Restrictive fluid management is usually recommended for thoracic surgery to prevent acute lung injury. However, maintaining adequate tissue perfusion is also important for achieving favorable outcomes. Identification of a threshold point during fluid challenge that assures responsiveness may lead to better fluid management in thoracic surgery.
Static preload indicators such as pulmonary capillary wedge pressure or central venous pressure do not predict fluid responsiveness.1–6 Furthermore, the need for less invasive monitoring in cardiothoracic anesthesia is growing. Dynamic preload indicators such as stroke volume variation (SVV) and pulse pressure variation (PPV) are based on changes in the arterial pressure waveform relative to positive pressure ventilation. Many studies have reported that SVV and PPV are good predictors of fluid responsiveness with acceptable levels of sensitivity and specificity.7–12
The usefulness of SVV and PPV is not well established in thoracic surgery. Alterations in intrathoracic pressure by chest wall opening and one-lung ventilation (OLV) can affect SVV or PPV. In addition, although some reported effectiveness of PPV or SVV in patients receiving low tidal volume (TV) of 5–6 mL/kg,13–16 these parameters are validated with TV of more than 8 mL/kg.3
Despite of these limitations, several studies have already reported goal-directed therapy (GDT) for thoracic surgery using the threshold of SVV reported from regular 2-lung ventilation.17 , 18 Thus, in the present study, we investigated whether SVV and PPV can predict fluid responsiveness, and whether the threshold of these dynamic preload indicators should be adjusted in patients undergoing thoracic surgery.
This study was approved by our institutional review board (file number: 2014-06-053-003) and registered with clinicaltrials.gov (NCT02331056, principal investigator: Hyun Joo Ahn, date of registration: December 15, 2014; the original primary end point submitted to this registry was SV 15%, which was revised to stroke volume index (SVI) 10% to remove variations from patient body size). Eighty patients undergoing elective thoracic surgery for lung cancer in a tertiary care academic center from August 2014 to March 2015 were recruited, and written informed consent was obtained from each patient.
The inclusion criteria were American Society of Anesthesiologists physical status (ASA) I to III and elective surgery for a pulmonary lobectomy under open thoracotomy or video-assisted thoracic surgery (VATS). Cases that required at least 1 hour of OLV were included. Exclusion criteria were a 1-second forced expiratory volume < 60% of predicted values, poorly controlled hypertension (systolic arterial pressure ≥ 160 mm Hg), heart disease including arrhythmia, and diuretic use before operation. Patients with renal insufficiency (creatinine > 1.5 mg/dL) and severe obesity (body mass index ≥ 35 kg/m2) were also excluded.
All patients were admitted 1 day before surgery and fasted after dinner and received intravenous fluid during the fasting period (dextrose water 80 mL/h and lactated Ringer’s solution 40 mL/h). No premedication was given before induction of anesthesia. After patients arrived, electrocardiography, noninvasive blood pressure cuff, pulse oximeter, and bispectral index (BIS) monitor (v. 4.0; Aspect Medical System, Natick, MA) were applied. After inducing anesthesia with fentanyl (1.0–2.0 μg/kg), propofol (1.5–2 mg/kg), and rocuronium (0.6 mg/kg), patients were intubated with a double-lumen endotracheal tube (Mallinckrodt; Covidien, Mansfield, MA), and its correct position was confirmed by fiberoptic bronchoscopy. Anesthesia was maintained with desflurane ≤1 minimum alveolar concentration and remifentanil (0.03–0.08 μg/kg/min) between BIS 40 and 55. A radial arterial catheter was placed on the opposite side of surgery and connected to the FloTrac system (version 4.0; Edwards Lifesciences, Irvine, CA) and the EV1000 Clinical Platform (Edwards Lifesciences, Irvine, CA). Pulse pressure variation (PPV) was monitored using the Philips IntelliVue MP70 system (Philips, Suresnes, France).
