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Ventilation-Induced Modulation of Pulse Oximeter Waveforms: A Method for the Assessment of Early Changes in Intravascular Volume During Spinal Fusion Surgery in Pediatric Patients

Alian, Aymen A. MD*; Atteya, Gourg MD*; Gaal, Dorothy MD*; Golembeski, Thomas MD*; Smith, Brian G. MD; Dai, Feng PhD*; Silverman, David G. MD*; Shelley, Kirk MD, PhD*

doi: 10.1213/ANE.0000000000001377
Technology, Computing, and Simulation: Original Clinical Research Report
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BACKGROUND: Scoliosis surgery is often associated with substantial blood loss, requiring fluid resuscitation and blood transfusions. In adults, dynamic preload indices have been shown to be more reliable for guiding fluid resuscitation, but these indices have not been useful in children undergoing surgery. The aim of this study was to introduce frequency-analyzed photoplethysmogram (PPG) and arterial pressure waveform variables and to study the ability of these parameters to detect early bleeding in children during surgery.

METHODS: We studied 20 children undergoing spinal fusion. Electrocardiogram, arterial pressure, finger pulse oximetry (finger PPG), and airway pressure waveforms were analyzed using time domain and frequency domain methods of analysis. Frequency domain analysis consisted of calculating the amplitude density of PPG and arterial pressure waveforms at the respiratory and cardiac frequencies using Fourier analysis. This generated 2 measurements: The first is related to slow mean arterial pressure modulation induced by ventilation (also known as DC modulation when referring to the PPG), and the second corresponds to pulse pressure modulation (AC modulation or changes in the amplitude of pulse oximeter plethysmograph when referring to the PPG). Both PPG and arterial pressure measurements were divided by their respective cardiac pulse amplitude to generate DC% and AC% (normalized values). Standard hemodynamic data were also recorded. Data at baseline and after bleeding (estimated blood loss about 9% of blood volume) were presented as median and interquartile range and compared using Wilcoxon signed-rank tests; a Bonferroni-corrected P value <0.05 was considered statistically significant.

RESULTS: There were significant increases in PPG DC% (median [interquartile range] = 359% [210 to 541], P = 0.002), PPG AC% (160% [87 to 251], P = 0.003), and arterial DC% (44% [19 to 84], P = 0.012) modulations, respectively, whereas arterial AC% modulations showed nonsignificant increase (41% [1 to 85], P = 0.12). The change in PPG DC% was significantly higher than that in PPG AC%, arterial DC%, arterial AC%, and systolic blood pressure with P values of 0.008, 0.002, 0.003, and 0.002, respectively. Only systolic blood pressure showed significant changes (11% [4 to 21], P = 0.003) between bleeding phase and baseline.

CONCLUSIONS: Finger PPG and arterial waveform parameters (using frequency analysis) can track changes in blood volume during the bleeding phase, suggesting the potential for a noninvasive monitor for tracking changes in blood volume in pediatric patients. PPG waveform baseline modulation (PPG DC%) was more sensitive to changes in venous blood volume when compared with respiration-induced modulation seen in the arterial pressure waveform.

Published ahead of print June 9, 2016

From the Departments of *Anesthesiology and Orthopaedics, Yale University School of Medicine, New Haven, Connecticut.

Accepted for publication February 24, 2016.

Published ahead of print June 9, 2016

Funding: None.

Conflict of Interest: See Disclosures at the end of the article.

Reprints will not be available from the authors.

Address correspondence to Aymen A. Alian, MD, Department of Anesthesiology, Yale University School of Medicine, 333 Cedar St, PO Box 208051, New Haven, CT 06520. Address e-mail to aymen.alian@yale.edu.

