Skip to main content
Download PDF

Prediction of postoperative complications after hepatectomy with dynamic monitoring of central venous oxygen saturation

BMC Surgery volume 23, Article number: 343 (2023) Cite this article

Abstract

Background

The usefulness of static monitoring using central venous pressure has been reported for anesthetic management in hepatectomy. It is unclear whether intra-hepatectomy dynamic monitoring can predict the postoperative course. We aimed to investigate the association between intraoperative dynamic monitoring and post-hepatectomy complications. Furthermore, we propose a novel anesthetic management strategy to reduce postoperative complication.

Methods

From 2018 to 2021, 93 patients underwent hepatectomy at our hospital. Fifty-three patients who underwent dynamic monitoring during hepatectomy were enrolled. Flo Trac system was used for dynamic monitoring. The baseline central venous oxygen saturation (ScvO2) was defined as the average ScvO2 for 30 min after anesthesia induction. ScvO2 fluctuation (ΔScvO2) was defined as the difference between the baseline and minimum ScvO2. Postoperative complications were evaluated using the comprehensive complication index (CCI).

Results

Patients with ΔScvO2 ≥ 10% had significantly higher CCI scores (0 vs. 20.9: p = 0.043). In univariate analysis, patients with higher CCI scores demonstrated significantly higher preoperative C-reactive protein-to-lymphocyte ratio (7.51 vs. 24.49: p = 0.039), intraoperative bleeding (105 vs. 581 ml: p = 0.008), number of patients with major hepatectomy (4/45 vs. 3/8: p = 0.028), and number of patients with ΔScvO2 ≥ 10% (11/45 vs. 6/8; p = 0.010). Multivariate logistic regression analysis revealed that ΔScvO2 ≥ 10% (odds ratio: 9.53, p = 0.016) was the only independent predictor of elevated CCI.

Conclusions

Central venous oxygen saturation fluctuation during hepatectomy is a predictor of postoperative complications. Anesthetic management based on intraoperative dynamic monitoring and minimizing the change in ScvO2 is a potential strategy for decreasing the risk of post-hepatectomy complications.

Peer Review reports

Background

Post-hepatectomy complications have decreased due to technological advances and improved perioperative management. However, post-hepatectomy liver failure, a serious complication, still occurs in 1.2% to 32% of patients after hepatectomy [1, 2]. The occurrence of post-hepatectomy complications is partially related to intraoperative bleeding and perioperative blood transfusion. During hepatectomy, blood loss can be minimized using the Pringle maneuver and low central venous pressure (CVP) management. Maintaining the CVP < 5 cmH2O during hepatectomy reportedly reduces intraoperative bleeding and postoperative complications [3, 4].

CVP measurement involves a static fluid monitoring system; thus, the CVP may not adequately reflect intraoperative fluid volume and tissue oxygen demand. Recently, the Flo Trac system (FTS) has attracted attention as a dynamic fluid monitoring system. The FTS can measure multiple fluid indicators every 20 s, allowing for rapid fluid volume adjustments during surgery [5,6,7]. Among the FTS parameters, intraoperative central venous oxygen saturation (ScvO2) fluctuation (ΔScvO2) is an indicator of increased total bilirubin level after hepatectomy [5].

Although the FTS is reported useful for appropriate intraoperative anesthetic management [5, 7, 8], no study has reported an association between ScvO2 and postoperative complications. The hypothesis of this study is that intraoperative dynamic monitoring will reveal predictors of postoperative complications in hepatectomy. Finally, we propose a novel anesthetic management strategy to reduce the occurrence of postoperative complications.

Methods

In this retrospective cohort study, we enrolled patients who underwent hepatectomy with FTS-monitored anesthetic management in our institution from August 2018 to December 2021. Informed consent for data collection was obtained in the form of an opt-out on the institution website. This study was approved by the ethics review board of our institution (approval number 17–124) in accordance with the ethical guidelines of the Japanese Ministry of Health, Labour, and Welfare regarding clinical studies.