The inspired O2 concentration (FIO2) was 100% at the start of surgery and was decreased to 50% if the SpO2 was >94% during OLV. OLV was started with a tidal volume of 6 mL/kg of predicted body weight, positive end-expiratory pressure (PEEP) of 5 cm H2O, and a ventilation rate of 10 to 15 breaths/min. End-tidal carbon dioxide was maintained at 35–40 mm Hg. The peak inspiratory pressure was kept below 35 cm H2O. Lactated Ringer’s solution was administered intravenously at 4 mL/kg/h. OLV was started when the patient was turned to the lateral decubitus position in VATS, and when the fascia was incised in open thoracotomy.
Lobectomy was performed by open thoracotomy or VATS. In open thoracotomy, patient chest was exposed to the atmospheric pressure. In VATS cases, the operated lung was deflated by scope insertion and elastic recoil of the lung, and CO2 gas was not used for the lung deflation.
Hemodynamic measurements were conducted at baseline (supine position), movement to the lateral position, 15 minutes after the start of OLV, before fluid loading (T1: 20 minutes after thoracic cavity opened), and just after finishing fluid loading (T2). Stable heart rate (HR) and mean arterial pressure were observed for 5 minutes before hemodynamic measurement without application of vasopressors/inotropes. For volume challenge, 7 mL/kg of colloid solution (Volulyte; Kabi, Bad Homburg, Germany) was infused at T1 over 30 minutes.
The FloTrac/EV1000 system continuously monitors stroke volume (SV), SVI, cardiac output, cardiac index, and SVV without external calibration. The FloTrac system algorithm analyzes the arterial pressure waveform at 100 times per second over 20 seconds.
SV was calculated as follows:19
where x factor compensates for the difference in vascular compliance and resistance, and std(BP) is the standard deviation of the arterial blood pressure.
SVV was calculated as follows:19
where SVmax is the maximum SV, SVmin is the minimum SV, and SVmean is the mean SV.
Automated calculation of PPV was displayed in real time by the Philips IntelliVue MP70 monitor. This system is based on automatic detection algorithms, rank-order filters, and kernel smoothing. It analyzes the pulse pressure over a window of 8 seconds, and the values from 4 consecutive windows (32 seconds) are used to calculate an averaged PPV.19
PPV was calculated as follows:19
where PPmax is the maximum arterial pulse pressure (PP), PPmin is the minimum arterial PP, and PPmean is the mean arterial PP.
A responder was defined as having an SVI increase of more than 10% after volume loading.20 , 21 The primary end point was to evaluate whether SVV and PPV at T1 can predict fluid responsiveness. This was determined by whether the area under the receiver-operating characteristics curve (AUCROC) of SVV and PPV is higher than 0.5 for fluid responsiveness; H0: AUCROC = 0.5 versus H1: AUCROC > 0.5 with α = .05 and power = 0.90, assuming equal number of responders and nonresponders.22 Data are presented as the mean ± SD, or numbers (percentage) as appropriate. Comparisons between responders and nonresponders were performed using Student t test, Mann-Whitney U test, or χ2 test as appropriate. Comparisons between variables of T1 and T2 were performed using paired t tests. Correlations between SVV at T1 and the percentage change in SVV (ΔSVV) and percentage change in SVI (ΔSVI) were examined using the Pearson correlation coefficient. Correlations between PPV at T1 and the percentage change in PPV (ΔPPV) and percentage change in SVI (ΔSVI) were also examined using the Pearson correlation coefficient. Values of P < .05 were considered significant.
We calculated that evaluating 28 patients would be required for the hypothesis, H0: AUCROC = 0.5 versus H1: AUCROC > 0.5 with an expected AUCROC of 0.8, α = .05 and power = 0.90, assuming equal number of responders and nonresponders.22 Taking dropouts into consideration, we included 40 patients each for VATS and open thoracotomy procedures. Threshold value for each parameter was determined by considering values that yielded the greatest sensitivity and specificity.23 The type I error by multiple testing was not corrected. All data were analyzed using MedCalc 14.12.0 (MedCalc; MedCalc Software bvba, Mariakerke, Belgium) except sample size estimation that was performed by the software: pROC package in R v. 3.2.1.