Pediatric patients undergoing spinal fusion surgery are at risk of significant morbidity as a result of intraoperative blood loss, fluid resuscitation, and blood transfusion. It has been reported that an increased intraoperative blood loss is associated with a greater Cobb angle (>50°), larger extent of repair (≥6 levels), concurrent neuromuscular disease, prolonged time of operation, and surgical approach (eg, osteotomy) during spinal fusion surgery.1–5

Clinically, it is difficult to assess the optimum blood volume for pediatric patients undergoing spinal fusion surgery. Although appropriate volume resuscitation is presumably beneficial, excessive fluid may lead to peripheral and pulmonary edema. Fluid boluses are the first line of treatment to optimize intravascular volume and cardiac output in children. Traditional indices, such as blood pressure (BP) and heart rate, may not reflect mild to moderate blood loss.6

The assessment of intravascular volume status, or cardiac preload, is a particular challenge in critically ill children. Traditional static variables of cardiac preload, such as central venous pressure, often used to guide fluid therapy in children, is as poor a guide to fluid resuscitation in children7 as it is in adults.8–10

Dynamic preload indicators, which are measured from ventilation-induced cyclic changes in the cardiac stroke volume, have been shown to be superior to static indicators in guiding fluid therapy in adults.11–13

In mechanically ventilated adults, dynamic variables derived from arterial BP (such as pulse pressure variability [PPV], systolic pressure variability, and stroke volume variability [SVV]) and from the pulse oximeter waveform (changes in the amplitude of pulse oximeter plethysmograph [ΔPOP] and pleth variability index) have been shown to be helpful.14–27

However, in mechanically ventilated children, the dynamic parameters based on arterial BP (SVV, PPV, systolic pressure variability, Δ down, and Δ up) have not worked as well.28–30 It has been hypothesized that the poor predictive value of the dynamic variables of arterial BP in children may be explained by the increase in compliance of both arterial vasculature and the chest wall. In scoliosis cases, this can be further complicated by the effect of the prone position on chest wall compliance. The increased arterial vascular compliance in children may absorb and nullify the respiratory variations observed in left ventricular stroke volume, and thus hinder its detection by peripheral variables such as PPV.31,32 As a consequence of increased chest wall and lung compliance in children, it is hypothesized that SVV is reduced in response to changes in intrathoracic pressure during normal tidal volume.33

The plethysmographic waveform is, however, dependent on changes in blood volume—both arterial and venous—in the tissue under the pulse oximeter probe.34

The correlation between the arterial PPV and respiratory plethysmograph waveforms modulation (ΔPOP and pleth variability index) has been reported by different investigators as either being poor35–37 or strong38–42 in adults. It would seem to depend on the specific conditions under which the photoplethysmogram (PPG) waveforms were collected. Specifically, under controlled conditions with the patient being quietly ventilated under positive pressure, and under either general anesthesia or deep sedation, the system appeared to work well. With conditions closer to that of the “real world” (eg, during surgery or in the intensive care unit with the patient awake), the system tended to fail. In children, even under ideal conditions, the correlation between PPG and arterial waveform modulation seen in research studies has not been encouraging.43,44 It has been speculated that the high peripheral compliance in the pediatric arterial vascular system may account for the inability to use arterial modulation to guide fluid therapy.30

The aim of this study was to introduce newly derived frequency-analyzed PPG variables and to study the ability of these parameters to detect early bleeding (hemorrhaging) in children during spinal surgery. We postulate that PPG baseline modulation (PPG DC%) provides insight into the status of the venous vascular reserve.45

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METHODS

We received IRB approval for our study, but the requirement for parental consent was waived because of the nature of the study (observational with minimal risk). Twenty children undergoing spinal fusion for correction of idiopathic scoliosis were studied. A priori power analysis suggested that a sample size of 20 would achieve a power of 0.8 to detect a standardized effect size (ie, mean difference/SD) of 0.81 with a significance level of 0.01 using a 2-side Wilcoxon test, assuming that the actual distribution was uniform. The standardized effect size was chosen because no previous data were available for estimating magnitude or size of differences in PPG measurements between baseline and after bleeding.