Surgical indication and intraoperative procedures

The extent of hepatectomy was determined based on the primary disease as well as the number and localization of tumors. A major hepatectomy was defined as the removal of one or more segments of the liver. A minor hepatectomy was defined as the removal of less than one segment of the liver. Preoperatively, the indocyanine green test was performed to evaluate the liver function. In patients who underwent major hepatectomy, the remnant K value (remnant liver volume multiplied by indocyanine green disappearance rate) was confirmed to be at least 0.05. Hepatic transection was mainly performed using the Cavitron ultrasonic surgical aspirator (Valleylab, Boulder, CO, USA) and ultrasonic scalpels, with an intermittent application of the Pringle maneuver, which involves clamping the portal triad for 15 and 10 min in patients with normal liver and liver dysfunction, respectively, and releasing the clamp at 5-min intervals. A hemostatic device on the cutting liver surface used saline-coupled soft coagulation of an IO advanced monopolar electrode with a VIO 300 D system (Erbe Elektromedizin GmbH, Tübingen, Germany).

Intraoperative anesthetic management

Each anesthesiologist determined the infusion fluid volume and ventilator settings. During anesthesia, data were collected using a dedicated transducer (FloTrac, Edwards Lifesciences) connected to the radial arterial line and a Vigileo™ monitor (Edwards Lifesciences) or EV1000 Critical Care monitor (Edwards Lifesciences) for continuous monitoring. This monitoring strategy analyzes the pressure waveform 20 times per second for 100 s, captures 2,000 data points for analysis, and performs calculations on the data acquired during the last 20 s. A PreSep central venous oximetry catheter (Edwards Lifesciences) was used to facilitate continuous ScvO2 monitoring [9]. The catheter tip was inserted into the superior vena cava and emitted near-infrared rays, which allowed for continuous blood oxygen saturation measurement. The radial arterial line was connected to the Vigileo™ monitor or EV1000 Critical Care monitor to allow for stroke volume variation (SVV) measurement. The SVV represents the respiratory variability in stroke volume and is affected by the vascular compliance and peripheral resistance. The vascular compliance is estimated from nomograms based on age, sex, height, and weight, whereas the peripheral resistance is determined using radial artery waveforms [10, 11]. In this study, the baseline ScvO2 and ΔScvO2 were defined with a simple modification of previously reported method [5]. The baseline ScvO2 was defined as the average ScvO2 value for 30 min after anesthesia induction. The minimum ScvO2 was defined as the lowest intraoperative ScvO2 value. ΔScvO2 was defined as the difference between the baseline and minimum ScvO2 values (Fig. 1). Moreover, the baseline SVV was defined as the average SVV value for 30 min after anesthesia induction. The maximum SVV was defined as the highest intraoperative SVV value. SVV fluctuation (ΔSVV) was defined as the difference between the baseline and maximum SVV values.

Fig. 1
figure 1

Definitions of baseline and minimum central venous oxygen saturation (ScvO2). The baseline and minimum ScvO2 values are defined as the average ScvO2 value for 30 min after anesthesia induction and the lowest intraoperative ScvO2 value, respectively. ΔScvO2 is defined as the difference between the baseline and minimum ScvO2 values

Full size image

Classification of postoperative complications

Postoperative complications were classified according to the Clavien-Dindo grading system [12] and evaluated using the comprehensive complication index (CCI), which is a score obtained by weighing all postoperative complications based on their severity [13].

Statistical analysis

Continuous variables were expressed as median (interquartile range) and compared using the Mann–Whitney U-test or Student's t-test. Pearson's chi-square test or Fisher's exact test was used to compare categorical variables. A multivariate logistic regression analysis was performed to identify predictors of postoperative complications. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Statistical analysis was performed using SPSS version 26 (IBM Corp., Armonk, NY, USA), and p-values < 0.05 were considered statistically significant.

Results

Of 93 patients who underwent hepatectomy during the study period at our institute, 58 patients were received anesthetic management with FTS monitoring. We excluded four patients with biliary reconstruction and one patient with aspiration pneumonia-induced in-hospital death. Therefore, 53 patients were enrolled in this study (Fig. 2).

Fig. 2
figure 2

Study flow chart. From August 2018 to December 2021, 58 patients underwent hepatectomy using Flo Trac system (FTS)-monitored anesthetic management. Four and one patients with biliary reconstruction and postoperative death were excluded, respectively. Hence, the 53 included patients were divided into two groups: low (central venous oxygen saturation fluctuation [ΔScvO2] < 10%, n = 36) and high (ΔScvO2 ≥ 10%, n = 17) ScvO2 groups. ΔScvO2 is defined as the difference between the baseline and minimum ScvO2 values

Full size image

Basic patient characteristics

Forty patients were men. All patients had liver diseases with Child–Pugh and liver damage classifications of A or B. One patient had a history of atrial fibrillation (Table 1).

