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Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma
This study aimed to create a tailored prediction model of hepatocellular carcinoma (HCC)-specific survival after transplantation based on pre-transplant parameters. Data collected from June 2006 to July 2018 were used as a derivation dataset and analyzed to create an HCC-specific survival prediction...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268829/ https://www.ncbi.nlm.nih.gov/pubmed/34203396 http://dx.doi.org/10.3390/jcm10132869 |
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author | Jamtani, Indah Lee, Kwang-Woong Choi, Yunhee Choi, YoungRok Lee, Jeong-Moo Han, Eui-Soo Hong, Kwangpyo Choi, Gyu-Seong Kim, Jong Man Yi, Nam-Joon Hong, Suk Kyun Byun, Jeik Hong, Su Young Suh, Sanggyeun Joh, Jae-Won Suh, Kyung-Suk |
author_facet | Jamtani, Indah Lee, Kwang-Woong Choi, Yunhee Choi, YoungRok Lee, Jeong-Moo Han, Eui-Soo Hong, Kwangpyo Choi, Gyu-Seong Kim, Jong Man Yi, Nam-Joon Hong, Suk Kyun Byun, Jeik Hong, Su Young Suh, Sanggyeun Joh, Jae-Won Suh, Kyung-Suk |
author_sort | Jamtani, Indah |
collection | PubMed |
description | This study aimed to create a tailored prediction model of hepatocellular carcinoma (HCC)-specific survival after transplantation based on pre-transplant parameters. Data collected from June 2006 to July 2018 were used as a derivation dataset and analyzed to create an HCC-specific survival prediction model by combining significant risk factors. Separate data were collected from January 2014 to June 2018 for validation. The prediction model was validated internally and externally. The data were divided into three groups based on risk scores derived from the hazard ratio. A combination of patient demographic, laboratory, radiological data, and tumor-specific characteristics that showed a good prediction of HCC-specific death at a specific time (t) were chosen. Internal and external validations with Uno’s C-index were 0.79 and 0.75 (95% confidence interval (CI) 0.65–0.86), respectively. The predicted survival after liver transplantation for HCC (SALT) at a time “t” was calculated using the formula: [1 − (HCC-specific death(t’))] × 100. The 5-year HCC-specific death and recurrence rates in the low-risk group were 2% and 5%; the intermediate-risk group was 12% and 14%, and in the high-risk group were 71% and 82%. Our HCC-specific survival predictor named “SALT calculator” could provide accurate information about expected survival tailored for patients undergoing transplantation for HCC. |
format | Online Article Text |
id | pubmed-8268829 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82688292021-07-10 Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma Jamtani, Indah Lee, Kwang-Woong Choi, Yunhee Choi, YoungRok Lee, Jeong-Moo Han, Eui-Soo Hong, Kwangpyo Choi, Gyu-Seong Kim, Jong Man Yi, Nam-Joon Hong, Suk Kyun Byun, Jeik Hong, Su Young Suh, Sanggyeun Joh, Jae-Won Suh, Kyung-Suk J Clin Med Article This study aimed to create a tailored prediction model of hepatocellular carcinoma (HCC)-specific survival after transplantation based on pre-transplant parameters. Data collected from June 2006 to July 2018 were used as a derivation dataset and analyzed to create an HCC-specific survival prediction model by combining significant risk factors. Separate data were collected from January 2014 to June 2018 for validation. The prediction model was validated internally and externally. The data were divided into three groups based on risk scores derived from the hazard ratio. A combination of patient demographic, laboratory, radiological data, and tumor-specific characteristics that showed a good prediction of HCC-specific death at a specific time (t) were chosen. Internal and external validations with Uno’s C-index were 0.79 and 0.75 (95% confidence interval (CI) 0.65–0.86), respectively. The predicted survival after liver transplantation for HCC (SALT) at a time “t” was calculated using the formula: [1 − (HCC-specific death(t’))] × 100. The 5-year HCC-specific death and recurrence rates in the low-risk group were 2% and 5%; the intermediate-risk group was 12% and 14%, and in the high-risk group were 71% and 82%. Our HCC-specific survival predictor named “SALT calculator” could provide accurate information about expected survival tailored for patients undergoing transplantation for HCC. MDPI 2021-06-28 /pmc/articles/PMC8268829/ /pubmed/34203396 http://dx.doi.org/10.3390/jcm10132869 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jamtani, Indah Lee, Kwang-Woong Choi, Yunhee Choi, YoungRok Lee, Jeong-Moo Han, Eui-Soo Hong, Kwangpyo Choi, Gyu-Seong Kim, Jong Man Yi, Nam-Joon Hong, Suk Kyun Byun, Jeik Hong, Su Young Suh, Sanggyeun Joh, Jae-Won Suh, Kyung-Suk Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma |
title | Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma |
title_full | Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma |
title_fullStr | Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma |
title_full_unstemmed | Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma |
title_short | Tailored Prediction Model of Survival after Liver Transplantation for Hepatocellular Carcinoma |
title_sort | tailored prediction model of survival after liver transplantation for hepatocellular carcinoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268829/ https://www.ncbi.nlm.nih.gov/pubmed/34203396 http://dx.doi.org/10.3390/jcm10132869 |
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