A simple new formula to assess liver weight

Transplant Proc. 2003 Jun;35(4):1415-20. doi: 10.1016/s0041-1345(03)00482-2.

Abstract

Introduction: In cadaveric or segmental liver transplantation, accurate assessment of graft volume is desirable but not always easy to achieve based on donor morphometric data. We sought to establish a simple, reliable formula for accurate prediction of liver volume.

Methods: Data from 1,413 cadaveric adult and pediatric liver donors were analyzed using simple and multiple regression analysis. Liver weight (LW) was plotted against age, height, body weight (BW), body surface area (BSA) or body mass index (BMI); a formula was developed using simple regression: LW (g) = 772 (g/m(2)) x BSA, r = 0.73, P <.01. For donors with BSA </=1.0, a pediatric factor (PF) of 1.0 was included, resulting in the formula: LW (g) = 772 (g/m(2)) x BSA - 38PF, r = 0.73, P <.01. We then applied our formula on 5 published formulae to estimate LW of our donors.

Results: Among donors with BSA >1.0, there was no significant difference between the actual and the estimated mean LW as calculated by the new formula. For pediatric donors, there was no significant difference between estimated and actual mean liver weight with any formula. When the new formula was applied, the difference between the actual and the estimated liver weight was acceptable (<20%) in 1040 (73.6%) cases. In all races, there was no significant difference between actual and estimated mean liver weight as calculated by this formula.

Conclusions: A simple formula to calculate liver weight in donors with BSA >1.0 is: LW = 772 x BSA, and for donors with BSA </=1.0: Liver Weight = 772 x BSA - 38.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Body Height
  • Body Mass Index
  • Body Surface Area
  • Body Weight
  • Cadaver
  • Child
  • Child, Preschool
  • Female
  • Humans
  • Infant
  • Liver / anatomy & histology*
  • Liver Transplantation*
  • Male
  • Middle Aged
  • Organ Size
  • Regression Analysis
  • Tissue Donors / statistics & numerical data