Table 1 Baseline patient characteristics
Full size table

Patient characteristics stratified by ΔScvO2 and average SVV

A previous study reported that ΔScvO2 ≥ 10.2% was a significant predictor of postoperative liver dysfunction. Therefore, the 53 patients were divided into two groups: low (ΔScvO2 < 10%, n = 36) and high (ΔScvO2 ≥ 10%, n = 17) ΔScvO2 groups (Fig. 2). There was no significant difference in patient background and preoperative clinicopathological factors between the two groups (Table 2). A previous study reported that intraoperative average SVV ≥ 13.6 was a significant predictor of postoperative liver dysfunction [5]. Hence, the 53 patients were divided into two groups: low (SVV < 13.6, n = 45) and high (SVV ≥ 13.6, n = 8) SVV groups. Additional file 1 compares the patient background, preoperative treatment, and preoperative blood test findings between the two groups. There were significant between-group differences in the levels of total bilirubin (0.7 [0.6–1.0] vs. 1.2 [0.9–1.5] mg/dL; p = 0.019), alkaline phosphatase (239 [199–310] vs. 202 [159–205] IU/l; p = 0.021), γ-glutamyltranspeptidase (55 [27–92] vs. 27 [14–27] IU/l; p = 0.026), and C-reactive protein (0.13 [0.05–0.37] vs. 0.05 [0.03–0.09] mg/dL; p = 0.028) as well as C-reactive protein-to-lymphocyte ratio (CLR) (9.87 [4.88–32.42] vs. 4.49 [2.68–8.36]; p = 0.034) and C-reactive protein-to-albumin ratio (0.030 [0.011–0.097] vs. 0.010 [0.007–0.021]; p = 0.028).

Table 2 Patient data stratified by ΔScvO2
Full size table

Intraoperative factors stratified by ΔScvO2 and average SVV

Table 2 also shows the intraoperative factors stratified by ΔScvO2. There was a significant between-group difference in the number of patients who underwent major (2/36 vs. 5/17; p = 0.017) and laparotomy (15/36 vs. 13/17; p = 0.018) hepatectomy. Operation time, intraoperative bleeding, intraoperative fluid volume, and hepatectomy time were not significantly different between the two groups. Additional file 1 also presents intraoperative factors stratified by average SVV. Intraoperative bleeding (275 [85–542] vs. 33 [13–86] ml; p = 0.005) was significantly different between the two groups.

Postoperative course stratified by ΔScvO2 and average SVV

Table 2 also shows postoperative course and details of postoperative complications stratified by ΔScvO2. The minimum cholinesterase level (179 [160–216] vs. 160 [124–172] IU/l; p = 0.036) and CCI score (0 [0–14.4] vs. 20.9 [0–24.2]; p = 0.043) were significantly different between the two groups. There was no significant between-group difference in the incidence of complications with Clavien-Dindo grade III or more. High ΔScvO2 tended to associate with more frequent pleural effusion and delayed gastric emptying. There was no patient of post hepatectomy liver failure. Additional file 1 also shows postoperative blood test findings and postoperative course stratified by average SVV. There was no significant between-group difference in the incidence of complications with Clavien-Dindo grade III or more and CCI score.

Patient characteristics stratified by CCI score

The abovementioned results suggested that the intraoperative ΔScvO2 was related to postoperative complication occurrence. Although the cut off value of the CCI is considered to be different depending on each surgical procedures, the median CCI score for the study participants was 20.9; hence, the participants were divided into two groups: low (CCI < 21, n = 45) and high (CCI ≥ 21, n = 8) groups.

Table 3 presents patient characteristics stratified by CCI score. We found no significant between-group difference in patient background and preoperative clinicopathological factors except for CLR (7.51 [4.02–16.39] vs. 24.49 [9.83–101.19]; p = 0.039) in univariate analysis.

