A Formula to Calculate Standard Liver Volume Using Thoracoabdominal Circumference.

Autor:	Shaw BI; School of Medicine, University of California, San Francisco, San Francisco, CA., Burdine LJ; Department of Surgery, University of California, San Francisco, San Francisco, CA., Braun HJ; Department of Surgery, University of California, San Francisco, San Francisco, CA., Ascher NL; Department of Surgery, University of California, San Francisco, San Francisco, CA., Roberts JP; Department of Surgery, University of California, San Francisco, San Francisco, CA.
Jazyk:	angličtina
Zdroj:	Transplantation direct [Transplant Direct] 2017 Oct 27; Vol. 3 (12), pp. e225. Date of Electronic Publication: 2017 Oct 27 (Print Publication: 2017).
DOI:	10.1097/TXD.0000000000000745
Abstrakt:	Background: With the use of split liver grafts as well as living donor liver transplantation (LDLT) it is imperative to know the minimum graft volume to avoid complications. Most current formulas to predict standard liver volume (SLV) rely on weight-based measures that are likely inaccurate in the setting of cirrhosis. Therefore, we sought to create a formula for estimating SLV without weight-based covariates. Methods: LDLT donors underwent computed tomography scan volumetric evaluation of their livers. An optimal formula for calculating SLV using the anthropomorphic measure thoracoabdominal circumference (TAC) was determined using leave-one-out cross-validation. The ability of this formula to correctly predict liver volume was checked against other existing formulas by analysis of variance. The ability of the formula to predict small grafts in LDLT was evaluated by exact logistic regression. Results: The optimal formula using TAC was determined to be SLV = (TAC × 3.5816) - (Age × 3.9844) - (Sex × 109.7386) - 934.5949. When compared to historic formulas, the current formula was the only one which was not significantly different than computed tomography determined liver volumes when compared by analysis of variance with Dunnett posttest. When evaluating the ability of the formula to predict small for size syndrome, many (10/16) of the formulas tested had significant results by exact logistic regression, with our formula predicting small for size syndrome with an odds ratio of 7.94 (95% confidence interval, 1.23-91.36; P = 0.025). Conclusion: We report a formula for calculating SLV that does not rely on weight-based variables that has good ability to predict SLV and identify patients with potentially small grafts. Competing Interests: The authors declare no funding or conflicts of interest.
Databáze:	MEDLINE
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