ventilation by chest excursion was described by Glenski et al.14 who used pneumobelts to assess manual oxygen injection during laryngoscopy. The respiratory rates were between 40 and 60 breaths/min. Inflation pressures were typically 40 psi and yielded estimated tidal volumes of 250 mL. No mention was made of the effect of cardiogenic oscillations on detection, but given the larger volumes and lower respiratory rates, these effects may not have been apparent. The principal use advocated for this assessment was detection of breath stacking, which might result in barotrauma. Bourgain et al.15 demonstrated a method for measuring end-expiratory pressure (EEP) in HFJV, and correlated this with volume above functional residual capacity using strain gauge measurements of chest and abdominal movement. The primary advantage of this technique is the ability to rapidly detect overdistension to avoid barotrauma. The ability to assess volume above functional residual capacity with EEP was discussed but the technique is limited because it becomes significantly less accurate in the presence of small airway disease. The potential impact of atelectasis on these measurements was not addressed. As such, the ability to rapidly determine efficacy of HFJV from end-tidal CO2, EEP, or chest wall tension may be limited with that technique.
The RIP monitor described herein detected when HFJV was turned off by measuring the absence of chest movement. The raw data were contaminated by cardiogenic oscillations, phasic variation during spontaneous respiration, and occasional artifact. This noise was filtered to reveal the jet ventilation signal. Spontaneous respiration and artifact were readily handled with a high-pass elliptical band filter as described above.
The HFJV signal is produced by a mechanical device with a defined frequency of 120 breaths/min. A comb filter was used because of its ability to produce a narrow passband. The comb filter is a recursive filter that combines a delayed version of the signal with the signal. The filter has a frequency response consisting of regularly spaced peaks that resemble a comb. The structure of the filter is simple; a single delay element and gain specify the filter, making it easy to implement. Because the filter requires few calculations to obtain the result, it is less sensitive to roundoff error. This makes the filter an attractive choice for implementation in inexpensive microcontrollers without double precision floating point capability. The principal design feature and controlled variable of the comb filter is the bandwidth (Df) at a default amplitude threshold of −3 dB. The effect of varying Df on the output of the detector from 0.01 to 0.1 Hz is illustrated in Figure 3. Because the fundamental frequency of the jet ventilator is known, it is a reasonable assumption that the energy imparted to the chest and abdomen will be at this fundamental frequency and its harmonics. In this regard, the comb filter is an efficient means of detecting the jet signal.
For the extraction of the HFJV signal, a type I peak comb filter was selected after consideration of other detection algorithms. The initial high-pass filter stage made a type II peak comb filter (which removes the DC peak present in the type I) redundant. This initial filter stage was chosen to permit us to combine multiple sequences derived from the 25 patients into a single data record to simplify subsequent analysis. A single stage composed of a type II filter might be more appropriate as a real-time, intraoperative detector during HFJV of a single patient and is currently under investigation by our group. Frequency domain techniques such as fast-Fourier transform were considered, but would have required considerably more computational power to achieve the spectral resolution of the comb filter. For detection of a single frequency in close proximity to the predominant noise frequency, the comb filter is a more parsimonious solution. It is relatively immune to fixed-precision arithmetic and thereby makes implementation in a small device more practical.
The detector was developed using comb filtering of the raw RIP signal from the thorax. The detector was able to differentiate between jet ventilation chest movement and chest movement induced by cardiogenic oscillations with a 12.5-second detection delay. Further noise reduction, however, is achieved at the expense of considerably longer rise times for the detector. This occurs because the filter incorporates more old information to permit filtering. This imposes a detection delay that is inversely proportional to the filter bandwidth. The filter has a finite rise time, but the ventilator is either on or off. For a period of time after the ventilator is turned on or off, the output of the filter is in transition. During the rise time, there is a greater probability of generating a false negative for any threshold we might choose to define jet on–jet off. If we use the output of the detector for this period in determining the ROC curve, it will decrease the estimate of the performance of the filter by inclusion of these false negatives. A filter that has a narrower bandwidth will exclude more noise, but will have a longer rise time. By excluding the very brief period of time after transitions in jet state, we get a more accurate estimate of the impact of filter bandwidth on detection accuracy. Depending on the clinical circumstances, a delay on the order of 12.5 seconds could be significant, in which case the anesthesiologist may elect to accept a lower sensitivity (i.e., a greater frequency of false alarms) in exchange for a minimal rise-time delay. The pulse oximeter exhibits a substantially longer delay in alerting the anesthesiologist to a change in the oxygenation status of the patient.
