Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

The tidal volume challenge improves the reliability of dynamic preload indices during robot-assisted laparoscopic surgery in the Trendelenburg position with lung-protective ventilation

BMC Anesthesiology volume 19, Article number: 142 (2019) Cite this article

Abstract

Background

The reliability of pulse pressure variation (PPV) and stroke volume variation (SVV) is controversial under pneumoperitoneum. In addition, the usefulness of these indices is being called into question with the increasing adoption of lung-protective ventilation using low tidal volume (VT) in surgical patients. A recent study indicated that changes in PPV or SVV obtained by transiently increasing VT (VT challenge) accurately predicted fluid responsiveness even in critically ill patients receiving low VT. We evaluated whether the changes in PPV and SVV induced by a VT challenge predicted fluid responsiveness during pneumoperitoneum.

Methods

We performed an interventional prospective study in patients undergoing robot-assisted laparoscopic surgery in the Trendelenburg position under lung-protective ventilation. PPV, SVV, and the stroke volume index (SVI) were measured at a VT of 6 mL/kg and 3 min after increasing the VT to 8 mL/kg. The VT was reduced to 6 mL/kg, and measurements were performed before and 5 min after volume expansion (infusing 6% hydroxyethyl starch 6 ml/kg over 10 min). Fluid responsiveness was defined as ≥15% increase in the SVI.

Results

Twenty-four of the 38 patients enrolled in the study were responders. In the receiver operating characteristic curve analysis, an increase in PPV > 1% after the VT challenge showed excellent predictive capability for fluid responsiveness, with an area under the curve (AUC) of 0.95 [95% confidence interval (CI), 0.83–0.99, P < 0.0001; sensitivity 92%, specificity 86%]. An increase in SVV > 2% after the VT challenge predicted fluid responsiveness, but showed only fair predictive capability, with an AUC of 0.76 (95% CI, 0.60–0.89, P < 0.0006; sensitivity 46%, specificity 100%). The augmented values of PPV and SVV following VT challenge also showed the improved predictability of fluid responsiveness compared to PPV and SVV values (as measured by VT) of 6 ml/kg.

Conclusions

The change in PPV following the VT challenge has excellent reliability in predicting fluid responsiveness in our surgical population. The change in SVV and augmented values of PPV and SVV following this test are also reliable.

Trial registration

This trial was registered with Clinicaltrials.gov, NCT03467711, 10th March 2018.

Peer Review reports

Background

Robot-assisted laparoscopic surgery is increasingly performed due to its many advantages over open surgery, including minimal tissue trauma, fewer surgical complications, and earlier postoperative recovery [1]. However, pneumoperitoneum is essential for adequate exposure in robot-assisted laparoscopic surgery, which has been associated with increased morbidity such as reduced renal blood flow (RBF) and post-operative renal dysfunction [2, 3]. As the level of hydration required to maintain RBF under pneumoperitoneum depends on the baseline volume status [4], an adequate assessment of intravascular volume and optimal fluid management are important [5]. However, predicting the fluid responsiveness with clinical signs, such as blood pressure, or urine output or cardiac filling pressure is more difficult during pneumoperitoneum [6, 7].

Dynamic preload indices such as pulse pressure variation (PPV) and stroke volume variation (SVV), are generally accepted as accurate indicators of fluid responsiveness during surgery and in the intensive care unit (ICU) in patients ventilated with tidal volume (VT) of at least 8 ml/kg or more of predicted body weight (PBW) [8,9,10]. As dynamic preload indices are generated by cyclic transmission of airway pressure to the pleural and pericardial spaces under positive ventilation, their reliability can be affected by extra-thoracic modification in compliance such as pneumoperitoneum. Several studies have highlighted the effect of intra-abdominal pressure (IAP) on the accuracy and cut-off values of these indices [11,12,13,14,15]. In addition, as the application of lung-protective ventilation using low VT with positive end expiratory pressure (PEEP) is gradually increasing in surgical patients [16,17,18], the usefulness of these indices during robot-assisted laparoscopic surgery has been questioned.

Recently, Myatra et al. developed a functional test called the “VT challenge”, which includes transiently increasing VT from 6 ml/kg to 8 ml/kg PBW and observing the changes in PPV and SVV [19]. They demonstrated that the absolute changes of PPV and SVV values induced by VT challenge, as well as their augmented values, predicted fluid responsiveness with high sensitivity and specificity, even in critically ill patients receiving low VT [19]. This test also validated in predicting fluid responsiveness in surgical patients receiving low VT [20].

Therefore, the aim of the current study was to investigate the ability of absolute changes in PPV and SVV values induced by VT challenge to predict fluid responsiveness in patients undergoing robot-assisted laparoscopic surgery in the Trendelenburg position under lung-protective ventilation. We also assessed the whether augmented values of PPV and SVV following this test are also reliable to predict fluid responsiveness.

Methods

Study design and patient population

This interventional prospective study was approved by the institutional review board of Hallym University Kangnam Sacred Heart Hospital (approval number: 2017–09-003). From March to June 2018, adult patients undergoing robot-assisted laparoscopic surgery with pneumoperitoneum in the Trendelenburg position were enrolled after obtaining their written informed consent. The trial was registered prior to patient enrollment at ClinicalTrials.gov (NCT03467711). Exclusion criteria were body mass index (BMI) > 30 or < 15 kg/m2, preoperative arrhythmia, moderate to severe valvular heart disease, preoperative left ventricular ejection fraction < 40%, right ventricular dysfunction, intracardiac shunt, 1-s forced expiratory volume < 60% of predicted value, moderate to severe renal or liver disease, new-onset arrhythmia after anesthesia induction, and contraindications for oesophageal Doppler monitor (ODM) probe insertion (i.e., oesophageal stent, carcinoma of the esophagus or pharynx, previous oesophageal surgery, oesophageal stricture, oesophageal varices, pharyngeal pouch, and severe coagulopathy). During surgery, all patients were placed in the 25° Trendelenburg position, and pneumoperitoneum was achieved by continuous carbon dioxide insufflation maintaining an IAP of 15 mmHg.