Table 3 Patient characteristics stratified by CCI
Full size table

Intraoperative factors stratified by CCI score

There were significant differences in the number of patients who underwent major hepatectomy (4/45 vs. 3/8; p = 0.028) and in intraoperative bleeding (105 [35–382] vs. 581 [465–694] ml; p = 0.008) (Table 4) in univariate analysis.

Table 4 Intraoperative factors stratified by CCI
Full size table

FTS measurements stratified by CCI score

We observed a significant between-group difference in the number of patients with ΔScvO2 ≥ 10% (11/45 vs. 6/8; p = 0.010) (Table 5) in univariate analysis. However, the average SVV, maximum SVV, and ΔSVV were not significantly different between the two groups. Furthermore, the pre- and postoperative CVP as well as the maximum intraoperative CVP were not significantly different between the two groups. Lactate levels measured immediately after surgery were not significantly different between the two groups.

Table 5 FTS measurements
Full size table

ΔScvO2 was an independent predictor of higher CCI scores

Multivariate logistic regression analysis revealed the discriminative capacity of high CCI scores (Table 6). The CLR (median, 9.7), intraoperative bleeding (median, 240 mL), the number of cases with major hepatectomy and ΔScvO2 ≥ 10% were included in the multivariate analysis. The result revealed that ΔScvO2 ≥ 10% (p = 0.016, odds ratio: 9.53) was the only independent predictor of higher CCI scores.

Table 6 Multivariate analysis
Full size table

Discussion

This study evaluated the intraoperative ScvO2 and SVV measured using the FTS in patients undergoing hepatectomy. ΔScvO2 showed a significant positive correlation with CCI score, whereas, average SVV, maximum SVV, and ΔSVV were not significantly correlated with CCI score. Multivariate analysis identified ΔScvO2 as an independent predictor of elevated CCI scores.

Although recent studies have reported that the mortality rate of patients undergoing hepatectomy is less than 5%, post-hepatectomy complication rates range from 20 to 40%, depending on the extent of resection and liver function [14, 15]. Intraoperative bleeding constitutes a major factor affecting post-hepatectomy outcomes [16, 17]. Intermittent blockage of hepatic blood flow using the Pringle maneuver can reduce intraoperative bleeding; nevertheless, it causes hepatocyte ischemia and reperfusion, leading to liver injury and elevated serum lactate levels [18, 19]. Patients with elevated lactate levels immediately after hepatectomy have a higher risk of postoperative morbidity and mortality [20]. In this study, there was no relationship between the CCI score and postoperative lactate levels. Postoperative lactate level may not be a good predictor of complications in patients undergoing minimally invasive surgery and minor hepatectomy.

Generally, lowering the CVP during hepatectomy reduces hepatic venous and sinusoidal pressures, thereby minimizing bleeding from the liver parenchyma [16, 21]. During hepatectomy, it is recommended to maintain the CVP < 5 cmH2O [3, 22, 23]. However, the CVP is affected by the patient's position during surgery, intrathoracic pressure, and operator compression or clamping of the inferior vena cava, hepatic vein, and portal vein [24]. Furthermore, the CVP is a static hemodynamic monitoring indicator, and thus it is inaccurate for diagnosing fluid deficiencies.

Enhanced recovery after surgery guidelines suggest that dynamic monitoring indicators may replace the CVP as an indicator of fluid responsiveness [25, 26]. Real-time monitoring of the oxygen demand–supply imbalance associated with hepatectomy enables an early detection and treatment of abnormalities and prevents perioperative complications. Previous studies have demonstrated that patients who underwent FTS-monitored anesthetic management had a good postoperative course [6, 27, 28]. The SVV, an FTS-measured indicator of fluid responsiveness, is useful for the perioperative management of patients undergoing highly invasive surgery [6, 29]. Moreover, the SVV is better than the CVP as a predictor of fluid responsiveness during hepatectomy [27]. An intraoperative mean SVV ≥ 13.6 has been reported to increase postoperative total bilirubin levels [5]. However, our study showed no relationship between the SVV and CCI score. Although the SVV is an index of fluid responsiveness, it does not assess tissue oxygenation. The oxygen demand–supply balance may be undisturbed even when the SVV is high. In addition, the SVV cannot be accurately assessed in patients with arrhythmias or in those undergoing laparoscopic surgery [11, 30].