In addition to standard American Society of Anesthesiologists monitoring, an invasive radial arterial catheter was placed in the radial artery of all the patients after induction of anesthesia and the initiation of mechanical ventilation. Spinal cord function was continuously monitored with neurophysiologic monitoring. Typically, anesthesia was induced with sevoflurane inhalation and then transitioned to total IV anesthetic technique using propofol and narcotics for maintenance.

Data were collected when the patients were in the prone position. Pediatric anesthesiologists were conscientious regarding body temperature; thus, there were 2 Bair Huggers® (Model 750 temperature management unit, 3M Health Care, St. Paul, MN) connected to the patient (one beneath and one above). The range of esophageal temperature was (36.0–37.5°C) with an average of 36.6°C. All waveforms, as well as airway pressure, were recorded at 100 Hz with a data acquisition system (Datex-Ohmeda S/5™ Collect, GE Healthcare Finland Oy, Helsinki, Finland). The decision to administer fluid (crystalloid or colloid) and to transfuse blood or plasma was at the discretion of the anesthesia care team and was not guided by respiratory variations of the arterial or the plethysmographic waveforms.

We used frequency analysis of PPG and arterial BP waveforms to explore blood volume changes during the bleeding phase (estimated blood loss approximately 9% of blood volume) of spinal fusion surgery.

Figure 1.

Figure 1.

Figure 2.

Figure 2.

Figure 3.

Figure 3.

Low- and high-pass filters (0.8 Hz) were applied to finger PPG (Nellcor/Covidien/Medtronic, Minneapolis, MN) and arterial BP waveforms (Figure 1). Finger PPG and arterial waveforms were analyzed using frequency domain analysis. Fast Fourier transform was used with the following parameters (spectrum, 8K [80 seconds] Hamming window, amplitude density (AD), 93.75% window overlap) over 3-minute windows using LabChart 7.37 (ADInstruments, Boulder, CO). Frequency domain analysis was used to isolate the modulation of the arterial BP and pulse oximeter waveforms at respiratory and cardiac pulse frequency. The respiratory frequency was defined as the same frequency as the airway pressure waveform, whereas cardiac pulse frequency was defined as the highest peak between 1 and 2.5 Hz. The strength of the waveform modulations was measured as AD (Appendix A). Figure 2 shows different waveform variables.

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PPG Variables

  1. PPG DC: corresponds to baseline modulation of the pulse oximeter waveform at the respiratory frequency. We believe that PPG DC is related to the movement of nonpulsatile venous blood underneath the PPG probe, as demonstrated in Figure 3. Phillips et al46 has demonstrated that the oxygen saturation of PPG baseline modulation of the nonpulsatile component (PPG DC) is consistent with venous blood saturation.
  2. PPG AC: corresponds to the pulse oximeter waveform amplitude modulation at the respiratory frequency. It corresponds to ΔPOP in the time domain analysis by other authors.14
  3. PPG cardiac pulse height: corresponds to PPG amplitude at the cardiac pulse frequency. This can be thought of as PPG height.
  4. PPG DC% and PPG AC%: this was used to normalize the PPG DC and AC values to allow comparison between patients. They are derived by dividing PPG DC and AC by PPG cardiac pulse height, respectively.

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Arterial Pressure Variables

  • 5. Arterial pressure DC: corresponds to mean arterial pressure (MAP) modulation at the respiratory frequency.
  • 6. Arterial pressure AC: corresponds to arterial pulse pressure (PP) modulation at the respiratory frequency. It corresponds to PP variability in the time domain analysis.
  • 7. Arterial cardiac pulse height: corresponds to AD of the arterial pressure waveform at the cardiac pulse frequency, and it represents average PP.
  • 8. Arterial pressure DC% and AC%: corresponds to normalize the arterial pressure of DC and AC values, respectively. It is derived by dividing arterial pressure DC and AC by arterial cardiac PP, respectively.
    • Arterial pressure DC% is the percent of MAP modulation
  • Arterial pressure AC% is the percent of PP modulation (PPV %)

Data were analyzed during 2 different phases: (1) baseline and (2) bleeding phase; after an average blood loss of 9% (with a range 5%–14% of blood volume). Figure 4 shows the representative data from 1 subject. Blood loss is estimated based on observed blood in the suction canister (accounting for irrigation used on the surgical field), in the surgical field, and on the surgical drapes. Hemodynamic data including heart rate, systolic blood pressure (SBP), diastolic blood pressure, MAP, and PP were also recorded.

