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Readdressing the Middle Hepatic Vein in Right Lobe Liver Donation: Triangle of Safety
Author(s) -
Goja Sanjay,
Kumar Yadav Sanjay,
Singh Soin Arvinder
Publication year - 2018
Publication title -
liver transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.814
H-Index - 150
eISSN - 1527-6473
pISSN - 1527-6465
DOI - 10.1002/lt.25289
Subject(s) - medicine , steatosis , surgery , lobe , liver transplantation , living donor liver transplantation , vein , left lobe , hepatectomy , transplantation , resection , anatomy
For equipoising donor safety and optimal recipient outcomes, we adopted an algorithmic “triangle of safety” approach to retrieve 3 types of right lobe liver grafts (RLGs), namely, the modified extended right lobe graft (MERLG), the partial right lobe graft (PRLG), and the modified right lobe graft (MRLG). Reconstruction to achieve a single wide anterior sector outflow was ensured in all patients. We present donor and recipient outcomes based on our approach in 665 right lobe (RL) living donor liver transplantations (LDLTs) performed from January 2013 to August 2015. There were 347 patients who received a MERLG, 117 who received a PRLG, and 201 who received a MRLG. A right lobe graft (RLG) with a middle hepatic vein was retrieved only in 3 out of 18 donors with steatosis >10%. Cold ischemia time was significantly more and remnant volume was less in the MRLG group. Of the donors, 29.3% had complications (26% Clavien‐Dindo grade I, II) with no statistically significant difference among the groups. The Model for End‐Stage Liver Disease score was higher in the MERLG group. There were 34 out of 39 with a graft‐to‐recipient weight ratio (GRWR) of <0.7% who received a MERLG with inflow modulation. Out of 4 patients who developed small‐for‐size syndrome in this group, 2 died. The 90‐day patient survival rate was similar among different GRWRs and types of RLG. In conclusion, a selective and tailored approach for RL donor hepatectomy based on optimal functional volume and metabolic demands not only addresses the key issue of double equipoise in LDLT but also creates a safe path for extending the limits.