The FTS can also measure the ScvO2, which is an indicator of oxygen demand–supply balance. Oxygen deprivation can lead to mitochondrial dysfunction-induced organ damage [31], which reduces resistance to postoperative stress, thereby increasing the occurrence of postoperative complications. Patients with low intraoperative ScvO2 values are more predisposed to complications after high-risk surgical procedures [32]. During hepatectomy, ischemia–reperfusion injury caused by the Pringle maneuver alters the balance of hepatic oxygen supply [1, 5]. The results of FTS, including ScvO2, are influenced by vascular compliance and peripheral vascular resistance. Vascular compliance is estimated from age, sex, height, and weight [11]. Above mentioned factors were not significantly different between two groups in this study. The optimal cutoff ScvO2 value for predicting postoperative complications differs between healthy patients and those with trauma, severe sepsis, and heart failure [33, 34]. It is difficult to determine the standard ScvO2 value for all patients; nevertheless, the postoperative course can be improved via intraoperative ΔScvO2 suppression. More detailed studies are needed on the factors and mechanisms involved in ScvO2 fluctuations.

Furthermore, we found a relationship between the CCI score and CLR in univariate analysis. Preoperative inflammatory biomarkers have been shown to be associated with the incidence of postoperative complications after esophagectomy [35, 36]. The postoperative course is affected by preoperative lymphocyte count and C-reactive protein levels, which are involved in immune and inflammatory reactions, respectively. CLR is thought to predict the postoperative status better than other inflammatory biomarkers. If the number of cases increases, preoperative CLR may become effective predictor of postoperative complications in hepatectomy.

This study has some potential limitations. First, the anesthetic management method was not standardized; it was selected at the discretion of each anesthesiologist. Therefore, the method used by the anesthesiologist may have influenced intraoperative indicators. Second, the study was a retrospective, single-center cohort with a small sample size. This may cause of the lack of significant differences in ΔScvo2 and individual complications, although a trend was observed for pleural effusions and DGE. Therefore, the study findings should be verified via large-scale, multicenter randomized controlled trials.

In conclusion, ScvO2 monitoring using the FTS can be used as an alternative to CVP monitoring and lactate level measurement to predict the risk of postoperative complications. Given the association between change in ScvO2 and postoperative complications, minimizing the change in ScvO2 is a potential strategy for decreasing the risk of postoperative complications after hepatectomy.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to institutional policies but are available from the corresponding author on reasonable request.

Abbreviations

CVP:

Central venous pressure

FTS:

Flo Trac system

ScvO2:

Central venous oxygen saturation

ΔScvO2 :

Central venous oxygen saturation fluctuation

SVV:

Stroke volume variation

ΔSVV:

Stroke volume variation fluctuation

CCI:

Comprehensive complication index

ORs:

Odds ratios

CIs:

Confidence intervals

CLR:

C-reactive protein-to-lymphocyte ratio

References

  1. Wu G, Chen T, Chen Z. Effect of controlled low central venous pressure technique on postoperative hepatic insufficiency in patients undergoing a major hepatic resection. Am J Transl Res. 2021;13:8286–93.

    PubMed  PubMed Central  Google Scholar 

  2. Rahbari NN, Garden OJ, Padbury R, Brooke-Smith M, Crawford M, Adam R, et al. Posthepatectomy Liver Failure: a definition and grading by the International Study Group of Liver Surgery (ISGLS). Surgery. 2011;149:713–24. https://0-doi-org.brum.beds.ac.uk/10.1016/j.surg.2010.10.001.

    Article  PubMed  Google Scholar 

  3. Peres LA, Bredt LC, Cipriani RF. Acute renal injury after partial hepatectomy. World J Hepatol. 2016;8:891–901. https://0-doi-org.brum.beds.ac.uk/10.4254/wjh.v8.i21.891.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Tympa A, Theodoraki K, Tsaroucha A, Arkadopoulos N, Vassiliou I, Smyrniotis V. Anesthetic considerations in hepatectomies under hepatic vascular control. HPB Surg. 2012;2012:720754. https://0-doi-org.brum.beds.ac.uk/10.1155/2012/720754.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Meguro M, Mizuguchi T, Kawamoto M, Nakamura Y, Ota S, Kukita K, et al. Continuous monitoring of central venous oxygen saturation predicts postoperative liver dysfunction after liver resection. Surgery. 2013;154:351–62. https://0-doi-org.brum.beds.ac.uk/10.1016/j.surg.2013.04.039.