Anesthetic technique

After the patients arrived at the operating room, pulse oximetry, three-lead electrocardiography (ECG), and non-invasive arterial pressure monitoring were applied. Anesthesia was induced with propofol (1.5–2.5 mg/kg) and remifentanil (0.05–0.15 μg/kg/min), and tracheal intubation was facilitated with rocuronium (0.8 mg/kg). The patient’s lungs were mechanically ventilated with a mixture of oxygen in air, with an inspired oxygen fraction of 0.5 using the volume-controlled mode. VT was adjusted to 6 ml/kg PBW (determined as x + 0.91[height (in cm) − 152.4], where x = 50 for males and x = 45.5 for females) [21]. The PEEP of 5 cm H2O was applied without inspiratory pause. Respiratory rate (RR) was adjusted to maintain an end-tidal carbon dioxide tension between 35 and 40 mmHg. The inspiratory to expiratory ratio was set to 1:2. Peak inspiratory airway pressure (PIP) and compliance of the respiratory system (Crs) were recorded from the anesthesia machine (Datex-Ohmeda Avance CS2 Anesthesia Machine; GE Healthcare, Helsinki, Finland). Anesthesia was maintained with continuous infusion of remifentanil (0.02–0.2 μg/kg/min) and sevoflurane (1.5–2.5 vol%) to maintain a bispectral index between 40 and 60.

Hemodynamic monitoring

After induction of anesthesia, a radial arterial catheter and ODM probe (CardioQ; Deltex Medical, Chichester, UK) were inserted. Both were connected to the CardioQ-ODM+ (Deltex Medical Ltd.) monitor.

The ODM probe was positioned to obtain the optimum signal for descending aorta blood velocity. Stroke volume index (SVI), and peak velocity (PV) were measured continuously and displayed, and their mean values were calculated over 10 s.

After zeroing the arterial transducer, a flush test was performed to ensure that the arterial pressure measurement system was critically damped. The arterial pulse pressure wave was simultaneously monitored through the patient monitor (CARESCAPE Monitor B850; GE Healthcare) and CardioQ-ODM+ monitor using a serial cable. The patient monitor displayed the automatically calculated PPV in real time using the algorithms described previously [22].

$$ \mathrm{PPV}\ \left(\%\right)=\left[\left({\mathrm{PP}}_{\mathrm{max}}-{\mathrm{PP}}_{\mathrm{min}}\right)/{\mathrm{PP}}_{\mathrm{mean}}\right]\times 100 $$

where PPmax and PPmin represent the maximum and minimum arterial pulse pressure (PP), and PPmean is the mean arterial PP.

The CardioQ-ODM+ monitor combines ODM with pulse pressure wave analysis to measure SVI. It uses ODM-derived SVI for initial and periodic calibrations, and then continuously calculates pulse pressure wave analysis-derived SVI using the Liljestrand–Zander formula [23]. By continuous beat detection and analysis, the SV, SVI, and SVV were displayed continuously in a separate pressure-based data window as a column of values. SVV were obtained as described previously, regardless of the respiratory cycle [24].

$$ \mathrm{SVV}\ \left(\%\right)=\left[\left({\mathrm{SV}}_{\mathrm{max}}-{\mathrm{SV}}_{\mathrm{min}}\right)/{\mathrm{SV}}_{\mathrm{mean}}\right]\times 100 $$

where SVmin and SVmax are the minimum and maximum SV values over one respiratory cycle, respectively.

All values were averages of at least three consecutive measurements acquired over 30 s. An independent investigator who was trained in maneuvering the ODM probe but was not involved in the present study assessed ODM and all other variables during the study. ODM is routinely used to monitor surgical patients in our center and shows good inter-observer reliability [14].

Study protocol

Figure 1 shows a schematic representation of the protocol, which was initiated at least 1 h after increasing IAP to 15 mmHg, and after stabilization of hemodynamic parameters, defined as changes in mean arterial pressure (MAP) < 10% during 5 min. In addition, to minimize acute changes in IAP and sympathetic tone due to ongoing surgery [25, 26], which could confound the effects of fluid challenge, the study protocol was performed with little or no surgical stimulation (absence of cautery and instrumentation of intra-abdominal structures).

Fig. 1
figure 1

Study protocol. Arrows indicate time points at which measurements were made. PnP, pneumoperitoneum; VT, tidal volume; PBW, predicted body weight

Full size image

We first measured the hemodynamic response to VT challenge, and then performed volume expansion (VE) to assess the subsequent changes in SVI. The first set of measurements, including (HR), MAP, SVI, PV, PIP, Crs, PPV with 6 ml/kg PBW VT ventilation (PPV6), and SVV with 6 ml/kg PBW VT ventilation (SVV6), were recorded at baseline (T1, base 1). After the baseline measurement, VT was increased from 6 to 8 ml/kg PBW for 3 min without changing the RR. During the last minute of high VT ventilation, measurements of the above-mentioned hemodynamic and respiratory variables, including PPV with 8 ml/kg PBW VT ventilation (PPV8) and SVV with 8 ml/kg PBW VT ventilation (SVV8), were again recorded (T2). The changes in PPV and SVV values induced by VT challenge (ΔPPV6–8 and ΔSVV6–8) were calculated as follows:

$$ {\Delta \mathrm{PPV}}_{6-8}={\mathrm{PPV}}_8-{\mathrm{PPV}}_{6,} $$
$$ {\Delta \mathrm{SVV}}_{6-8}={\mathrm{SVV}}_8-{\mathrm{SVV}}_{6.} $$

The percentage change in PPV and SVV values induced by VT challenge (%ΔPPV6–8 and %ΔSVV6–8) also calculated.