A total of 79 patients finished the study (VATS [n = 39], open thoracotomy [n = 40]), while 1 patient was excluded due to inoperability (an open and close case). There were no cases with significant intraoperative blood loss (>200 mL). No vasopressors/inotropes were administered during the volume challenge and hemodynamic measurement.
Table 1 shows the characteristics of patients and surgeries. Of the 79 patients, 29 (37%) were responders (an increase in SVI ≥ 10%) and 50 (63%) were nonresponders. There were no clinically significant differences in demographic and operative characteristics between responders and nonresponders.
Generally, most hemodynamic variables increased and SVV and PPV decreased after volume challenge (P < .05). An exception was HR, which did not change significantly (Table 2).
Higher SVV and PPV at T1 were related to greater change of ΔSVV and ΔPPV at T2 in the total patients (SVV [r = 0.42; 95% confidence interval [CI], 0.21–0.58; P < .001], PPV [r = 0.55; 95% CI, 0.37–0.69; P < .001]), VATS patients (SVV [r = 0.44; 95% CI, 0.14–0.66; P = .005], PPV [r = 0.60; 95% CI, 0.35–0.78; P < .001]), and open thoracotomy patients (SVV [r = 0.44; 95% CI, 0.15–0.66; P < .001], PPV [r = 0.50; 95% CI, 0.22–0.70; P < .001]; Figure 1).
SVV at T1 could not predict fluid responsiveness; there were no clinically significant differences in SVV at T1 between nonresponders and responders (7.1% ± 2.7% vs 7.4% ± 2.6%; P = .68). This finding was true for both VATS (6.5% ± 2.2% vs 7.2% ± 2.1%; P = .33) and open thoracotomy (7.8% ± 3.1% vs 7.5% ±3 .0%; P = .77) patients (Table 3). In addition, SVV at T1 was not associated with ΔSVI in the total patients (r = −0.01; 95% CI, 0.23–0.21; P = .93) or in the subpopulations (VATS [r = 0.14; 95% CI, 0.19–0.44; P = .40], open thoracotomy [r = −0.10; 95% CI, 0.40–0.22; P = .54]; Figure 2A). The performance of SVV in predicting fluid responsiveness was evaluated via ROC curve analysis (Figure 3A). The AUCROC was 0.53 (95% CI, 0.42–0.65; P = .62), which indicated that SVV at T1 was not predictive of volume responsiveness.
PPV at T1 could predict fluid responsiveness but the performance of prediction was not satisfactory; PPV showed a difference at T1 between nonresponders and responders in the total patients (6.9% ± 3.0%, 8.4 ± 3.2%; P = .045) but not in the subpopulations (VATS [6.6% ± 3.1% vs 8.2% ± 2.7%; P = .12], open thoracotomy [7.3% ± 2.9% vs 8.5% ± 3.7%; P = .24]; Table 3). PPV at T1 was correlated with ΔSVI in the VATS patients (r = 0.32; 95% CI, 0.00–0.58; P = .048), but not in the open thoracotomy patients (r = 0.15; 95% CI, −0.17 to 0.44; P = .36) and the total patients (r = 0.21; 95% CI, 0.00–0.42; P = .06; Figure 2B). The AUCROC was only 0.63 (95% CI, 0.52–0.74; P = .041) for PPV, which was below our hypothesized value (AUCROC > 0.8; Figure 3B). The optimal PPV threshold value to discriminate between responders and nonresponders was 7%, but the sensitivity and specificity of this threshold were low (sensitivity 58%, specificity 62%).
The results of our study suggest that dynamic preload indicators such as SVV or PPV are not useful in predicting fluid responsiveness for thoracic surgery and OLV.