An ideal monitor would provide information regarding adequacy of ventilation during jet. This study was not designed to measure tidal volumes or minute ventilation using the RIP signal. The overall analysis, on which detector performance was measured, used uncalibrated raw data. However, many patients were intubated for a portion of the study. For these patients, calibration of the RIP signal, to obtain an estimate of volumes attained during HFJV, was obtained by placing the patient on volume-controlled ventilation. Volumes included in Figures 2 and 3 were obtained using this calibration methodology and were reported to provide estimations of the range of tidal volumes delivered in representative patients. Tidal volume estimates are not possible for the concatenated data set because calibration was not performed on patients who were not intubated at the end of the procedure.
The discriminating power of this system is high. Indeed, inaccuracy in manual tagging of the jet state may have factitiously decreased the estimated discrimination. This could be remedied by simultaneous, automated detection of ventilator activation that is likely to increase the overall sensitivity of the system. The ROC curve permits us to assess the performance of the detector across a range of thresholds, and informs us whether any threshold can be found that discriminates between the 2 conditions. This study was not intended to identify a single optimal threshold for detection but rather to demonstrate the feasibility of RIP for detection of HFJV within the dynamic range of signals generated. The high performance of the ensemble data suggests that a global threshold could be chosen for all patients, rather than requiring a period of training for each patient. It is conceivable that further research will identify conditions under which alternate thresholds might be desirable to optimize monitoring in certain patient populations.
This study was not powered or designed to predict circumstances under which jet ventilation will fail because of inadequate oxygenation. However, we observed in the first of the 25 patients enrolled that patient oxygen saturation could not be maintained >90% with jet ventilation using 100% fraction of inspired oxygen. Jet ventilation was abandoned and the patient was intubated for the procedure. Analysis of the approximately 1 minute of attempted jet ventilation with the comb filter reveals a jet ventilation signal with amplitude considerably lower than that seen in the other 24 cases. Indeed, in this case, the amplitude of the jet ventilation signal was lower than the cardiogenic oscillations. Applying our RIP system in real time to this patient should have detected no HFJV signal potentially allowing the team to proceed with intubation before the onset of desaturation after which resuscitative measures were the only option. This case also raises the question of what physiologic conditions would lead the HFJV signal to be lower than the cardiogenic oscillation signal. Possibilities include elevated small airway resistance and diminished alveolar compliance. These questions are not addressed by the current study and will require further investigation to resolve. The RIP monitoring system, as currently implemented, cannot quantify adequacy of ventilation or oxygenation. However, the study data demonstrate that RIP can be used to generate a high-fidelity signal under operating room conditions with general anesthesia. Moreover, the system, with the first-pass technical limitations described above, has excellent sensitivity to detect HFJV and thereby function as a monitor to alert the anesthesiologist to both ventilator disconnect or absence of detectable thoracic excursion under the extant conditions. This information might guide the clinician to (1) reconfirm jet ventilation catheter placement and system function, (2) alter jet ventilator settings, or (3) consider alternative modes of ventilation. A system capable of both detecting and quantifying minute ventilation during HFJV would be optimal and we continue to investigate ways to use RIP in this regard.
JHA helped with study design, conduct of study, data analysis, manuscript preparation, and is the author responsible for archiving. This author approved the final manuscript and reviewed the original study data and data analysis. JEM helped with study design, conduct of study, data analysis, and manuscript preparation. This author reviewed the original study data and data analysis. GEW and NM helped with conduct of study.
The authors acknowledge the excellent research support for this study provided by Mary Hammond, BSN, of the Department of Anesthesiology and Critical Care.
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A comb filter adds a delayed version of the signal to itself, creating a spectrum with regularly spaced spikes, resembling a comb. The comb filter can be implemented with feedforward and feedback; the current effort uses both. A block diagram is illustrated in Figure A1.
The z−N denotes a delay of N sampling intervals. For a sampling frequency (fs) of 50 Hz, a 25-element delay yields a 0.5-second delay, corresponding to the jet ventilator rate of 120 breaths/min. The transfer function is given by equation A1:
The filter is characterized by several parameters, as illustrated in Figure A2.
The parameter f0 is the center frequency for the fundamental frequency of the filter, in this case 2 Hz. The parameter Δf is the bandwidth at −3 dB. The choice of sampling frequency and the number of delay elements specifies f0, and Δf can be derived from the values of a and b. Although only 3 peaks are represented in Figure A2, peaks will occur at every integer multiple of f0 from 0 to fs/2.
The simplicity of the filter is evident; a delay line, 2 summing junctions, and 2 gains are all that are required to implement the filter. The filter is well suited to analysis of periodic signals that contain harmonics of a fundamental frequency because every harmonic is equally represented in the output at 0 phase shift.
The recursive nature of the filter causes the output to increase over time when presented with a signal, as illustrated in Figure A3. Here, a sine wave of amplitude 1.0 and frequency of 2 Hz is applied to the filter. The envelope of the signal asymptotically increases to 1.0 over approximately 20 seconds. This property of the filter explains why the decisions made during the first 12.5 seconds after a transition are less reliable; the filter output has not yet stabilized.
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