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Pulse photoplethysmographic amplitude and heart rate variability during laparoscopic cholecystectomy

A prospective observational study

Colombo, Riccardo; Raimondi, Ferdinando; Corona, Alberto; Marchi, Andrea; Borghi, Beatrice; Pellegrin, Simone; Bergomi, Paola; Fossali, Tommaso; Guzzetti, Stefano; Porta, Alberto

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European Journal of Anaesthesiology: August 2017 - Volume 34 - Issue 8 - p 526-533
doi: 10.1097/EJA.0000000000000660
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Abstract

Introduction

Despite using general anaesthesia, surgery is responsible for a stress response resulting from tissue damage, organ manipulation, pain, and heat loss, leading to an increase in sympathetic activity, catecholamine production and hormones associated with stress, together with associated changes in cardiovascular activity.1–4 Some authors have suggested that stress response attenuation may have beneficial effects on perioperative morbidity.5–7 Measuring the sympathetic response to surgery during general anaesthesia is a fascinating challenge for anaesthesiologists and can facilitate better titration of hypnosis and analgesia to blunt this reaction. Although the use of plasma catecholamine concentration assay to evaluate the stress response during laparoscopic or open surgical procedures has been widely investigated, there have been conflicting results.7–11 Its value appears to be only speculative and it is not expected to continue in routine use because it is expensive and does not provide real-time information. Two novel indices based on pulse photoplethysmographic amplitude analysis – the autonomic nervous system state (ANSS) and ANSS index (ANSSi) – have recently been suggested to be useful tools for noninvasively monitoring sympathetic activity.12,13 However, their ability to assess autonomic nervous system modulation in anaesthetised patients remains unproven because they have been validated only on awake healthy young volunteers.14

We conducted this study to investigate the magnitude of changes of both photoplethysmographic indices and their agreement with autonomic nervous system modulation in healthy patients undergoing laparoscopic surgery with general anaesthesia.

Methods

Ethical approval for this study (Comitato Etico Interaziendale Milano Area A, prot.n. 2015/ST/018) was provided by the Ethical Committee on 3 April 2015.

We conducted a single centre, prospective, observational study at Luigi Sacco Hospital in Milan, Italy. Adult patients between 18 and 50-year old, American Society of Anesthesiologists physical status classes I and II, scheduled for elective laparoscopic cholecystectomy under general anaesthesia were eligible for the study. Exclusion criteria were hypertension requiring pharmacological treatment, chronic cardiovascular disease, arrhythmias (atrial fibrillation, atrial flutter, or ectopic beats >5% of normal sinus beats), a history of alcohol or drug abuse, obesity (BMI >30 kg m−2), diabetes or other endocrine pathology (thyroid or adrenal), peripheral neuropathy or any kind of chronic neurological disease. The study adhered to the principles of the Declaration of Helsinki for medical research involving human subjects. All patients provided written informed consent before the surgical procedure. The trial was registered at ClinicalTrials.gov identifier: NCT02324478.

Study protocol

All patients were asked to fast for eight hours before surgery and no vagolytic drugs (atropine, scopolamine) or benzodiazepines were administered as premedication. Oxycodone 10 mg was given by mouth one hour before surgery. In the operating theatre patients were placed in a supine position and connected to a S/5 Advance Monitor (General Electric, Helsinki, Finland). Monitoring consisted of continuous ECG, noninvasive blood pressure (BP) every 3 min, pulse oximetry, respiratory gas analysis, end-tidal capnography (EtCO2), electroencephalographic response, and state entropy. A temperature sensor was applied to the thenar eminence of the left hand. All data were recorded continuously and exported to a laptop computer which had the S/5 Collect (General Electric, Helsinki, Finland) software installed. Three times were considered in the analysis: T0, baseline, before induction of general anaesthesia; T1, during mechanical ventilation 5 min after tracheal intubation; T2, 5 min after insufflation of pneumoperitoneum.