After the VT was returned to 6 ml/kg PBW and all of the hemodynamic variables had returned to baseline values (variations < 10%), VE was performed for 10 min using an infusion of 6% hydroxyethyl starch (HES 130/0.4, Volulyte; Fresenius Kabi, Stans, Switzerland) 6 ml/kg PBW. Two sets of measurements (HR, MAP, SVI, PV, PIP, Crs, PPV, and SVV) were performed before (T3, base 2) and 5 min after VE (T4) [27, 28]. Percentage differences in ODM-derived SVIs before and after VE were used as principal indicators of fluid responsiveness. Patients were classified as responders to VE if they showed an increase in SVI ≥ 15% and as non-responders if they showed an increase < 15% [29, 30]. The changes in SVV and PPV values after VE (ΔPPVVE and ΔSVVVE) were calculated as follows:

$$ {\Delta \mathrm{PPV}}_{\mathrm{VE}}=\mathrm{PPV}\ \left(\mathrm{T}4\right)-\mathrm{PPV}\ \left(\mathrm{base}\ 2,\mathrm{T}3\right), $$
$$ {\Delta \mathrm{SVV}}_{\mathrm{VE}}=\mathrm{SVV}\ \left(\mathrm{T}4\right)-\mathrm{SVV}\ \left(\mathrm{base}\ 2,\mathrm{T}3\right). $$

Statistical analysis

MedCalc for Windows (ver. 15.6.1; MedCalc Software, Ostend, Belgium) was used to calculate sample size. The sample size was determined using the difference between the area under the curve (AUC) of 0.75 (alternative hypothesis that PPV8 can predict fluid responsiveness after VE) and 0.5 (null hypothesis). At least 38 patients were required to detect an AUC difference of 0.25 with a type I error of 0.05 and a desired power of 0.80, assuming equal numbers of responders and non-responders. With the expectation of a 10% dropout rate, 42 patients were enrolled in the study.

The normality of the continuous data was tested with the Shapiro–Wilk test. Data are presented as the mean (SD), median [interquartile range (IQR)], or number of patients (%).Student’s t-test or the Mann–Whitney U test for continuous variables, and the chi-square test for categorical data, were used to compare patient characteristics between responders and non-responders. The hemodynamic parameters were compared between responders and non-responders using the Mann–Whitney U-test or t-test, as appropriate. The effects of the temporary increase in VT from 6 to 8 ml/kg and VE on hemodynamic parameters were assessed using the paired t-test or the Wilcoxon signed-rank sum test after the normality test. A Bonferroni-adjusted P-value (normal P-value multiplied by the number of outcomes being tested) was used to control for multiple comparisons.

The relations between percentage changes in SVI after VE and hemodynamic parameters before VE (PPV6, PPV8, ΔPPV6–8, SVV6, SVV8, and ΔSVV6–8) were assessed using Spearman’s rank correlation. The relationship between the percentage changes in SVI after and the changes in PPV and SVV after VE (ΔPPVVE and ΔSVVVE) were also assessed using Spearman’s rank correlation analysis. The intraclass correlation between the SVI, PPV, and SVV measurements at the two baseline steps (T1 and T3) was measured using random-effects models [31].

To test the abilities of dynamic preload indices to predict fluid responsiveness, the AUCs of receiver operating characteristic (ROC) curves were calculated and compared using the DeLong method. Briefly, the general interpretations of a test according to the value of the AUC of the ROC were as follows: AUC = 0.5, no better than chance, a useless test with no prediction possible; AUC = 0.6–0.69, a test with a poor predictive capability; AUC = 0.7–0.79, a fair test; AUC = 0.8–0.89, a test with good predictive capability; AUC = 0.9–0.99, an excellent test; AUC = 1.0, a perfect test with the best possible prediction. An optimal threshold value was determined for each variable to maximize the Youden index (sensitivity + specificity – 1). Considering the possibility of an overlap between responders and non-responders, we determined a grey zone for dynamic preload indices, considering a low cut-off value including 90% of negative fluid challenge responses, and a high cut-off value predicting positive fluid challenge in 90% of cases [32].

Statistical analyses were performed using MedCalc (ver. 15.6.1) and SPSS software (ver. 24.0; IBM Corp., Armonk, NY, USA) and R package (version 3.4.2; https://www.r-project.org/). In all analyses, P < 0.05 was taken to indicate statistical significance.

Results

Patient characteristics

Of the 49 patients included in the initial screen, 42 fulfilled the inclusion criteria and were enrolled in the study. Four patients were excluded; one developed intraoperative subcutaneous emphysema and required a ventilator mode change, one had severe hypotension during VE and required vasopressor support, one developed paroxysmal atrial fibrillation during surgery, and the remaining patient’s arterial pressure measurement system was critically damped. Among the 38 patients included in the final analysis, 24 patients (63%) were responders and 14 (37%) were non-responders (Fig. 2). There were no significant differences in age, PBW, or BMI between responders and non-responders, whereas the surgery type and sex distribution differed between the two groups (Table 1). The intraclass correlation between the SVI, PPV, and SVV measurements at the two baseline steps (T1 and T3) were 0.98 [95% confidence interval (CI), 0.96–0.99], 0.96 (95% CI, 0.92–0.98), and 0.81 (95% CI, 0.64–0.90), respectively.