Figure 4.

Figure 4.

Descriptive statistics were presented as median and interquartile range (IQR: first quartile to third quartile). Differences of hemodynamics, PPG, and arterial pressure variables between baseline and bleeding phase were compared using Wilcoxon signed-rank test. For each patient, the percentage changes of hemodynamics, PPG, and arterial pressure variables between bleeding and baseline phases were computed using the following equation: [(bleeding value − baseline value)/baseline value] × 100]. The Wilcoxon signed-rank test was then used to compare the percentage changes between these variables. All the data analyses were performed using the IBM SPSS Statistics version 21 (IBM Corp, Released 2012, IBM SPSS Statistics for Windows, Version 21.0, Armonk, NY). The Bonferroni correction method (ie, original P values multiplied by a constant of 20 [tests]) was used to adjust for multiple comparisons, and an adjusted P value <0.05 was considered statistically significant.

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RESULTS

Eighteen females and 2 males were studied. The average age was 13.7 years, and the average weight was 50 kg. As shown in the Table, among different hemodynamic parameters, only SBP showed significant increase during the bleeding phase with a median (IQR) percentage change of 11% (4 to 21) from baseline (P = 0.003; Figure 5). The PPG waveform parameters showed significant increases in PPG DC, PPG DC%, and PPG AC% during the bleeding phase with a median (IQR) percentage change of 158% (57 to 231), 359% (210 to 541), and 160% (87 to 251), with P = 0.002, 0.002, and 0.003, respectively (Figure 6). The PPG cardiac pulse height was significantly decreased during the bleeding phase (−46% [−57 to −35], P = 0.002). The PPG AC (PPG amplitude modulation) showed nonsignificant increase during bleeding phase (35% [−12 to 80], P = 0.58).

Table.

Table.

Figure 5.

Figure 5.

Figure 6.

Figure 6.

The arterial DC and DC% (MAP modulations) were increased during the bleeding phase with a median (IQR) percentage change of 31% (12 to 53) (P = 0.002) and 44% (19 to 84) (P = 0.012), respectively, whereas arterial AC and AC% (PP modulation) increased during bleeding phase at 28% (−20 to 64) and 41% (1 to 85), respectively, but did not reach the statistically significant level, with P values of 0.99 and 0.12, respectively (Table; Figure 6).

Among the PPG variables, percentage changes of PPG DC% was statistically significantly higher than that in PPG AC% (359% [210 to 541] vs 160% [87 to 251], P = 0.008) during bleeding phase. In comparison with the arterial variables, percentage changes of PPG DC% was significantly higher than those of both arterial DC% (359% [210 to 541] vs 44% [19 to 84], P = 0.002) and arterial AC% (359% [210 to 541] vs 41% [1 to 85], P = 0.003). Furthermore, there was a significantly higher increase in the PPG DC% when compared with SBP (359% [210 to 541] vs 11% [4 to 21], P = 0.002).

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DISCUSSION

Hypovolemia remains a common clinical challenge for children undergoing spinal surgery. Traditionally, fluid therapy in pediatric patients is guided by vital signs, central venous pressure, or other hemodynamic measurements, especially those from the arterial line. Unfortunately, these often do not change substantially until cardiovascular collapse is imminent. In our study, with episodes averaging a 9% blood loss, the vital signs were stable, whereas PPG DC, PPG DC%, and PPG AC% were changing significantly.