Only a few studies have described the use of SVV or PPV to predict fluid responsiveness in thoracic surgery. In 1 report, SVV at baseline was correlated with ΔSVI and served as an indicator of fluid responsiveness with a threshold value of 10.5%.24 However, another study reported that SVV is unable to predict fluid responsiveness. In that study, SVV at baseline (8.6% vs 8.4%) and the changes (−1.5% vs −1.5%) related to volume expansion did not vary between nonresponders and responders with an AUCROC value of 0.507 (95% CI, 0.294–0.720).23 These 2 previous studies were performed for VATS24 or open thoracotomy23 only, enrolled a small number of patients (n = 30), and used a TV of 8 mL/kg during OLV. The best TV during OLV is not established yet especially in open thoracic surgery; however, most thoracic surgeons or cardiothoracic anesthesiologists recommend TV of 5–6 mL/kg.25 In another study, PPV was tested during OLV with 2 different ventilation modes (TV 6 mL/kg and PEEP 5 cm H2O versus TV 10 mL/kg and no PEEP).16 Although that study reported that PPV can only predict fluid responsiveness with low TV with PEEP ventilation, they enrolled a small number of patients under mixed operations (VATS [n = 4] and open thoracotomy [n = 21]). In addition, this result is considered controversial because conventional ventilation using large TV with no PEEP produces more significant PPV during positive pressure ventilation.15 , 26–28 In a study comparing TV of 6 mL/kg and 8 mL/kg during OLV, SVV predicted fluid responsiveness only when TV is at least 8 mL/kg.15 Reflecting these controversies, a recent review article on the use of SVV/PPV in cardiothoracic surgery reported that there is no definite conclusion on this issue yet, and small sample size and high heterogeneity of studies contributed to the conflicting results, thus further studies are required.29
SVV and PPV are obtained from cyclic changes of stroke volume and pulse pressure relative to cyclic changes of intrathoracic pressure produced by positive pressure ventilation.30 This respiratory variation increases under hypovolemic conditions.3 Therefore, dynamic preload indices such as SVV and PPV can predict fluid responsiveness more precisely than static preload indices in mechanical ventilation.31 However, PPV and SVV are known to be influenced not only by preload status but also by arterial compliance, use of vasopressor, heart function, arrhythmia, tidal volume, lung and chest compliance, and abdominal pressure.29 We excluded severe hypertension, arrhythmia, heart failure, and did not administer vasopressor/inotrope during the study period. Under this controlled circumstance, SVV and PPV were not effective in predicting fluid responsiveness in our study.
The suggested reason is changes in heart-lung interaction in thoracic surgery; First, the nonventilated lung does not produce cyclic changes in intrathoracic pressure. Second, the ventilated lung with small TV (eg, 6 mL/kg) produces reduced variations in pleural and transpulmonary pressures.29 With both mechanisms combined, inspiration does not induce significant change in vena caval, pulmonary arterial, and aortic flows,15 , 29 and thus the cyclic changes in SVV or PPV decrease. Third, the shunt flow of 20%–30% through the nonventilated lung32 does not contribute to SVV or PPV generation. Forth, most of the pressure generated by the positive ventilation is transmitted to the atmosphere through the open chest. Fifth, routine PEEP,26 and surgical manipulation such as compression of the heart and lungs can affect SVV and PPV compared with ordinary 2-lung ventilation.26 , 33 , 34 Therefore, a difference in SVV and PPV between responders and nonresponders, even if it exists, may be small or erratic in thoracic surgery. This may explain why SVV and PPV at T1 were not effective in predicting fluid responsiveness in the current study.