Induction and maintenance of general anaesthesia

General anaesthesia was induced with propofol and remifentanil via target-controlled infusion pumps (Asena Alaris; Cardinal Health, Basingstoke, UK). The pharmacokinetic models used were that of Schnider15 and Minto16 for propofol and remifentanil, respectively. The predicted effect-site concentration of propofol (CEprop) was initially set at 4 μg ml−1 and for remifentanil 4 ng ml−1. After loss of consciousness, oxygen was administered by facemask and muscle relaxation was induced using a bolus of cis-atracurium (0.2 mg kg−1). After tracheal intubation, the lungs were mechanically ventilated with the following settings: 15 breaths min−1 at a minute volume targeted to keep the EtCO2 between 35 and 38 mmHg. When the EtCO2 exceeded 38 mmHg the respiratory rate was increased. The tidal volume was increased up to 10 ml kg−1 and/or a maximum airway plateau pressure of 35 cmH2O. A respiratory rate less than 15 breaths min−1 was not allowed during the study period.

The CEprop was adjusted to maintain an EEG state entropy level between 40 and 60 (the minimum allowed CEprop was 3 μg ml−1). If needed, a rescue dose of cis-atracurium (0.08 mg kg−1) was given 40 min after the induction of general anaesthesia. The titration of the remifentanil infusion rate was based on clinical judgment, taking into account signs of inadequate analgesia [SBP above +20% from baseline, mean BP >100 mmHg, heart rate (HR) >90 bpm, coughing, tube chewing, grimacing, mydriasis, or tears] or excessive analgesia (HR <50 bpm, SBP below −20% from baseline, or mean BP <55 mmHg in the absence of active bleeding). A pneumoperitoneum was induced by CO2 insufflation through a surgically inserted trocar and maintained between 12 and 14 mmHg.

Atropine (0.01 mg kg−1 intravenously) was given for severe bradycardia (<45 bpm), whereas hypotension (SBP <90 mmHg or mean BP <50 mmHg) was treated by a fluid challenge of Ringer's acetate 500 ml, and by 5 mg intravenous ephedrine boluses if needed. Patients were excluded from the analysis if atropine or vasopressors were used, if blood loss exceeded 200 ml, if laparoscopy was converted to open surgery, or if cutaneous temperature dropped more than 1.0°C from baseline before the last data recording. State entropy and cutaneous temperature values represent the mean of values recorded every 15 s during the ECG acquisition. The HR was displayed as R to R mean interval of the ECG measured in ms.

Pulse photoplethysmographic analysis

Pulse plethysmographic amplitude was calculated for each beat of the pulse photoplethysmographic beat series corresponding to the analysed R to R series of 300 consecutive beats detected on the ECG (Fig. 1). Pulse to pulse interval was detected as the interval between consecutive pulse photoplethysmographic peaks. ANSS was calculated using 300 consecutive pulse beats and ANSSi was calculated from the same series using the equations:12,13

Fig. 1
Fig. 1:
Sample of recorded waves from a healthy subject. The upper wave is electrocardiographic, the lower is pulse photoplethysmographic. RR, heart period; PPGA, pulse plethysmographic amplitude; PPI, pulse-to-pulse interval on the photoplethysmographic wave.

where PPI is the pulse to pulse interval of the photoplethysmogram, PPGA is the pulse photoplethysmographic amplitude, and ANSSmax is the maximum ANSS for the subject during each study phase.

Autonomic nervous system activity assessment

Autonomic nervous system activity was measured noninvasively by the HR variability (HRV) analysis in the frequency domain.17,18 At each study time point, the ECG was sampled at 300 Hz for HRV analysis according to the recommendations of the European Society of Cardiology Task Force.19 HRV analysis was performed offline by a semiautomatic tachogram identifier (R–R interval identification made by a derivative/threshold method taken from the ECG).20 After detecting the QRS complex on the ECG and locating the R-apex using parabolic interpolation, the temporal distance between two consecutive R parabolic apices was computed and used as an approximation of the heart period. The tachogram was extracted from the recorded ECG and reviewed by an investigator to avoid erroneous detections or missed beats. If isolated ectopic beats occurred, these were removed and linearly interpolated using the closest values unaffected by ectopic beats.