    Article  PubMed  Google Scholar 

  6. Kobayashi M, Ko M, Kimura T, Meguro E, Hayakawa Y, Irinoda T, et al. Perioperative monitoring of fluid responsiveness after esophageal surgery using stroke volume variation. Expert Rev Med Devices. 2008;5:311–6. https://0-doi-org.brum.beds.ac.uk/10.1586/17434440.5.3.311.

    Article  PubMed  Google Scholar 

  7. Manecke GR. Edwards FloTrac sensor and Vigileo monitor: easy, accurate, reliable cardiac output assessment using the arterial pulse wave. Expert Rev Med Devices. 2005;2:523–7. https://0-doi-org.brum.beds.ac.uk/10.1586/17434440.2.5.523.

    Article  PubMed  Google Scholar 

  8. Ratti F, Cipriani F, Reineke R, Catena M, Paganelli M, Comotti L, et al. Intraoperative monitoring of Stroke volume variation versus central venous pressure in laparoscopic liver Surgery: a randomized prospective comparative trial. HPB. 2016;18:136–44. https://0-doi-org.brum.beds.ac.uk/10.1016/j.hpb.2015.09.005.

    Article  PubMed  Google Scholar 

  9. Soga T, Kawahito S, Oi R, Kakuta N, Katayama T, Wakamatsu N, et al. Recent less-invasive circulatory monitoring during renal transplantation. J Med Invest. 2013;60:159–63. https://0-doi-org.brum.beds.ac.uk/10.2152/jmi.60.159.

    Article  PubMed  Google Scholar 

  10. Cannesson M, Musard H, Desebbe O, Boucau C, Simon R, Hénaine R, et al. The ability of Stroke volume variations obtained with Vigileo/FloTrac system to monitor fluid responsiveness in mechanically ventilated patients. Anesth Analg. 2009;108:513–7. https://0-doi-org.brum.beds.ac.uk/10.1213/ane.0b013e318192a36b.

    Article  PubMed  Google Scholar 

  11. Slagt C, Malagon I, Groeneveld AB. Systematic review of uncalibrated arterial pressure waveform analysis to determine cardiac output and Stroke volume variation. Br J Anaesth. 2014;112:626–37. https://0-doi-org.brum.beds.ac.uk/10.1093/bja/aet429.

    Article  PubMed  CAS  Google Scholar 

  12. Veličković J, Feng C, Palibrk I, Veličković D, Jovanović B, Bumbaširević V. The assessment of Complications after major abdominal Surgery: a comparison of two scales. J Surg Res. 2020;247:397–405. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jss.2019.10.003.

    Article  PubMed  Google Scholar 

  13. Slankamenac K, Graf R, Barkun J, Puhan MA, Clavien PA. The comprehensive complication index: a novel continuous scale to measure surgical morbidity. Ann Surg. 2013;258:1–7. https://0-doi-org.brum.beds.ac.uk/10.1097/SLA.0b013e318296c732.

    Article  PubMed  Google Scholar 

  14. Giani A, Cipriani F, Famularo S, Donadon M, Bernasconi DP, Ardito F, et al. Performance of comprehensive complication index and Clavien-Dindo complication scoring system in liver Surgery for hepatocellular carcinoma. Cancers. 2020;12:3868. https://0-doi-org.brum.beds.ac.uk/10.3390/cancers12123868.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Paugam-Burtz C, Janny S, Delefosse D, Dahmani S, Dondero F, Mantz J, et al. Prospective validation of the fifty-fifty criteria as an early and accurate predictor of death after liver resection in intensive care unit patients. Ann Surg. 2009;249:124–8. https://0-doi-org.brum.beds.ac.uk/10.1097/SLA.0b013e31819279cd.

    Article  PubMed  Google Scholar 

  16. Wang F, Sun D, Zhang N, Chen Z. The efficacy and safety of controlled low central venous pressure for liver resection: a systematic review and meta-analysis. Gland Surg. 2020;9:311–20. https://0-doi-org.brum.beds.ac.uk/10.21037/gs.2020.03.07.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kamiyama T, Nakanishi K, Yokoo H, Kamachi H, Tahara M, Yamashita K, et al. Perioperative management of hepatic resection toward zero mortality and morbidity: analysis of 793 consecutive cases in a single institution. J Am Coll Surg. 2010;211:443–9. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jamcollsurg.2010.06.005.

