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Validation and comparison of non-invasive prediction models based on liver stiffness measurement to identify patients who could avoid gastroscopy
Several non‐invasive tests (NITs) based on liver stiffness measurement (LSM) have been developed to rule out varices needing treatment (VNT), including the Baveno VI criteria (B6C), the expanded Baveno VI criteria (EB6C), the LSM-spleen diameter to platelet ratio score (LSPS), and the VariScreen alg...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794348/ https://www.ncbi.nlm.nih.gov/pubmed/33420233 http://dx.doi.org/10.1038/s41598-020-80136-0 |
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author | Hu, Youwen Wen, Zhili |
author_facet | Hu, Youwen Wen, Zhili |
author_sort | Hu, Youwen |
collection | PubMed |
description | Several non‐invasive tests (NITs) based on liver stiffness measurement (LSM) have been developed to rule out varices needing treatment (VNT), including the Baveno VI criteria (B6C), the expanded Baveno VI criteria (EB6C), the LSM-spleen diameter to platelet ratio score (LSPS), and the VariScreen algorithm. We aimed to validate and compare those NITs in patients with compensated advanced chronic liver disease (cACLD). This retrospective study enrolled 354 patients with cACLD; LSM, platelet count (PLT), international normalized ratio (INR), gastroscopy and spleen diameter (SD) were collected. VNT prevalence was 28.5%. In comparison, patients with VNT included higher LSM, INR, and SD and lower PLT. Gastroscopies were spared for 27.7% of patients using the B6C with 1.0% VNT missed rate, 47.2% of patients using the EB6C with 5.9% VNT missed rate, 57.6% of patients using the LSPS with 9.9% VNT missed rate, and 45.5% of patients using the VariScreen algorithm with 3.0% VNT missed rate. Only the B6C and the VariScreen algorithm could safely avoid gastroscopies, and the VariScreen algorithm spared more gastroscopies than the B6C. The results were consistent with the previous when performed subgroup analysis. In conclusion, the VariScreen algorithm performed the best and can be used in clinical. |
format | Online Article Text |
id | pubmed-7794348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77943482021-01-11 Validation and comparison of non-invasive prediction models based on liver stiffness measurement to identify patients who could avoid gastroscopy Hu, Youwen Wen, Zhili Sci Rep Article Several non‐invasive tests (NITs) based on liver stiffness measurement (LSM) have been developed to rule out varices needing treatment (VNT), including the Baveno VI criteria (B6C), the expanded Baveno VI criteria (EB6C), the LSM-spleen diameter to platelet ratio score (LSPS), and the VariScreen algorithm. We aimed to validate and compare those NITs in patients with compensated advanced chronic liver disease (cACLD). This retrospective study enrolled 354 patients with cACLD; LSM, platelet count (PLT), international normalized ratio (INR), gastroscopy and spleen diameter (SD) were collected. VNT prevalence was 28.5%. In comparison, patients with VNT included higher LSM, INR, and SD and lower PLT. Gastroscopies were spared for 27.7% of patients using the B6C with 1.0% VNT missed rate, 47.2% of patients using the EB6C with 5.9% VNT missed rate, 57.6% of patients using the LSPS with 9.9% VNT missed rate, and 45.5% of patients using the VariScreen algorithm with 3.0% VNT missed rate. Only the B6C and the VariScreen algorithm could safely avoid gastroscopies, and the VariScreen algorithm spared more gastroscopies than the B6C. The results were consistent with the previous when performed subgroup analysis. In conclusion, the VariScreen algorithm performed the best and can be used in clinical. Nature Publishing Group UK 2021-01-08 /pmc/articles/PMC7794348/ /pubmed/33420233 http://dx.doi.org/10.1038/s41598-020-80136-0 Text en © The Author(s) 2021 Open Access This 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/. |
spellingShingle | Article Hu, Youwen Wen, Zhili Validation and comparison of non-invasive prediction models based on liver stiffness measurement to identify patients who could avoid gastroscopy |
title | Validation and comparison of non-invasive prediction models based on liver stiffness measurement to identify patients who could avoid gastroscopy |
title_full | Validation and comparison of non-invasive prediction models based on liver stiffness measurement to identify patients who could avoid gastroscopy |
title_fullStr | Validation and comparison of non-invasive prediction models based on liver stiffness measurement to identify patients who could avoid gastroscopy |
title_full_unstemmed | Validation and comparison of non-invasive prediction models based on liver stiffness measurement to identify patients who could avoid gastroscopy |
title_short | Validation and comparison of non-invasive prediction models based on liver stiffness measurement to identify patients who could avoid gastroscopy |
title_sort | validation and comparison of non-invasive prediction models based on liver stiffness measurement to identify patients who could avoid gastroscopy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794348/ https://www.ncbi.nlm.nih.gov/pubmed/33420233 http://dx.doi.org/10.1038/s41598-020-80136-0 |
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