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Shulan Estimation Model: A New Formula for Estimation of Standard Liver Volume In Chinese Adults.

Authors :
Lin, Xin
Xiao, Min
Gu, Yangjun
Zhu, Hengkai
Zhuang, Li
Lu, Jianfang
Zheng, Shusen
Li, Qiyong
Source :
Transplantation Proceedings. Oct2022, Vol. 54 Issue 8, p2236-2242. 7p.
Publication Year :
2022

Abstract

To establish a new and accurate model for standard liver volume (SLV) estimation and graft size prediction in liver transplantation for Chinese adults. In this study, the data of morphologic indices and liver volume (LV) were retrospectively obtained on 507 cadaveric liver transplantation donors between June 2017 and September 2020 in Shulan (Hangzhou) Hospital. Linear regression analysis was performed to evaluate the impact of each parameter and develop a new SLV formula. The new formula was then validated prospectively on 97 donors between October 2020 and June 2021, and the prediction accuracy was compared with previous formulas. The average LV in all subjects was 1445.68 ± 309.94 mL. Body weight (BW) showing the strongest correlation (r = 0.453, P <.001). By stepwise multiple linear regression analysis, BW and age were the only 2 independent correlation factors for LV. Shulan estimation model derived: SLV (mL) = 13.266 × BW (kg) – 4.693 × age + 797.16 (R2 = 0.236, P <.001). In the validation cohort, our new model achieved no significant differences between the estimated SLV and the actual LV (P >.05), and showed the lowest mean percentage error of 0.33%. The proportions of estimated SLV within the actual LV ± 20%, ± 15%, and ± 10% percentage errors were 69.1%, 55.7%, and 40.2%, respectively. The Shulan SLV estimation model predicted LV more accurately than previous formulas on Chinese adults, which could serve as a simple screening tool during the initial assessment of graft volume for potential donors. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00411345
Volume :
54
Issue :
8
Database :
Academic Search Index
Journal :
Transplantation Proceedings
Publication Type :
Academic Journal
Accession number :
160536405
Full Text :
https://doi.org/10.1016/j.transproceed.2022.08.005