    Article  PubMed  Google Scholar 

  18. Wiggans MG, Starkie T, Shahtahmassebi G, Woolley T, Birt D, Erasmus P, et al. Serum arterial lactate concentration predicts mortality and organ dysfunction following liver resection. Perioper Med. 2013;2:21. https://0-doi-org.brum.beds.ac.uk/10.1186/2047-0525-2-21.

    Article  Google Scholar 

  19. Theodoraki K, Arkadopoulos N, Fragulidis G, Voros D, Karapanos K, Markatou M, et al. Transhepatic lactate gradient in relation to liver ischemia/reperfusion injury during major hepatectomies. Liver Transpl. 2006;12:1825–31. https://0-doi-org.brum.beds.ac.uk/10.1002/lt.20911.

    Article  PubMed  Google Scholar 

  20. Lemke M, Karanicolas PJ, Habashi R, Behman R, Coburn NG, Hanna SS, et al. Elevated lactate is independently associated with adverse outcomes following hepatectomy. World J Surg. 2017;41:3180–8. https://0-doi-org.brum.beds.ac.uk/10.1007/s00268-017-4118-0.

    Article  PubMed  Google Scholar 

  21. Hughes MJ, Ventham NT, Harrison EM, Wigmore SJ. Central venous pressure and liver resection: a systematic review and meta-analysis. HPB. 2015;17:863–71. https://0-doi-org.brum.beds.ac.uk/10.1111/hpb.12462.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wang WD, Liang LJ, Huang XQ, Yin XY. Low central venous pressure reduces blood loss in hepatectomy. World J Gastroenterol. 2006;12:935–9. https://0-doi-org.brum.beds.ac.uk/10.3748/wjg.v12.i6.935.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Liu TS, Shen QH, Zhou XY, Shen X, Lai L, Hou XM, et al. Application of controlled low central venous pressure during hepatectomy: a systematic review and meta-analysis. J Clin Anesth. 2021;75:110467.

    Article  PubMed  Google Scholar 

  24. Choi SS, Kim SH, Kim YK. Fluid management in living donor hepatectomy: recent issues and perspectives. World J Gastroenterol. 2015;21:12757–66. https://0-doi-org.brum.beds.ac.uk/10.3748/wjg.v21.i45.12757.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Agarwal V, Divatia JV. Enhanced recovery after Surgery in liver resection: current concepts and controversies. Korean J Anesthesiol. 2019;72:119–29. https://0-doi-org.brum.beds.ac.uk/10.4097/kja.d.19.00010.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Dunki-Jacobs EM, Philips P, Scoggins CR, McMasters KM, Martin RC 2. Stroke volume variation in hepatic resection: a replacement for standard central venous pressure monitoring. Ann Surg Oncol. 2014;21:473–8. https://0-doi-org.brum.beds.ac.uk/10.1245/s10434-013-3323-9.

    Article  PubMed  Google Scholar 

  27. Kitaguchi K, Gotohda N, Yamamoto H, Kato Y, Takahashi S, Konishi M, et al. Intraoperative circulatory management using the FloTrac™ system in laparoscopic liver resection. Asian J Endosc Surg. 2015;8:164–70. https://0-doi-org.brum.beds.ac.uk/10.1111/ases.12158.

    Article  PubMed  Google Scholar 

  28. Giustiniano E, Procopio F, Ruggieri N, Grimaldi S, Torzilli G, Raimondi F. Impact of the FloTrac/VigileoTM monitoring on intraoperative fluid management and outcome after liver resection. Dig Surg. 2018;35:435–41. https://0-doi-org.brum.beds.ac.uk/10.1159/000481406.

    Article  PubMed  CAS  Google Scholar 

  29. Su BC, Tsai YF, Cheng CW, Yu HP, Yang MW, Lee WC, et al. Stroke volume variation derived by arterial pulse contour analysis is a good indicator for preload estimation during liver transplantation. Transpl Proc. 2012;44:429–32. https://0-doi-org.brum.beds.ac.uk/10.1016/j.transproceed.2011.12.037.