The power spectrum was measured by an autoregressive model.21 Sequences of 300 consecutives heart beats were selected inside each experimental step. The mean and the variance of heart period are expressed in ms and ms2, respectively. Autoregressive spectral density was factorised into components each characterised by a central frequency. A spectral component was labelled as low frequency if its central frequency was between 0.04 and 0.15 Hz, and classified as high frequency if its central frequency was between 0.15 and 0.5 Hz.19 Spectral values were expressed in normalised units to rule out the effect of changes of total power spectrum densities on low frequency and high frequency components. Normalisation consisted in dividing the power of a spectral component by the total power minus the power below 0.04 Hz (very low frequency spectral component), and multiplying the ratio by 100. The high frequency power of heart beat series was used as a marker of the vagal modulation directed to the heart22 while the ratio of the low frequency power to the high frequency (low frequency/high frequency) was considered as an indicator of the balance between sympathetic and vagal modulation directed to the heart.21

Outcomes

The primary outcome was the measurement of the magnitude of changes of photoplethysmographic indices (ANSS and ANSSi) and autonomic modulation induced by general anaesthesia and pneumoperitoneum for laparoscopic cholecystectomy assessed by HRV analysis. A secondary outcome was the measurent of the agreement between photoplethysmographic and HRV-derived indices.

Statistical methodology

Sample size was calculated with G*Power for Windows (Universitat Kiel, Kiel, Germany). We needed to study 52 participants to detect a difference of 0.5 (with SD of 1) in the means of low frequency/high frequency at the pneumoperitoneum insufflation, with a power of 0.8, alpha error of 0.05 using one-way analysis of variance with two tail significance test.

Continuous variables are expressed as median (95% CI). Normal distribution was checked with D’Agostino-Pearson's test. We used Student's t test for independent samples to compare the means (Mann–Whitney U when not normally distributed) between groups of nonrepeated measures. HRV variables expressed in ms2 often have a logarithmic distribution among large samples, so we used a log10 to transform them into a normal distribution. Repeated measures were analysed with one way analysis of variance followed by Bonferroni's post hoc test for comparison between experimental phases.

Each data series was normalised to a common scale through a 0–1 normalisation, since the variables have different units of measure. Normalisation consisted of setting 0 as the lowest value and 1 as the highest value in each series: ANSSi, low frequency/high frequency, low frequency and power spectral heart rate density in the low frequency band expressed in normalized units (LFNU). All other values of the series were eventually and proportionally normalised using the equation:

where x was a value of the series, xmin was the lowest value, and xmax was the highest value of the series. Since PPGA, ANSS, high frequency and power spectral heart rate density in the high frequency band expressed in normalized units (HFNU) have an inverse relationship to the previous variables, their normalisation consisted of setting 0 as the highest value and 1 as the lowest one of each series.

We performed Bland-Altman analysis adjusted for a random effect model to estimate within study participant variance, in which each study participant has a different intercept over the observation period23 because data were recorded at three time points for all patients. The linear mixed model analysis was performed with R 3.0 with lme4 package. In the model the ‘subject’ (with interaction term with study phase) was entered as random effect, ‘difference from the mean’ for each study participants (without interaction term) and ‘study phase’ (baseline, induction or pneumoperitoneum) as fixed effects shown in the following:

variable. model = lmer (value ∼ phase + (1 + phase|id) + (1|diffmean), data = variable, REML = FALSE)

Where ‘variable’ was the studied variable (i.e. PPGA, low frequency, and so on), ‘value’ was the normalised value for each study participants at the corresponding ‘phase’ (baseline, after induction of general anaesthesia, after pneumoperitoneum insufflation), ‘id’ was the studied subject, and ‘diffmean’ was the difference between the value from the mean value from all subjects at that study phase. The agreements between HRV variables and pulse photoplethysmographic derived indices were assessed by Bland-Altman plots. Because the scatter of values for the differences decreases as the averages increase due to heteroscedasticity, we constructed V-shaped limits for the regression of differences on averages with Excel 2010 (Microsoft Corporation, Redmond, Washington, USA) as suggested by Ludbrook.24 Statistical analysis of other parametric and nonparametric data was carried out using Graphpad Prism 5 (GraphPad Software, Inc., La Jolla, California, USA). Two tailed P < 0.05 were considered statistically significant.

