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Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma

BACKGROUND/AIMS: Posthepatectomy liver failure (PHLF) is a major complication that increases mortality in patients with hepatocellular carcinoma after surgical resection. The aim of this retrospective study was to evaluate the utility of magnetic resonance elastography-assessed liver stiffness (MRE-...

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Autores principales: Cho, Hyo Jung, Ahn, Young Hwan, Sim, Min Suh, Eun, Jung Woo, Kim, Soon Sun, Kim, Bong Wan, Huh, Jimi, Lee, Jei Hee, Kim, Jai Keun, Lee, Buil, Cheong, Jae Youn, Kim, Bohyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Editorial Office of Gut and Liver 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924801/
https://www.ncbi.nlm.nih.gov/pubmed/34810297
http://dx.doi.org/10.5009/gnl210130
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author Cho, Hyo Jung
Ahn, Young Hwan
Sim, Min Suh
Eun, Jung Woo
Kim, Soon Sun
Kim, Bong Wan
Huh, Jimi
Lee, Jei Hee
Kim, Jai Keun
Lee, Buil
Cheong, Jae Youn
Kim, Bohyun
author_facet Cho, Hyo Jung
Ahn, Young Hwan
Sim, Min Suh
Eun, Jung Woo
Kim, Soon Sun
Kim, Bong Wan
Huh, Jimi
Lee, Jei Hee
Kim, Jai Keun
Lee, Buil
Cheong, Jae Youn
Kim, Bohyun
author_sort Cho, Hyo Jung
collection PubMed
description BACKGROUND/AIMS: Posthepatectomy liver failure (PHLF) is a major complication that increases mortality in patients with hepatocellular carcinoma after surgical resection. The aim of this retrospective study was to evaluate the utility of magnetic resonance elastography-assessed liver stiffness (MRE-LS) for the prediction of PHLF and to develop an MRE-LS-based risk prediction model. METHODS: A total of 160 hepatocellular carcinoma patients who underwent surgical resection with available preoperative MRE-LS data were enrolled. Clinical and laboratory parameters were collected from medical records. Logistic regression analyses were conducted to identify the risk factors for PHLF and develop a risk prediction model. RESULTS: PHLF was present in 24 patients (15%). In the multivariate logistic analysis, high MRE-LS (kPa; odds ratio [OR] 1.49, 95% confidence interval [CI] 1.12 to 1.98, p=0.006), low serum albumin (≤3.8 g/dL; OR 15.89, 95% CI 2.41 to 104.82, p=0.004), major hepatic resection (OR 4.16, 95% CI 1.40 to 12.38, p=0.014), higher albumin-bilirubin score (>–0.55; OR 3.72, 95% CI 1.15 to 12.04, p=0.028), and higher serum α-fetoprotein (>100 ng/mL; OR 3.53, 95% CI 1.20 to 10.40, p=0.022) were identified as independent risk factors for PHLF. A risk prediction model for PHLF was established using the multivariate logistic regression equation. The area under the receiver operating characteristic curve (AUC) of the risk prediction model was 0.877 for predicting PHLF and 0.923 for predicting grade B and C PHLF. In leave-one-out cross-validation, the risk model showed good performance, with AUCs of 0.807 for all-grade PHLF and 0. 871 for grade B and C PHLF. CONCLUSIONS: Our novel MRE-LS-based risk model had excellent performance in predicting PHLF, especially grade B and C PHLF.
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spelling pubmed-89248012022-03-24 Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma Cho, Hyo Jung Ahn, Young Hwan Sim, Min Suh Eun, Jung Woo Kim, Soon Sun Kim, Bong Wan Huh, Jimi Lee, Jei Hee Kim, Jai Keun Lee, Buil Cheong, Jae Youn Kim, Bohyun Gut Liver Original Article BACKGROUND/AIMS: Posthepatectomy liver failure (PHLF) is a major complication that increases mortality in patients with hepatocellular carcinoma after surgical resection. The aim of this retrospective study was to evaluate the utility of magnetic resonance elastography-assessed liver stiffness (MRE-LS) for the prediction of PHLF and to develop an MRE-LS-based risk prediction model. METHODS: A total of 160 hepatocellular carcinoma patients who underwent surgical resection with available preoperative MRE-LS data were enrolled. Clinical and laboratory parameters were collected from medical records. Logistic regression analyses were conducted to identify the risk factors for PHLF and develop a risk prediction model. RESULTS: PHLF was present in 24 patients (15%). In the multivariate logistic analysis, high MRE-LS (kPa; odds ratio [OR] 1.49, 95% confidence interval [CI] 1.12 to 1.98, p=0.006), low serum albumin (≤3.8 g/dL; OR 15.89, 95% CI 2.41 to 104.82, p=0.004), major hepatic resection (OR 4.16, 95% CI 1.40 to 12.38, p=0.014), higher albumin-bilirubin score (>–0.55; OR 3.72, 95% CI 1.15 to 12.04, p=0.028), and higher serum α-fetoprotein (>100 ng/mL; OR 3.53, 95% CI 1.20 to 10.40, p=0.022) were identified as independent risk factors for PHLF. A risk prediction model for PHLF was established using the multivariate logistic regression equation. The area under the receiver operating characteristic curve (AUC) of the risk prediction model was 0.877 for predicting PHLF and 0.923 for predicting grade B and C PHLF. In leave-one-out cross-validation, the risk model showed good performance, with AUCs of 0.807 for all-grade PHLF and 0. 871 for grade B and C PHLF. CONCLUSIONS: Our novel MRE-LS-based risk model had excellent performance in predicting PHLF, especially grade B and C PHLF. Editorial Office of Gut and Liver 2022-03-15 2021-11-23 /pmc/articles/PMC8924801/ /pubmed/34810297 http://dx.doi.org/10.5009/gnl210130 Text en Copyright © Gut and Liver. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Cho, Hyo Jung
Ahn, Young Hwan
Sim, Min Suh
Eun, Jung Woo
Kim, Soon Sun
Kim, Bong Wan
Huh, Jimi
Lee, Jei Hee
Kim, Jai Keun
Lee, Buil
Cheong, Jae Youn
Kim, Bohyun
Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma
title Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma
title_full Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma
title_fullStr Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma
title_full_unstemmed Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma
title_short Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma
title_sort risk prediction model based on magnetic resonance elastography-assessed liver stiffness for predicting posthepatectomy liver failure in patients with hepatocellular carcinoma
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924801/
https://www.ncbi.nlm.nih.gov/pubmed/34810297
http://dx.doi.org/10.5009/gnl210130
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