Fig. 2
figure 2

Study diagram

Full size image
Table 1 Patient Characteristics
Full size table

Effects of VT challenge and VE on hemodynamic and respiratory variables

At baseline, with 6 ml/kg PBW VT ventilation, no significant differences were found in any hemodynamic variables, including PPV6 and SVV6, between responders and non-responders. After increasing VT to 8 ml/kg PBW, MAP decreased and PPV and SVV increased significantly only in responders, resulting in significant differences in MAP, PPV (PPV8), and SVV (SVV8) between responders and non-responders. Baseline PIP and Crs were comparable between the two groups and increased significantly after VT challenge in responders and non-responders (Table 2).

Table 2 Hemodynamic and Respiratory Variables at Baseline, after the Tidal Volume Challenge, before Volume Expansion, and after Volume Expansion in Responders (n = 24) and Non-responders (n = 14)
Full size table

Significant changes in PIP, Crs, SVI, and PPV were induced in responders and non-responders after VE, while significant decreases in HR and SVV were induced only in responders (Table 2).

Relationships between changes in PPV and SVV induced by VE and percentage changes in SVI induced by VE

PPVVE and ΔSVVVE were significantly correlated with VE-induced percentage changes in SVI (r = − 0.61 [95% CI − 0.78 to − 0.36], P < 0.001; r = − 0.44 [95% CI − 0.67 to − 0.14], P = 0.006, respectively) (Fig. 3), indicating the ability of these variables to track changes in SVI induced by VE during pneumoperitoneum.

Fig. 3
figure 3

Relationship between volume expansion-induced changes in pulse pressure variation and stroke volume variation and volume expansion-induced percentage changes in the stroke volume index (SVI). ΔPPVVE, changes in pulse pressure variation after volume expansion; ΔSVVVE, changes in stroke volume variation after volume expansion

Full size image

Prediction of fluid responsiveness

In the ROC curve analysis, ΔPPV6–8 showed excellent predictive capability for fluid responsiveness with an AUC of 0.95 (95% CI 0.83–0.99, P < 0.0001). ΔPPV6–8 > 1% identified responders with sensitivity of 92% (95% CI 73–99) and specificity of 86% (95% CI 57–98). ΔSVV6–8 could also predict fluid responsiveness but showed only fair predictive capability, with an AUC of 0.76 (0.60–0.89). The ΔSVV6–8 > 2% identified responders with a sensitivity 46% (95% CI 26–67) and specificity of 100% (95% CI 77–100) (Table 3).

PPV6 showed poor ability to predict fluid responsiveness, with an AUC of 0.69 (95% CI 0.52–0.83, P = 0.036), but PPV8 showed good ability, with an AUC of 0.85 (95% CI 0.70–0.95, P < 0.0001). SVV6 was not predictive of fluid responsiveness, but SVV8 showed fair predictive ability, with an AUC of 0.77 (95% CI 0.61–0.89). The optimal threshold values of PPV8 and SVV8 were 7% [sensitivity 79% (95% CI 58–93); specificity 79% (95% CI 49–95)] and 5% [sensitivity 67% (95% CI 45–84); specificity 79% (95% CI 49–95)], respectively (Table 3). The AUCs for ΔPPV6–8 and PPV8 were significantly greater than those for PPV6 (P < 0.001 and P = 0.003, respectively). The AUCs for SVV8 were significantly greater than those for SVV6 (P = 0.007) but there were no significant differences in the AUCs for ΔSVV6–8 and SVV6 (P = 0.11). No significant differences were found between the AUCs for ΔPPV6–8 and PPV8 (P = 0.09) or between those for SVV6–8 and SVV8 (P = 0.85) (Fig. 4).

Table 3 Prediction of Fluid Responsiveness based on the ROC Curves of Various Indices
Full size table
Fig. 4
figure 4

Comparison of receive -operating characteristic curves of PPV6, PPV8, ΔPPV6–8, SVV6, SVV8 and ΔSVV6–8 to predict fluid responsiveness during robot-assisted laparoscopic surgery in the Trendelenburg position under lung-protective ventilation. PPV6, pulse pressure variation during tidal volume at 6 ml/kg predicted body weight (PBW); PPV8, pulse pressure variation during tidal volume at 8 ml/kg PBW; ΔPPV6–8, change in value of pulse pressure variation after tidal volume challenge; SVV6, stroke volume variation during tidal volume at 6 ml/kg predicted body weight (PBW); SVV8, stroke volume variation during tidal volume at 8 ml/kg PBW; ΔSVV6–8, change in value of stroke volume variation after tidal volume challenge; area under the ROC curve appears in cartouche with 95% confidence interval

Full size image

Discussion

In this study on patients undergoing robot-assisted laparoscopic surgery in the Trendelenburg position under lung-protective ventilation, we demonstrated that absolute change in PPV values obtained by VT challenge (ΔPPV6–8) can predict fluid responsiveness with excellent predictive capability, whereas ΔSVV6–8 showed only fair predictive capability. The optimal thresholds of ΔPPV6–8 and ΔSVV6–8 were > 1% and > 2%, respectively. This study also showed that the augmentation of PPV and SVV following VT challenge improved the predictability of fluid responsiveness compared to PPV and SVV measured with a VT of 6 ml/kg.

Our study demonstrated that a VT challenge improved the predictive power of PPV and SVV values, even in patients with elevated IAP. Because PPV and SVV values are augmented in fluid responders, but not in non-responders, the absolute changes in the PPV and SVV values induced by increased VT could also be used to predict fluid responsiveness, which is consistent with studies of Myatra et al. [19] and Messina et al. [20].

