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Nomogram-based development and evaluation for predictions of 30-day and 1-year survival in patients with spontaneously ruptured hepatocellular carcinoma
BACKGROUND: Accurately predicting the prognosis of patients with spontaneously ruptured hepatocellular carcinoma (HCC) is crucial for effective clinical management. The aim of the present study was to establish and evaluate prediction models for 30-day and 1-year survival in patients with spontaneou...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9664604/ https://www.ncbi.nlm.nih.gov/pubmed/36376820 http://dx.doi.org/10.1186/s12885-022-10290-3 |
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author | Wang, Peng Yang, Shuping Li, Chao Han, Xiangjun Hong, Duo Shao, Haibo |
author_facet | Wang, Peng Yang, Shuping Li, Chao Han, Xiangjun Hong, Duo Shao, Haibo |
author_sort | Wang, Peng |
collection | PubMed |
description | BACKGROUND: Accurately predicting the prognosis of patients with spontaneously ruptured hepatocellular carcinoma (HCC) is crucial for effective clinical management. The aim of the present study was to establish and evaluate prediction models for 30-day and 1-year survival in patients with spontaneously ruptured HCC. METHODS: A total of 118 patients with spontaneous rupture HCC were enrolled. Univariate and multivariate analyses were performed using logistic-regression model and Cox proportional-hazard model. The identified indicators were used to establish prediction models, the performance of which we compared with those of commonly used liver disease scoring models. The survival possibilities of different risk categories were calculated using the newly developed models. RESULTS: Largest tumor size (LTS), serum albumin (ALB), total bilirubin (TBil), and serum creatinine were identified as independent predictors, which were used to establish a 30-day survival prediction model. LTS, BCLC staging, ALB, TBil, hepatectomy at rupture, and TACE during follow-up were identified as independent predictors of 1-year survival model. The 30-day survival model had sensitivity of 79.3%, specificity of 87.1%, and an AUC of 0.879, exhibiting better predictive performance than scores for Chronic Liver Failure Consortium Acute Decompensation score (CLIF-C ADs) and Model for End-stage Liver Disease (MELD). The 1-year survival model had sensitivity of 66.7%, specificity of 94.6%, and an AUC of 0.835, showing better predictive performance than Albumin–Bilirubin (ALBI), Child–Pugh, CLIF-C ADs, and MELD. After stratification, survival possibilities were 90.9 and 21.1% in low- and high-risk groups within 30 days, respectively, and 43.90, 4.35%, and 0 in low-, intermediate-, and high-risk groups at 1 year, respectively. CONCLUSIONS: The established models exhibited good performance in predicting both 30-day and 1-year survival in patients with spontaneously ruptured HCC. |
format | Online Article Text |
id | pubmed-9664604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-96646042022-11-15 Nomogram-based development and evaluation for predictions of 30-day and 1-year survival in patients with spontaneously ruptured hepatocellular carcinoma Wang, Peng Yang, Shuping Li, Chao Han, Xiangjun Hong, Duo Shao, Haibo BMC Cancer Research BACKGROUND: Accurately predicting the prognosis of patients with spontaneously ruptured hepatocellular carcinoma (HCC) is crucial for effective clinical management. The aim of the present study was to establish and evaluate prediction models for 30-day and 1-year survival in patients with spontaneously ruptured HCC. METHODS: A total of 118 patients with spontaneous rupture HCC were enrolled. Univariate and multivariate analyses were performed using logistic-regression model and Cox proportional-hazard model. The identified indicators were used to establish prediction models, the performance of which we compared with those of commonly used liver disease scoring models. The survival possibilities of different risk categories were calculated using the newly developed models. RESULTS: Largest tumor size (LTS), serum albumin (ALB), total bilirubin (TBil), and serum creatinine were identified as independent predictors, which were used to establish a 30-day survival prediction model. LTS, BCLC staging, ALB, TBil, hepatectomy at rupture, and TACE during follow-up were identified as independent predictors of 1-year survival model. The 30-day survival model had sensitivity of 79.3%, specificity of 87.1%, and an AUC of 0.879, exhibiting better predictive performance than scores for Chronic Liver Failure Consortium Acute Decompensation score (CLIF-C ADs) and Model for End-stage Liver Disease (MELD). The 1-year survival model had sensitivity of 66.7%, specificity of 94.6%, and an AUC of 0.835, showing better predictive performance than Albumin–Bilirubin (ALBI), Child–Pugh, CLIF-C ADs, and MELD. After stratification, survival possibilities were 90.9 and 21.1% in low- and high-risk groups within 30 days, respectively, and 43.90, 4.35%, and 0 in low-, intermediate-, and high-risk groups at 1 year, respectively. CONCLUSIONS: The established models exhibited good performance in predicting both 30-day and 1-year survival in patients with spontaneously ruptured HCC. BioMed Central 2022-11-15 /pmc/articles/PMC9664604/ /pubmed/36376820 http://dx.doi.org/10.1186/s12885-022-10290-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Wang, Peng Yang, Shuping Li, Chao Han, Xiangjun Hong, Duo Shao, Haibo Nomogram-based development and evaluation for predictions of 30-day and 1-year survival in patients with spontaneously ruptured hepatocellular carcinoma |
title | Nomogram-based development and evaluation for predictions of 30-day and 1-year survival in patients with spontaneously ruptured hepatocellular carcinoma |
title_full | Nomogram-based development and evaluation for predictions of 30-day and 1-year survival in patients with spontaneously ruptured hepatocellular carcinoma |
title_fullStr | Nomogram-based development and evaluation for predictions of 30-day and 1-year survival in patients with spontaneously ruptured hepatocellular carcinoma |
title_full_unstemmed | Nomogram-based development and evaluation for predictions of 30-day and 1-year survival in patients with spontaneously ruptured hepatocellular carcinoma |
title_short | Nomogram-based development and evaluation for predictions of 30-day and 1-year survival in patients with spontaneously ruptured hepatocellular carcinoma |
title_sort | nomogram-based development and evaluation for predictions of 30-day and 1-year survival in patients with spontaneously ruptured hepatocellular carcinoma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9664604/ https://www.ncbi.nlm.nih.gov/pubmed/36376820 http://dx.doi.org/10.1186/s12885-022-10290-3 |
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