Results

Over the study period, 52 patients were recruited and their data used in the analysis. Patients’ characteristics are summarised in Table 1. The haemodynamic and autonomic nervous system variables are summarised in Table 2 and Fig. 2. The induction of general anaesthesia had a significant effect on cardiovascular variables, low and high frequency oscillatory components of HRV, and pulse photoplethysmographic indices. At the induction of pneumoperitoneum (T2) – corresponding to the maximal visceral stimulation for this kind of surgery – ANSS, ANSSi, and low frequency/high frequency changed significantly from T1, the latter probably because of both HFNU decrease and LFNU increase.

Table 1
Table 1:
Study participants characteristics
Table 2
Table 2:
Hemodynamic, heart rate variability and pulse photoplethysmographic variables during the study phases
Fig. 2
Fig. 2:
Changes of pulse photoplethysmographic and HRV-derived indices during the study phases. Whiskers represent 10–90th percentiles. For all displayed variables overall variability assessed with one-way analysis of variance during the study phases was significant (ANSS, ANSSi and HFNU P < 0.0001; LF/HF P = 0.0004). a P < 0.0001 between columns, b P = 0.008 between columns assessed with post hoc Bonferroni's test. ANSS, autonomic nervous system state; ANSSi, autonomic nervous system state index; bas, baseline before induction of general anaesthesia; HFNU, power spectral heart rate density in the high frequency band expressed in normalised units; LF/HF, ratio between the relative power spectral heart rate density in the low frequency and high frequency bands; ind, during general anaesthesia 5 min after beginning of mechanical ventilation; pne, 5 min after the insufflation of pneumoperitoneum.

After normalisation there was agreement between ANSSi and low frequency/high frequency (bias 16.1, 95%CI −1.4 to 33.5), LFNU (bias 10.2, 95%CI −13 to 33.4), and HFNU (bias 6.1, 95%CI −16.3 to 28.6). There was less agreement between ANSS and low frequency/high frequency (bias 17.7, 95%CI −6.94 to 42.35), LFNU (bias 11.8, 95%CI −17.3 to 41), and HFNU (bias 7.8, 95%CI −20.5 to 36.1; Fig. 3).

Fig. 3
Fig. 3:
Bland-Altman plot of agreement between couples of studied variables, corrected for within-subject variance with the linear mixed model. Thick lines represent the mean difference, thin lines represent 95% CI. ANSS, autonomic nervous system state; ANSSi, autonomic nervous system state index; HF, high frequency; LF, low frequency; LF/HF, ratio between the relative power spectral heart rate density in the low frequency and high frequency bands.

Discussion

The major finding of this study is that photoplethysmographic changes reflect those of HR variability indices in anaesthetised healthy humans. The induction of general anaesthesia increases ANSS and decreases ANSSi, decreases low frequency, high frequency, and total variance of the power spectrum, and decreases the low frequency spectral component more than the high frequency, leading to a lower low frequency/high frequency ratio. Furthermore, the insufflation of the pneumoperitoneum increases the low frequency/high frequency ratio and changes all pulse photoplethysmographic indices. Thus pulse photoplethysmographic-derived indices, especially ANSSi, seem to reflect the effect on the heart of changes in autonomic nervous system modulation during general anaesthesia for laparoscopic surgery.

