Abstract
Background
Excessive intraoperative hemorrhage is a critical factor of poor prognoses after hepatectomy.
Low central venous pressure during parenchymal transection is recognized to effectively
reduce intraoperative hemorrhage in open procedures. However, the role of controlled
low central venous pressure in laparoscopic hepatectomy is still controversial.
Methods
In the present randomized clinical trial, we set up a standard boundary of low central
venous pressure according to our Pilot Study, then enrolled patients scheduled for
elective laparoscopic hepatectomy and allocated them randomly to a group undergoing
central venous pressure reduction by anesthesiologic interventions or a control group.
The primary efficacy endpoint was total intraoperative blood loss and perioperative
adverse events. Analyses were performed following the intention-to-treat principle,
and patients and surgeons were blinded (ClinicalTrials.gov, Number: NCT03422913).
Results
Between January 2017 and October 2018, 146 out of 469 patients were randomized and
eligible for inclusion in the final analyses. Based on the retrospective training
cohort, we set a central venous pressure of 5 cm H2O as a cutoff value (standard low central venous pressure). Compared with patients
in the control group, those in the controlled low central venous pressure group had
a significantly lower central venous pressure during resection (4.83 ± 3.41 cm H2O vs 9.26 ± 3.38 cm H2O; P < .001) and significantly reduced total intraoperative blood loss (188.00 ± 162.00
mL vs 346.00 ± 336.00 mL; P < .001). The perioperative adverse events were comparable in both study groups (P = .313).
Conclusion
The safety and efficacy of controlled low central venous pressure were demonstrated
in complex laparoscopic hepatectomy for the first time by our study, and this technique
is recommended to be applied routinely in laparoscopic hepatectomy.
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Article info
Publication history
Published online: March 23, 2020
Accepted:
February 5,
2020
Footnotes
Yang-Xun Pan, Jun-Cheng Wang, and Xiao-Yun Lu contributed equally to this work.
Identification
Copyright
© 2020 Elsevier Inc. All rights reserved.
