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Model for liver hardness using two-dimensional shear wave elastography, durometer, and preoperative biomarkers

BACKGROUND: Post-hepatectomy liver failure (PHLF) increases morbidity and mortality after liver resection for patients with advanced liver fibrosis and cirrhosis. Preoperative liver stiffness using two-dimensional shear wave elastography (2D-SWE) is widely used to evaluate the degree of fibrosis. Ho...

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Autores principales: Ju, Bing-Jie, Jin, Ming, Tian, Yang, Zhen, Xiang, Kong, De-Xing, Wang, Wei-Lin, Yan, Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898182/
https://www.ncbi.nlm.nih.gov/pubmed/33643533
http://dx.doi.org/10.4240/wjgs.v13.i2.127
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author Ju, Bing-Jie
Jin, Ming
Tian, Yang
Zhen, Xiang
Kong, De-Xing
Wang, Wei-Lin
Yan, Sheng
author_facet Ju, Bing-Jie
Jin, Ming
Tian, Yang
Zhen, Xiang
Kong, De-Xing
Wang, Wei-Lin
Yan, Sheng
author_sort Ju, Bing-Jie
collection PubMed
description BACKGROUND: Post-hepatectomy liver failure (PHLF) increases morbidity and mortality after liver resection for patients with advanced liver fibrosis and cirrhosis. Preoperative liver stiffness using two-dimensional shear wave elastography (2D-SWE) is widely used to evaluate the degree of fibrosis. However, the 2D-SWE results were not accurate. A durometer measures hardness by quantifying the ability of a material to locally resist the intrusion of hard objects into its surface. However, the durometer score can only be obtained during surgery. AIM: To measure correlations among 2D-SWE, palpation by surgeons, and durometer-measured objective liver hardness and to construct a liver hardness regression model. METHODS: We enrolled 74 hepatectomy patients with liver hardness in a derivation cohort. Tactile-based liver hardness scores (0-100) were determined through palpation of the liver tissue by surgeons. Additionally, liver hardness was measured using a durometer. Correlation coefficients for durometer-measured hardness and preoperative parameters were calculated. Multiple linear regression models were constructed to select the best predictive durometer scale. Receiver operating characteristic (ROC) curves and univariate and multivariate analyses were used to calculate the best model’s prediction of PHLF and risk factors for PHLF, respectively. A separate validation cohort (n = 162) was used to evaluate the model. RESULTS: The stiffness measured using 2D-SWE and palpation scale had good linear correlation with durometer-measured hardness (Pearson rank correlation coefficient 0.704 and 0.729, respectively, P < 0.001). The best model for the durometer scale (hardness scale model) was based on stiffness, hepatitis B virus surface antigen, and albumin level and had an R(2) value of 0.580. The area under the ROC for the durometer and hardness scale for PHLF prediction were 0.807 (P = 0.002) and 0.785 (P = 0.005), respectively. The optimal cutoff value of the durometer and hardness scale was 27.38 (sensitivity = 0.900, specificity = 0.660) and 27.87 (sensitivity = 0.700, specificity = 0.787), respectively. Patients with a hardness scale score of > 27.87 were at a significantly higher risk of PHLF with hazard ratios of 7.835 (P = 0.015). The model’s PHLF predictive ability was confirmed in the validation cohort. CONCLUSION: Liver stiffness assessed by 2D-SWE and palpation correlated well with durometer hardness values. The multiple linear regression model predicted durometer hardness values and PHLF.
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spelling pubmed-78981822021-02-27 Model for liver hardness using two-dimensional shear wave elastography, durometer, and preoperative biomarkers Ju, Bing-Jie Jin, Ming Tian, Yang Zhen, Xiang Kong, De-Xing Wang, Wei-Lin Yan, Sheng World J Gastrointest Surg Retrospective Study BACKGROUND: Post-hepatectomy liver failure (PHLF) increases morbidity and mortality after liver resection for patients with advanced liver fibrosis and cirrhosis. Preoperative liver stiffness using two-dimensional shear wave elastography (2D-SWE) is widely used to evaluate the degree of fibrosis. However, the 2D-SWE results were not accurate. A durometer measures hardness by quantifying the ability of a material to locally resist the intrusion of hard objects into its surface. However, the durometer score can only be obtained during surgery. AIM: To measure correlations among 2D-SWE, palpation by surgeons, and durometer-measured objective liver hardness and to construct a liver hardness regression model. METHODS: We enrolled 74 hepatectomy patients with liver hardness in a derivation cohort. Tactile-based liver hardness scores (0-100) were determined through palpation of the liver tissue by surgeons. Additionally, liver hardness was measured using a durometer. Correlation coefficients for durometer-measured hardness and preoperative parameters were calculated. Multiple linear regression models were constructed to select the best predictive durometer scale. Receiver operating characteristic (ROC) curves and univariate and multivariate analyses were used to calculate the best model’s prediction of PHLF and risk factors for PHLF, respectively. A separate validation cohort (n = 162) was used to evaluate the model. RESULTS: The stiffness measured using 2D-SWE and palpation scale had good linear correlation with durometer-measured hardness (Pearson rank correlation coefficient 0.704 and 0.729, respectively, P < 0.001). The best model for the durometer scale (hardness scale model) was based on stiffness, hepatitis B virus surface antigen, and albumin level and had an R(2) value of 0.580. The area under the ROC for the durometer and hardness scale for PHLF prediction were 0.807 (P = 0.002) and 0.785 (P = 0.005), respectively. The optimal cutoff value of the durometer and hardness scale was 27.38 (sensitivity = 0.900, specificity = 0.660) and 27.87 (sensitivity = 0.700, specificity = 0.787), respectively. Patients with a hardness scale score of > 27.87 were at a significantly higher risk of PHLF with hazard ratios of 7.835 (P = 0.015). The model’s PHLF predictive ability was confirmed in the validation cohort. CONCLUSION: Liver stiffness assessed by 2D-SWE and palpation correlated well with durometer hardness values. The multiple linear regression model predicted durometer hardness values and PHLF. Baishideng Publishing Group Inc 2021-02-27 2021-02-27 /pmc/articles/PMC7898182/ /pubmed/33643533 http://dx.doi.org/10.4240/wjgs.v13.i2.127 Text en ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
spellingShingle Retrospective Study
Ju, Bing-Jie
Jin, Ming
Tian, Yang
Zhen, Xiang
Kong, De-Xing
Wang, Wei-Lin
Yan, Sheng
Model for liver hardness using two-dimensional shear wave elastography, durometer, and preoperative biomarkers
title Model for liver hardness using two-dimensional shear wave elastography, durometer, and preoperative biomarkers
title_full Model for liver hardness using two-dimensional shear wave elastography, durometer, and preoperative biomarkers
title_fullStr Model for liver hardness using two-dimensional shear wave elastography, durometer, and preoperative biomarkers
title_full_unstemmed Model for liver hardness using two-dimensional shear wave elastography, durometer, and preoperative biomarkers
title_short Model for liver hardness using two-dimensional shear wave elastography, durometer, and preoperative biomarkers
title_sort model for liver hardness using two-dimensional shear wave elastography, durometer, and preoperative biomarkers
topic Retrospective Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898182/
https://www.ncbi.nlm.nih.gov/pubmed/33643533
http://dx.doi.org/10.4240/wjgs.v13.i2.127
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