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.
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.
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).
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).
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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