However, the cut-off values of ΔPPV6–8 and ΔSVV6–8 in our study and in the study of Messina et al. [20] were lower than those reported by Myatra et al. [19]. There are several possible explanations for this discrepancy. The extent of airway pressure transmission to the pleural space is inversely proportional to chest wall compliace (Ccw) and proportional to lung compliance (CL) [33, 34]. Messina et al. described that their cut-off values was lower because Myatra et al. enrolled critically ill patients in the ICU with reduced Ccw reported at 30%, while they enrolled healthy surgical patients [20, 35]. As pneumoperitoneum has been known to decreases Ccw [36], higher cut-off values could be expected in our study population. However, adding a steep Trendelenburg position to pneumoperitoneum reduces Ccw as well as CL [37], which may have caused the cut off value lower than expected. In addition to the aforementioned factors, different hemodynamic characteristics of the patients may contribute to different cut-off values; Myatra et al. studied critically ill patients with acute circulatory failure, whereas our study population consisted of patients with no clinical signs of shock. That most our study population were in the ‘grey zone’ where responders and non-responders are not well separated [32], would also have contributed to the less profound changes in PPV and SVV values.

Unlike Myatra et al. [19] and Messina et al. [20]., the absolute change in SVV after VT challenge showed only fair ability to predict true fluid responders in our study. This discrepancy may have been attributable to the fact that pneumoperitoneum can induce significant increases in systemic vascular resistance (SVR) [38]. The trending ability of pressure waveform devices to accurately monitor changes in SVI is influenced by SVR [39, 40]. Renner et al. reported that increasing IAP to 25 mmHg abolishes the ability of SVV to predict fluid responsiveness, but not in the case of PPV [12]. This supports our finding that PPV was better able to predict fluid responsiveness than SVV with all of the tested VT values at 6 and 8 ml/kg.

Our study showed discordant results versus those of previous studies regarding the reliability of dynamic preload indices in predicting fluid responsiveness, as well as the appropriate cut-off value [11,12,13,14,15]. These conflicting results can be explained by the fact that the cardiopulmonary interactions that are altered during pneumoperitoneum depend on the level of IAP [41]. Although IAP elevations > 20 mmHg are known to progressively increase the values of dynamic preload indices independent of volume status [12, 13], the lower IAP (10–15 mmHg) in a surgical setting did not modify the cut-off values of dynamic preload indices in the present or previous studies [11, 14, 15]. Unlike the results of Hoiseth et al. [14], which showed relatively poor predictive capability of dynamic preload indices for fluid responsiveness during laparoscopy, we found good predictive capability of PPV at VT 8 ml/kg. This discrepancy can be explained by those researchers not controlling clinical factors, such as blood loss, use of vasopressors, or changes in ventilator settings, which may have altered PPV values independent of the preload condition, whereas we performed VE under controlled clinical conditions with little or no surgical stimulation.

Due to the complex relationship between intrathoracic pressure and IAP, as well as the effect of pressure transmitted to pleura space [42, 43], during pneumoperitoneum, the detection of hypovolemia is particularly difficult. In addition, protective ventilation strategies [18], specifically small VT, have been shown to modify the reliability of dynamic preload indices [44]. However, this study showed that the absolute change in PPV after a “VT challenge” reliably predicted fluid responsiveness even under the above limited conditions. As suggested by Myatra et al. [19], the VT challenge can be applied in resource-limited settings as this test does not require a continuous cardiac output monitor. Because ΔPPVVE were significantly correlated with percentage changes in SVI induced by VE (Fig. 3), fluid responsiveness after VE also can be confirmed without continuous cardiac output monitoring.

Nevertheless, 24% of patients were included in the grey zone analysis of ΔPPV6–8, suggesting caution in using of the single cutoff (ΔPPV6–8 > 1%) as the sole target in hemodynamic management. Furthermore, hemodynamic resuscitation should be aimed at achieving not only adequate SV but also sufficient MAP for the body to maintain tissue oxygenation [45]. Because the arterial pressure response VE depends on arterial tone, knowing whether a patient is preload-dependent provides only a partial solution to the problem [46]. In this regard, functional assessment of arterial load by dynamic arterial elastance (Eadyn), defined as the ratio between PPV and SVV has recently been shown to predict the arterial pressure response to VE in hypotensive, preload-dependent patients [47]. Further studies are needed to determine whether this parameter is effective in pneumoperitoneum, using time-synced data from patient monitor and cardiac output monitor [48].

This study had several limitations. First, the study population consisted of only a small number of highly selected patients receiving robot-assisted laparoscopic surgery in the Trendelenburg position. As the IAP was maintained at 15 mmHg, our results cannot be extrapolated to different IAP values. Our results require validation in a larger and more heterogeneous population. Second, we performed the VT challenge for 3 min, not 1 min as described previously [19]. We used an extended time because we have assumed that extending the time for VT challenge may have sensitized the test and possibly led to more observations of increases in PPV and SVV. However, during extended time, some equilibrium or compensatory effects could be occurred, which may be an important reason to explain the difference in our cut off values from the study of Myatra et al. [19]. Third, there were sex differences between the responders and non-responders, which may have been due to differences in types of surgery based on sex, resulting in different hemodynamic status at the start of the study protocol. However, we considered it unlikely that this difference would have affected our primary outcome in predicting the fluid response during robot-assisted laparoscopic surgery in the Trendelenburg position because the IAP and angle of Trendelenburg position during surgery were constant in all patients.

Conclusions

In conclusion, the change in PPV following the VT challenge test has excellent reliability in predicting fluid responsiveness in patients undergoing robot-assisted laparoscopic surgery in the Trendelenburg position under lung-protective ventilation. The change in SVV and absolute values of PPV and SVV following this test are also reliable.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due to the regulation of Institutional Review Board, but are available from the corresponding author after getting permission from IRB for sharing the dataset on reasonable request.