The increase in sympathetic discharge resulting from the stress response to anaesthesia, surgery and unbalanced nociception has been claimed to influence postoperative outcome.6,7 Balancing both hypnosis and analgesia to maintain a low level of sympathetic activity during surgery might have an interesting influence on the perioperative course. Unfortunately, the direct measurement of autonomic nervous system modulation is not achievable in daily clinical practice. A widely used noninvasive measurement of autonomic nervous system modulation on the cardiovascular system is based on the analysis of the HRV.17–19,21–22 It has been demonstrated that the induction of general anaesthesia reduces total autonomic nervous system modulation resulting in very low HR variability.2,25 Despite deep hypnosis, surgical manipulation evokes beat to beat oscillatory patterns that can be analysed in stable conditions by means of HRV analysis.26,27

Using the Bland-Altman analysis, we found an agreement between HRV and the novel pulse photoplethysmographic-derived indices used to measure autonomic nervous system modulation, albeit not unaffected by biases. The bias between ANSSi and low frequency/high frequency seems large, and it might be unacceptable when measuring small oscillatory phenomena. Alternatively, the same results may be satisfactory when noninvasively measuring autonomic nervous system activity, especially the sympathetic system, as it exerts ‘mass stimulation’ on the body.4,28 The overall ratio of preganglionic fibres to postganglionic fibres is about 1 : 204 accounting for the concurrent stimulation of many organs and tissues in response to a sympathetic stimulus. Sympathetic mediated vasoconstriction reduces the pulsatile changes of tissue blood volume. This has usually been measured noninvasively using a plethysmographic finger cuff in a research setting under controlled stimulation of the sympathetic system such as a tilt test.14,29 The pulse photoplethysmographic probe does not measure the pulsatile change of tissue blood volume directly, as does the plethysmographic finger cuff, but it does estimate pulsatile variation through the medium of reflected infrared light analysis of the oxygen content of blood. This study endorses the hypothesis that the fluctuations in the autonomic nervous system – especially the sympathetic branch – during general anaesthesia can be detected by the analysis of the pulse photoplethysmographic wave sampled through a commercial photoplethysmographic probe, commonly used in a clinical setting for continuous pulse oximetry during surgical procedures.

ANSS and ANSSi can be easily computed and it is possible that in the future, software to generate these indices could be incorporated into anaesthesia monitors. The main advantage is that the algorithm for ANSS and ANSSi is readily available. Another PPGA-derived index, the surgical pleth index, has been shown to estimate autonomic modulation both in anaesthetised and awake healthy study participants.26,30 Unfortunately, the normalisation process used in the surgical pleth index algorithm is undisclosed and is the property of the manufacturer (GE, Helsinki, Finland) and only available for commercial use.

The study has some limitations. We studied ANSS and ANSSi in healthy patients and our findings cannot be extrapolated to unhealthy patients. The effect of monitoring ANSSi during general anaesthesia in moderate to high risk patients or during high-risk procedures should be further investigated.

It has been demonstrated that perioperative pulse photoplethysmographic amplitude is influenced by variables such as temperature, anaesthetic and vasoactive drugs, intravascular volume status, and nociceptive stimuli.12,31,32 Erroneously, it is assumed that sympathetic nervous system activity is simply another variable that cannot easily be isolated from the others. We believe that this has confounded the interpretation of the pulse photoplethysmography during anaesthesia. Pulse photoplethysmography indirectly measures the beat-to-beat changes as arteriolar blood volume flows into the tissue. The blood volume variations (ΔV) are related both to the systemic intravascular pulse pressure (ΔP) and to the distensibility of the vascular wall (D) in the equation: ΔVP.D.33 Distensibility is influenced by intravascular volume status and by sympathetic activity directed to the vessels. As a consequence, indices derived from the pulsatile photoplethysmographic amplitude – like ANSS and ANSSi – are affected by a wide variety of stimuli through the final pathway of the sympathetic fibres, leading to vessel constriction.2,3,34–36 From this point of view it is questionable whether the autonomic nervous system should be considered as a distinct entity from other surgical stimuli.

This study demonstrates the validity of novel pulse photoplethysmographic derived indices, especially ANSSi, in the estimation of changes in autonomic modulation during general anaesthesia for laparoscopic surgery. The indices could be easily incorporated into anaesthesia monitoring susytems as a surrogate of autonomic modulation changes during surgery.

Acknowledgements relating to this article

Assistance with the study: none.

Sponsorship and funding: From intramural departmental sources.

Conflicts of interest: none.

Presentation: none.

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