The findings of this study suggest that PPG DC% and AC% may be early indicators of hypovolemia with an average blood loss of 9% of blood volume in children undergoing spinal fusion surgery. We postulated that PPG DC% might be sensitive to changes in the preload venous reserve volume and, therefore, could potentially detect hypovolemia before other commonly available hemodynamic data. The magnitude of changes in PPG DC% exceeded other parameters, even those that are surrogates for respiratory variation in the left ventricular stroke volume (such as arterial pressure waveform variables [DC% and AC%] or pulse oximeter amplitude modulation – ΔPOP or PPG AC%). The limitations of arterial DC% and arterial AC% (as opposed to PPG DC%) might be explained by the high peripheral vascular compliance in the pediatric population. At the other extreme, the absence of respiratory modulation of the PPG DC (baseline modulation) might be seen to indicate adequate resuscitation. This could then alert the anesthesia provider to slow down fluid resuscitation and thereby help reduce the risk of volume overload. The sensitivity and specificity of this technique remain to be determined.

With frequency analysis of the PPG waveforms, the nonpulsatile component (PPG DC) is isolated at the respiratory frequency. This helps to ensure that PPG DC variability is related to respiration and is therefore less affected by random artifacts (such as electrocautery) and motion.

Another interesting observation is that arterial DC modulation (MAP modulation) may be a better indicator of blood volume changes in children undergoing spinal fusion surgery because it is less affected by the high peripheral vascular compliance in the pediatric patient than is the arterial AC modulation (PP modulation). In our study, the changes in arterial DC and arterial DC% reached a statistically significant level, during bleeding phase.

There are multiple limitations to this study. First is the lack of a gold standard by which one can identify whether the patient is hypovolemic. In this study, we are attempting to detect early changes in blood volume by using the PPG DC and PPG DC% modulations in pediatric patients and to gain experience with these variables before determining the specific threshold necessary for guiding fluid therapy. Second, PPG waveforms are noncalibrated waveforms. They show patient-to-patient variability and are prone to artifact with motion. To overcome these potential problems, we analyzed the data when there is little or no motion artifact and we used PPG DC% and arterial DC% values to enable normalization of these values, thereby allowing comparison between patients. Third, despite our interesting findings, we recognize that the commercially available pulse oximeter waveforms we used are heavily processed and filtered.47

We used this equipment in the hope of providing more readily available tools to the practicing clinician. We believe the use of frequency domain analysis is a useful tool to help overcome some of these inherent limitations.48 The next step of this study will be to assess the ability of PPG DC% and PPG AC% to predict fluid responsiveness in children, by using central hemodynamic changes measured by echocardiography in different clinical settings. Another logical extension of this study is to assess the effect of different pulse oximeter probe locations (ear versus finger versus nose), as well as to examine whether different methods of waveform processing can be used to track changes in total blood volume.

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APPENDIX A

Figure A1.

Figure A1.

Figure A2.

Figure A2.

Figure A3.

Figure A3.

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DISCLOSURES

Name: Aymen A. Alian, MD.

Contribution: This author helped design the study, conduct the study, collect the data, analyze the data, and prepare the manuscript.

Conflicts of Interest: None.

Name: Gourg Atteya, MD.

Contribution: This author helped conduct the study, collect the data, and analyze the data.

Conflicts of Interest: None.

Name: Dorothy Gaal, MD.

Contribution: This author helped conduct the study.

Conflicts of Interest: None.

Name: Thomas Golembeski, MD.

Contribution: This author helped conduct the study.

Conflicts of Interest: None.

Name: Brian G. Smith, MD.

Contribution: This author helped conduct the study.

Conflicts of Interest: None.

Name: Feng Dai, PhD.

Contribution: This author helped in statistical analysis and prepare the manuscript.

Conflicts of Interest: None.

Name: David G. Silverman, MD.

Contribution: This author helped design the study, analyze the data, and prepare the manuscript.

Conflicts of Interest: Patent applications have been submitted for some of the technology presented in this study. Yale University is the assignee.

Name: Kirk Shelley, MD, PhD.

Contribution: This author helped design the study, collect the data, analyze the data, and prepare the manuscript.

Conflicts of Interest: Patent applications have been submitted for some of the technology presented in this study. Yale University is the assignee.

This manuscript was handled by: Maxime Cannesson, MD, PhD.

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