Abbreviations

AUC:

Area under the curve

BMI:

Body mass index

CI:

Confidence interval

Crs:

Compliance of the respiratory system

ECG:

Electrocardiography

HR:

Heart rate

IAP:

Intra-abdominal pressure

MAP:

Mean arterial pressure

ODM:

Oesophageal Doppler monitor

PBW:

Predicted body weight

PEEP:

Positive end expiratory pressure

PIP:

Peak inspiratory airway pressure Pulse pressure variation

PP:

Pulse pressure

PV:

Peak velocity

RBF:

Renal blood flow

SVI:

Stroke volume index

SVV:

Stroke volume variation

VE:

Volume expansion

VT :

Tidal volume

References

  1. Novara G, Ficarra V, Rosen RC, Artibani W, Costello A, Eastham JA, Graefen M, Guazzoni G, Shariat SF, Stolzenburg JU, et al. Systematic review and meta-analysis of perioperative outcomes and complications after robot-assisted radical prostatectomy. Eur Urol. 2012;62(3):431–52.

    Article  PubMed  Google Scholar 

  2. Khoury W, Jakowlev K, Fein A, Orenstein H, Nakache R, Weinbroum AA. Renal apoptosis following carbon dioxide pneumoperitoneum in a rat model. J Urol. 2008;180(4):1554–8.

    Article  PubMed  Google Scholar 

  3. Wiesenthal JD, Fazio LM, Perks AE, Blew BD, Mazer D, Hare G, Honey RJ, Pace KT. Effect of pneumoperitoneum on renal tissue oxygenation and blood flow in a rat model. Urology. 2011;77(6):1508 e1509–15.

    Article  Google Scholar 

  4. Bergman S, Nutting A, Feldman LS, Vassiliou MC, Andrew CG, Demyttenaere S, Woo D, Carli F, Jutras L, Buthieu J, et al. Elucidating the relationship between cardiac preload and renal perfusion under pneumoperitoneum. Surg Endosc. 2006;20(5):794–800.

    Article  PubMed  Google Scholar 

  5. Demyttenaere SV, Taqi A, Polyhronopoulos GN, Bergman S, Stanbridge DD, Unikowsky B, Carli F, Fried GM, Feldman LS. Targeting individual hemodynamics to maintain renal perfusion during pneumoperitoneum in a porcine model. Surgery. 2007;142(3):350–6.

    Article  PubMed  Google Scholar 

  6. Sharma KC, Brandstetter RD, Brensilver JM, Jung LD. Cardiopulmonary physiology and pathophysiology as a consequence of laparoscopic surgery. Chest. 1996;110(3):810–5.

    Article  CAS  PubMed  Google Scholar 

  7. Lestar M, Gunnarsson L, Lagerstrand L, Wiklund P, Odeberg-Wernerman S. Hemodynamic perturbations during robot-assisted laparoscopic radical prostatectomy in 45 degrees Trendelenburg position. Anesth Analg. 2011;113(5):1069–75.

    Article  PubMed  Google Scholar 

  8. Marik PE, Cavallazzi R, Vasu T, Hirani A. Dynamic changes in arterial waveform derived variables and fluid responsiveness in mechanically ventilated patients: a systematic review of the literature. Crit Care Med. 2009;37(9):2642–7.

    Article  PubMed  Google Scholar 

  9. Hong JQ, He HF, Chen ZY, Du ZS, Liu WF, Weng PQ, Huang HB. Comparison of stroke volume variation with pulse pressure variation as a diagnostic indicator of fluid responsiveness in mechanically ventilated critically ill patients. Saudi Med J. 2014;35(3):261–8.

    PubMed  Google Scholar 

  10. Yang X, Du B. Does pulse pressure variation predict fluid responsiveness in critically ill patients? A systematic review and meta-analysis. Crit Care. 2014;18(6):650.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bliacheriene F, Machado SB, Fonseca EB, Otsuke D, Auler JO Jr, Michard F. Pulse pressure variation as a tool to detect hypovolaemia during pneumoperitoneum. Acta Anaesthesiol Scand. 2007;51(9):1268–72.

    CAS  PubMed  Google Scholar 

  12. Renner J, Gruenewald M, Quaden R, Hanss R, Meybohm P, Steinfath M, Scholz J, Bein B. Influence of increased intra-abdominal pressure on fluid responsiveness predicted by pulse pressure variation and stroke volume variation in a porcine model. Crit Care Med. 2009;37(2):650–8.

    Article  PubMed  Google Scholar 

  13. Jacques D, Bendjelid K, Duperret S, Colling J, Piriou V, Viale JP. Pulse pressure variation and stroke volume variation during increased intra-abdominal pressure: an experimental study. Crit Care. 2011;15(1):R33.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hoiseth LO, Hoff IE, Myre K, Landsverk SA, Kirkeboen KA. Dynamic variables of fluid responsiveness during pneumoperitoneum and laparoscopic surgery. Acta Anaesthesiol Scand. 2012;56(6):777–86.

    Article  CAS  PubMed  Google Scholar 

  15. Guinot PG, de Broca B, Bernard E, Abou Arab O, Lorne E, Dupont H. Respiratory stroke volume variation assessed by oesophageal Doppler monitoring predicts fluid responsiveness during laparoscopy. Br J Anaesth. 2014;112(4):660–4.

    Article  PubMed  Google Scholar 

  16. Futier E, Constantin JM, Paugam-Burtz C, Pascal J, Eurin M, Neuschwander A, Marret E, Beaussier M, Gutton C, Lefrant JY, et al. A trial of intraoperative low-tidal-volume ventilation in abdominal surgery. N Engl J Med. 2013;369(5):428–37.