The performance of SVV and PPV in predicting fluid responsiveness was evaluated via ROC curve analysis. The AUCROC was 0.53 (95% CI, 0.42–0.65; P = .62) for SVV and 0.63 (95% CI, 0.52–0.74; P = .041) for PPV. Both were below our hypothesized value (AUCROC > 0.8). Previous reports using TV 6 mL/kg during OLV reported that AUCROC was 0.857 (95% CI, 0.712–1.003) for PPV16 and 0.648 (95% CI, 0.495–0.802) for SVV.15 A systemic review reported better performance of PPV,31 and indicated that PPV has a diagnostic odds ratio 2 times higher than that of SVV in mechanically ventilated intensive care unit patients.31 PPV reportedly changes more than the SVV and a possible explanation for the observation is that during hypovolemia, SV decreases at each mechanical breath accompanying a decrease of SVV, while the mean PP value (denominator) is always smaller than baseline PP.31
We were unable to identify a threshold value for SVV but the optimal PPV threshold value to discriminate between responders and nonresponders was 7%. However, the sensitivity and specificity of this threshold were low (sensitivity 58%, specificity 62%). The optimal cutoff value for dynamic preload indicators remains uncertain. Previous reports have suggested SVV threshold values of 8% to 10.5%15 , 24 and PPV threshold value of 5.8% during OLV.16 Threshold values were also inconsistent in 2 lung ventilations. Study performed for patients undergoing right hepatectomy proposed an optimal SVV cutoff value of 6%.35 In septic shock patients, the optimal threshold value was 10%,36 whereas Wu et al37 reported a threshold value of 14% in lumbar spine surgery. The diversity of these results suggest that the threshold of dynamic preload indicators may be vary depending on situations and should be viewed cautiously even during ordinary 2-lung ventilation.
GDT is currently in the spotlight and has been utilized in 2 thoracic surgery studies.17 , 18 These studies used SVV thresholds of 11% and 10% for volume loading and reported the effectiveness of GDT in thoracic surgery.17 , 18 Nevertheless, according to the results of our study, the feasibility of dynamic preload indicator-guided fluid management in thoracic surgery appears to be low. A recent review article cautioned the use of PPV/SVV in aiding clinicians with fluid responsiveness in the OLV scenario,29 and we think we provided confirmation on that recommendation.
With respect to study limitations, this was an observational study and recruited a relatively healthy population undergoing pulmonary lobectomy which limits the application of our results to other populations. Second, thermodilution is considered as the clinical standard for SV measurement. In this study, SV was measured using the FloTrac/EV1000 system. SV measured using these 2 methods is well correlated in open chest conditions with a percentage error of 20%.38 In addition, thermodilution is not commonly used for thoracic surgery. Therefore, it may be more relevant to determine whether there is a correlation between dynamic preload indicators and SV measured with the FloTrac/EV1000 system. Third, we used 7 mL/kg hydroxyethyl starch as a volume challenge and administered over 30 minutes following the method of previous SVV/PPV studies in thoracic surgery.3 , 15–18 , 23 , 24 However, this poses potential problem. In our study, the patients received intravenous fluid during fasting before surgery. Hydroxyethyl starch administration to euvolemic patient may be deleterious to the glycocalyx membrane and cause more fluid leakiness into the extravascular space39 and could potentially affect the volume responsiveness. Forth, we defined an increase of SVI more than 10% as fluid responder. Various parameters (SVV,18 CI,16 , 23 or SVI24) and ranges (increase of 10%–25%) have been used to determine volume responsiveness. We chose SVI ≥ 10% to increase sensitivity, and exclude body size difference and the influence of HR. However there are no standardized criteria yet to define fluid responsiveness.29 Finally, increased type I error from multiple testing is possible in our study.
In conclusion, dynamic preload indicators such as SVV and PPV are not useful for prediction of fluid responsiveness in thoracic surgery. Based on the current data, it has yet to be determined whether they are the appropriate tools for indication of volume responsiveness in thoracic surgery and OLV.
Name: Dae Myoung Jeong, MD.
Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.
Name: Hyun Joo Ahn, MD, PhD.
Contribution: This author helped design the study, conduct the study, analyze the data, and prepare the manuscript.
Name: Hyo Won Park, MD.
Contribution: This author helped analyze the data and prepare the manuscript.
Name: Mikyung Yang, MD, PhD.
Contribution: This author helped conduct the study and analyze the data.
Name: Jie Ae Kim, MD, PhD.
Contribution: This author helped analyze the data and prepare the manuscript.
Name: Joohyun Park, MD.
Contribution: This author helped analyze the data and prepare the manuscript.
This manuscript was handled by: Tong J. Gan, MD.
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