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Developing and validating a nomogram based on skeletal muscle index and clinical scoring system for prediction of liver failure after hepatectomy
BACKGROUND AND OBJECTIVES: Hepatectomy is the preferred treatment for patients with liver tumors. Post-hepatectomy liver failure (PHLF) remains one of the most fatal postoperative complications. We aim to explore the risk factors of PHLF and create a nomogram for early prediction of PHLF. METHODS: W...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9893412/ https://www.ncbi.nlm.nih.gov/pubmed/36741004 http://dx.doi.org/10.3389/fonc.2023.1036921 |
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author | Ding, Cong Jia, Jianye Han, Lei Zhou, Wei Liu, Ziyan Bai, Genji Wang, Qian |
author_facet | Ding, Cong Jia, Jianye Han, Lei Zhou, Wei Liu, Ziyan Bai, Genji Wang, Qian |
author_sort | Ding, Cong |
collection | PubMed |
description | BACKGROUND AND OBJECTIVES: Hepatectomy is the preferred treatment for patients with liver tumors. Post-hepatectomy liver failure (PHLF) remains one of the most fatal postoperative complications. We aim to explore the risk factors of PHLF and create a nomogram for early prediction of PHLF. METHODS: We retrospectively analyzed patients undergoing hepatectomy at the Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University between 2015 and 2022, and the patients were divided into training and internal validation cohorts at an 8:2 ratio randomly. The patients undergoing liver resection from the Affiliated Huaian Hospital of Xuzhou Medical University worked as external validation. Then, a nomogram was developed which was based on multivariate analyses to calculate the risk of PHLF. The area under the ROC curve (AUROC) and Hosmer -Lemeshow test was used to evaluate the prediction effect of the model. RESULTS: A total of 421 eligible patients were included in our study. Four preoperative variables were identified after multivariate analysis as follows, ASA (American Society of Anesthesiologists) score, Child-Pugh score, SMI (Skeletal muscle index), and MELD (Model for end-stage liver disease) score as independent predictors of PHLF. The area under the ROC curve of the predictive model in the training, internal, and external validation cohorts were 0.89, 0.82, and 0.89. Hosmer -Lemeshow P values in the training, internal, and external validation cohorts were 0.91, 0.22, and 0.15. The Calibration curve confirmed that our nomogram prediction results were in accurate agreement with the actual occurrence of PHLF. CONCLUSION: We construct a nomogram to predict the grade B/C PHLF of ISGLS (International Study Group of Liver Surgery) in patients who underwent hepatic resection based on risk factors. This tool can provide a visual and accurate preoperative prediction of the grade B/C PHLF and guide the next step of clinical decision-making. |
format | Online Article Text |
id | pubmed-9893412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98934122023-02-03 Developing and validating a nomogram based on skeletal muscle index and clinical scoring system for prediction of liver failure after hepatectomy Ding, Cong Jia, Jianye Han, Lei Zhou, Wei Liu, Ziyan Bai, Genji Wang, Qian Front Oncol Oncology BACKGROUND AND OBJECTIVES: Hepatectomy is the preferred treatment for patients with liver tumors. Post-hepatectomy liver failure (PHLF) remains one of the most fatal postoperative complications. We aim to explore the risk factors of PHLF and create a nomogram for early prediction of PHLF. METHODS: We retrospectively analyzed patients undergoing hepatectomy at the Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University between 2015 and 2022, and the patients were divided into training and internal validation cohorts at an 8:2 ratio randomly. The patients undergoing liver resection from the Affiliated Huaian Hospital of Xuzhou Medical University worked as external validation. Then, a nomogram was developed which was based on multivariate analyses to calculate the risk of PHLF. The area under the ROC curve (AUROC) and Hosmer -Lemeshow test was used to evaluate the prediction effect of the model. RESULTS: A total of 421 eligible patients were included in our study. Four preoperative variables were identified after multivariate analysis as follows, ASA (American Society of Anesthesiologists) score, Child-Pugh score, SMI (Skeletal muscle index), and MELD (Model for end-stage liver disease) score as independent predictors of PHLF. The area under the ROC curve of the predictive model in the training, internal, and external validation cohorts were 0.89, 0.82, and 0.89. Hosmer -Lemeshow P values in the training, internal, and external validation cohorts were 0.91, 0.22, and 0.15. The Calibration curve confirmed that our nomogram prediction results were in accurate agreement with the actual occurrence of PHLF. CONCLUSION: We construct a nomogram to predict the grade B/C PHLF of ISGLS (International Study Group of Liver Surgery) in patients who underwent hepatic resection based on risk factors. This tool can provide a visual and accurate preoperative prediction of the grade B/C PHLF and guide the next step of clinical decision-making. Frontiers Media S.A. 2023-01-19 /pmc/articles/PMC9893412/ /pubmed/36741004 http://dx.doi.org/10.3389/fonc.2023.1036921 Text en Copyright © 2023 Ding, Jia, Han, Zhou, Liu, Bai and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Ding, Cong Jia, Jianye Han, Lei Zhou, Wei Liu, Ziyan Bai, Genji Wang, Qian Developing and validating a nomogram based on skeletal muscle index and clinical scoring system for prediction of liver failure after hepatectomy |
title | Developing and validating a nomogram based on skeletal muscle index and clinical scoring system for prediction of liver failure after hepatectomy |
title_full | Developing and validating a nomogram based on skeletal muscle index and clinical scoring system for prediction of liver failure after hepatectomy |
title_fullStr | Developing and validating a nomogram based on skeletal muscle index and clinical scoring system for prediction of liver failure after hepatectomy |
title_full_unstemmed | Developing and validating a nomogram based on skeletal muscle index and clinical scoring system for prediction of liver failure after hepatectomy |
title_short | Developing and validating a nomogram based on skeletal muscle index and clinical scoring system for prediction of liver failure after hepatectomy |
title_sort | developing and validating a nomogram based on skeletal muscle index and clinical scoring system for prediction of liver failure after hepatectomy |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9893412/ https://www.ncbi.nlm.nih.gov/pubmed/36741004 http://dx.doi.org/10.3389/fonc.2023.1036921 |
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