    Article  CAS  PubMed  Google Scholar 

  17. Neto AS, Hemmes SN, Barbas CS, Beiderlinden M, Fernandez-Bustamante A, Futier E, Gajic O, El-Tahan MR, Ghamdi AA, Gunay E, et al. Association between driving pressure and development of postoperative pulmonary complications in patients undergoing mechanical ventilation for general anaesthesia: a meta-analysis of individual patient data. Lancet Respir Med. 2016;4(4):272–80.

    Article  PubMed  Google Scholar 

  18. Park SJ, Kim BG, Oh AH, Han SH, Han HS, Ryu JH. Effects of intraoperative protective lung ventilation on postoperative pulmonary complications in patients with laparoscopic surgery: prospective, randomized and controlled trial. Surg Endosc. 2016;30(10):4598–606.

    Article  CAS  PubMed  Google Scholar 

  19. Myatra SN, Prabu NR, Divatia JV, Monnet X, Kulkarni AP, Teboul JL. The changes in pulse pressure variation or stroke volume variation after a “tidal volume challenge” reliably predict fluid responsiveness during low tidal volume ventilation. Crit Care Med. 2017;45(3):415–21.

    Article  PubMed  Google Scholar 

  20. Messina A, Montagnini C, Cammarota G, De Rosa S, Giuliani F, Muratore L, Della Corte F, Navalesi P, Cecconi M. Tidal volume challenge to predict fluid responsiveness in the operating room: an observational study. Eur J Anaesthesiol. 2019;36(8):583–91.

    Article  PubMed  Google Scholar 

  21. Linares-Perdomo O, East TD, Brower R, Morris AH. Standardizing predicted body weight equations for mechanical ventilation tidal volume settings. Chest. 2015;148(1):73–8.

    Article  PubMed  Google Scholar 

  22. Deflandre E, Bonhomme V, Hans P. Delta down compared with delta pulse pressure as an indicator of volaemia during intracranial surgery. Br J Anaesth. 2008;100(2):245–50.

    Article  CAS  PubMed  Google Scholar 

  23. Zhang J, Critchley LA, Huang L. Five algorithms that calculate cardiac output from the arterial waveform: a comparison with Doppler ultrasound. Br J Anaesth. 2015;115(3):392–402.

    Article  CAS  PubMed  Google Scholar 

  24. Guinot PG, de Broca B, Abou Arab O, Diouf M, Badoux L, Bernard E, Lorne E, Dupont H. Ability of stroke volume variation measured by oesophageal Doppler monitoring to predict fluid responsiveness during surgery. Br J Anaesth. 2013;110(1):28–33.

    Article  PubMed  Google Scholar 

  25. Lahner D, Kabon B, Marschalek C, Chiari A, Pestel G, Kaider A, Fleischmann E, Hetz H. Evaluation of stroke volume variation obtained by arterial pulse contour analysis to predict fluid responsiveness intraoperatively. Br J Anaesth. 2009;103(3):346–51.

    Article  CAS  PubMed  Google Scholar 

  26. Gouvea G, Diaz R, Auler L, Toledo R, Martinho JM. Evaluation of the pulse pressure variation index as a predictor of fluid responsiveness during orthotopic liver transplantation. Br J Anaesth. 2009;103(2):238–43.

    Article  CAS  PubMed  Google Scholar 

  27. Wilkman E, Kuitunen A, Pettila V, Varpula M. Fluid responsiveness predicted by elevation of PEEP in patients with septic shock. Acta Anaesthesiol Scand. 2014;58(1):27–35.

    Article  CAS  PubMed  Google Scholar 

  28. Yang SY, Shim JK, Song Y, Seo SJ, Kwak YL. Validation of pulse pressure variation and corrected flow time as predictors of fluid responsiveness in patients in the prone position. Br J Anaesth. 2013;110(5):713–20.

    Article  PubMed  Google Scholar 

  29. Cecconi M, Parsons AK, Rhodes A. What is a fluid challenge? Curr Opin Crit Care. 2011;17(3):290–5.

    Article  PubMed  Google Scholar 

  30. Hong DM, Lee JM, Seo JH, Min JJ, Jeon Y, Bahk JH. Pulse pressure variation to predict fluid responsiveness in spontaneously breathing patients: tidal vs. forced inspiratory breathing. Anaesthesia. 2014;69(7):717–22.

    Article  CAS  PubMed  Google Scholar 

  31. Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979;86(2):420–8.

    Article  CAS  PubMed  Google Scholar 

  32. Cannesson M, Le Manach Y, Hofer CK, Goarin JP, Lehot JJ, Vallet B, Tavernier B. Assessing the diagnostic accuracy of pulse pressure variations for the prediction of fluid responsiveness: a “gray zone” approach. Anesthesiology. 2011;115(2):231–41.

    Article  PubMed  Google Scholar 

  33. Chiumello D, Carlesso E, Cadringher P, Caironi P, Valenza F, Polli F, Tallarini F, Cozzi P, Cressoni M, Colombo A, et al. Lung stress and strain during mechanical ventilation for acute respiratory distress syndrome. Am J Respir Crit Care Med. 2008;178(4):346–55.

    Article  PubMed  Google Scholar 

  34. Liu Y, Wei LQ, Li GQ, Yu X, Li GF, Li YM. Pulse pressure variation adjusted by respiratory changes in pleural pressure, rather than by tidal volume, reliably predicts fluid responsiveness in patients with acute respiratory distress syndrome. Crit Care Med. 2016;44(2):342–51.

    Article  PubMed  Google Scholar 

  35. Gattinoni L, Chiumello D, Carlesso E, Valenza F. Bench-to-bedside review: chest wall elastance in acute lung injury/acute respiratory distress syndrome patients. Crit Care. 2004;8(5):350–5.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Henny CP, Hofland J. Laparoscopic surgery: pitfalls due to anesthesia, positioning, and pneumoperitoneum. Surg Endosc. 2005;19(9):1163–71.