    Article  CAS  Google Scholar 

  30. Høiseth LØ, Hoff IE, Myre K, Landsverk SA, Kirkebøen KA. Dynamic variables of fluid responsiveness during pneumoperitoneum and laparoscopic Surgery. Acta Anaesthesiol Scand. 2012;56:777–86. https://0-doi-org.brum.beds.ac.uk/10.1111/j.1399-6576.2011.02641.x.

    Article  PubMed  Google Scholar 

  31. Mayer J, Boldt J, Mengistu AM, Röhm KD, Suttner S. Goal-directed intraoperative therapy based on autocalibrated arterial pressure waveform analysis reduces hospital stay in high-risk surgical patients: a randomized, controlled trial. Crit Care. 2010;14:R18. https://0-doi-org.brum.beds.ac.uk/10.1186/cc8875.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Collaborative Study Group on Perioperative ScvO2 Monitoring. Multicentre study on peri- and postoperative central venous oxygen saturation in high-risk surgical patients. Crit Care. 2006;10:R158. https://0-doi-org.brum.beds.ac.uk/10.1186/cc5094.

    Article  PubMed Central  Google Scholar 

  33. Pearse R, Dawson D, Fawcett J, Rhodes A, Grounds RM, Bennett ED. Changes in central venous saturation after major Surgery, and association with outcome. Crit Care. 2005;9:R694–9. https://0-doi-org.brum.beds.ac.uk/10.1186/cc3888.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Reinhart K, Kuhn HJ, Hartog C, Bredle DL. Continuous central venous and pulmonary artery oxygen saturation monitoring in the critically ill. Intensive Care Med. 2004;30:1572–8. https://0-doi-org.brum.beds.ac.uk/10.1007/s00134-004-2337-y.

    Article  PubMed  Google Scholar 

  35. Tsujiura M, Yamamoto A, Imaoka H, Shimura T, Kitajima T, Morimoto Y, et al. Clinical utility of lymphocyte to C-reactive protein ratio in predicting survival and postoperative complication for esophago-gastric junction cancer. Surg Oncol. 2022;44:101842. https://0-doi-org.brum.beds.ac.uk/10.1016/j.suronc.2022.101842.

    Article  PubMed  Google Scholar 

  36. Yamamoto A, Toiyama Y, Okugawa Y, Ichikawa T, Imaoka H, Yasuda H, et al. Clinical implications of the preoperative lymphocyte C-reactive protein ratio in Esophageal cancer patients. Surg Today. 2021;51:745–55. https://0-doi-org.brum.beds.ac.uk/10.1007/s00595-020-02166-5.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Hamamatsu, 431-3192, Japan

    Shinya Ida, Yoshifumi Morita, Akio Matsumoto, Ryuta Muraki, Ryo Kitajima, Satoru Furuhashi, Makoto Takeda, Hirotoshi Kikuchi, Yoshihiro Hiramatsu & Hiroya Takeuchi

  2. Division of Surgical Care, Morimachi, Hamamatsu University School of Medicine, Hamamatsu, Japan

    Yoshifumi Morita

  3. Department of Perioperative Functioning Care and Support, Hamamatsu University School of Medicine, Hamamatsu, Japan

    Yoshihiro Hiramatsu

Authors
  1. Shinya Ida
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoshifumi Morita
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Akio Matsumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ryuta Muraki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ryo Kitajima
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Satoru Furuhashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Makoto Takeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hirotoshi Kikuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yoshihiro Hiramatsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hiroya Takeuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Shinya Ida and Yoshifumi Morita wrote the main manuscript text and prepared tables and figures. All authors reviewed the manuscript.

Corresponding author

Correspondence to Yoshifumi Morita.

Ethics declarations

Ethics approval and consent to participate

Informed consent for data collection was obtained in the form of an opt-out on the institution website. This study was approved by the ethics review board of our institution (approval number 17–124) in accordance with the ethical guidelines of the Japanese Ministry of Health, Labour, and Welfare regarding clinical studies.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

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

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ida, S., Morita, Y., Matsumoto, A. et al. Prediction of postoperative complications after hepatectomy with dynamic monitoring of central venous oxygen saturation. BMC Surg 23, 343 (2023). https://0-doi-org.brum.beds.ac.uk/10.1186/s12893-023-02238-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s12893-023-02238-6

Keywords

BMC Surgery

ISSN: 1471-2482

Contact us