    Article  CAS  PubMed  Google Scholar 

  37. Cinnella G, Grasso S, Spadaro S, Rauseo M, Mirabella L, Salatto P, De Capraris A, Nappi L, Greco P, Dambrosio M. Effects of recruitment maneuver and positive end-expiratory pressure on respiratory mechanics and transpulmonary pressure during laparoscopic surgery. Anesthesiology. 2013;118(1):114–22.

    Article  CAS  PubMed  Google Scholar 

  38. Joris JL, Noirot DP, Legrand MJ, Jacquet NJ, Lamy ML. Hemodynamic changes during laparoscopic cholecystectomy. Anesth Analg. 1993;76(5):1067–71.

    Article  CAS  PubMed  Google Scholar 

  39. Suehiro K, Tanaka K, Funao T, Matsuura T, Mori T, Nishikawa K. Systemic vascular resistance has an impact on the reliability of the Vigileo-FloTrac system in measuring cardiac output and tracking cardiac output changes. Br J Anaesth. 2013;111(2):170–7.

    Article  CAS  PubMed  Google Scholar 

  40. Kubitz JC, Annecke T, Forkl S, Kemming GI, Kronas N, Goetz AE, Reuter DA. Validation of pulse contour derived stroke volume variation during modifications of cardiac afterload. Br J Anaesth. 2007;98(5):591–7.

    Article  CAS  PubMed  Google Scholar 

  41. Duperret S, Lhuillier F, Piriou V, Vivier E, Metton O, Branche P, Annat G, Bendjelid K, Viale JP. Increased intra-abdominal pressure affects respiratory variations in arterial pressure in normovolaemic and hypovolaemic mechanically ventilated healthy pigs. Intensive Care Med. 2007;33(1):163–71.

    Article  PubMed  Google Scholar 

  42. Jardin F, Genevray B, Brun-Ney D, Bourdarias JP. Influence of lung and chest wall compliances on transmission of airway pressure to the pleural space in critically ill patients. Chest. 1985;88(5):653–8.

    Article  CAS  PubMed  Google Scholar 

  43. Hedenstierna G, Larsson A. Influence of abdominal pressure on respiratory and abdominal organ function. Curr Opin Crit Care. 2012;18(1):80–5.

    Article  PubMed  Google Scholar 

  44. De Backer D, Heenen S, Piagnerelli M, Koch M, Vincent JL. Pulse pressure variations to predict fluid responsiveness: influence of tidal volume. Intensive Care Med. 2005;31(4):517–23.

    Article  PubMed  Google Scholar 

  45. Pinsky MR. Targets for resuscitation from shock. Minerva Anestesiol. 2003;69(4):237–44.

    CAS  PubMed  Google Scholar 

  46. Correa TD, Vuda M, Takala J, Djafarzadeh S, Silva E, Jakob SM. Increasing mean arterial blood pressure in sepsis: effects on fluid balance, vasopressor load and renal function. Crit Care. 2013;17(1):R21.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Monge Garcia MI, Gil Cano A, Gracia Romero M. Dynamic arterial elastance to predict arterial pressure response to volume loading in preload-dependent patients. Crit Care. 2011;15(1):R15.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Lee HC, Jung CW. Vital recorder-a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices. Sci Rep. 2018;8(1):1527.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Funding

This research was supported by the Hallym University Research Fund, 40074 (HURF-2017-51). The funding body had no influence on study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Anesthesiology and Pain Medicine, Kangnam Sacred Heart Hospital, Hallym University, College of Medicine, Seoul, South Korea

    Joo-Hyun Jun, Mi Hwa Chung & Joon-Sang Hyeon

  2. Department of Anesthesiology and Pain Medicine, Ewha Womans University, College of Medicine, Anyangcheon-ro, Yangcheon-gu, Seoul, 1071, South Korea

    Rack Kyung Chung & Hee Jung Baik

  3. Department of Urology, Kangnam Sacred Heart Hospital, Hallym University, College of Medicine, Seoul, South Korea

    Young-Goo Lee

  4. Department of Obstetrics and Gynecology, Kangnam Sacred Heart Hospital, Hallym University, College of Medicine, Seoul, South Korea

    Sung-Ho Park

Authors
  1. Joo-Hyun Jun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rack Kyung Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hee Jung Baik
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mi Hwa Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joon-Sang Hyeon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Young-Goo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sung-Ho Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J-HJ: concept/design, data collection, drafting article, approval of article. RKC: concept/design, critical revision of article, approval of article. HJB: data collection, critical revision of article, approval of article. MHC: data collection, critical revision of article, approval of article. J-SH: data collection, critical revision of article, approval of article. Y-GL: data analysis/interpretation, critical revision of article, approval of article. S-HP: data analysis/interpretation, critical revision of article, approval of article. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Rack Kyung Chung.

Ethics declarations

Ethics approval and consent to participate

This study was performed according to the Declaration of Helsinki after the approval of Institutional Review Board of Hallym University Kangnam Sacred Heart Hospital (2017–09-003). Written informed consent was obtained from the enrolled patients before surgery.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jun, JH., Chung, R.K., Baik, H.J. et al. The tidal volume challenge improves the reliability of dynamic preload indices during robot-assisted laparoscopic surgery in the Trendelenburg position with lung-protective ventilation. BMC Anesthesiol 19, 142 (2019). https://doi.org/10.1186/s12871-019-0807-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12871-019-0807-6

Keywords

BMC Anesthesiology

ISSN: 1471-2253

Contact us