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Performance of the Enhanced Liver Fibrosis Score, Comparison with Vibration-controlled Transient Elastography Data, and Development of a Simple Algorithm to Predict Significant Liver Fibrosis in a Community-based Liver Service: A Retrospective Evaluation
BACKGROUND AND AIMS: Liver fibrosis is a key risk factor for cirrhosis, hepatocellular carcinoma and end stage liver failure. The National Institute for Health and Care Excellence guidelines for assessment for advanced (≥F3) liver fibrosis in people with nonalcoholic fatty liver disease recommend th...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
XIA & HE Publishing Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318295/ https://www.ncbi.nlm.nih.gov/pubmed/37408822 http://dx.doi.org/10.14218/JCTH.2022.00335 |
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author | Reinson, Tina Patel, Janisha Mathews, Mead Fountain, Derek Buchanan, Ryan M. Byrne, Christopher D. |
author_facet | Reinson, Tina Patel, Janisha Mathews, Mead Fountain, Derek Buchanan, Ryan M. Byrne, Christopher D. |
author_sort | Reinson, Tina |
collection | PubMed |
description | BACKGROUND AND AIMS: Liver fibrosis is a key risk factor for cirrhosis, hepatocellular carcinoma and end stage liver failure. The National Institute for Health and Care Excellence guidelines for assessment for advanced (≥F3) liver fibrosis in people with nonalcoholic fatty liver disease recommend the use of enhanced liver fibrosis (ELF) test, followed by vibration-controlled transient elastography (VCTE). Performance of ELF at predicting significant (≥F2) fibrosis in real-world practice is uncertain. To assess the accuracy of ELF using VCTE; investigate the optimum ELF cutoff value to identify ≥F2 and ≥F3; and develop a simple algorithm, with and without ELF score, for detecting ≥F2. METHODS: Retrospective evaluation of patients referred to a Community Liver Service for VCTE, Jan-Dec 2020. Assessment included: body mass index (BMI), diabetes status, alanine aminotransferase (ALT) levels, ELF score and biopsy-validated fibrosis stages according to VCTE. RESULTS: Data from 273 patients were available. n=110 patients had diabetes. ELF showed fair performance for ≥F2 and ≥F3, area under the curve (AUC) = 0.70, 95% confidence interval (CI) 0.64–0.76 and AUC=0.72, 95% CI: 0.65–0.79 respectively. For ≥F2 Youden’s index for ELF=9.85 and for ≥F3, ELF=9.95. Combining ALT, BMI, and HbA1c (ALBA algorithm) to predict ≥F2 showed good performance (AUC=0.80, 95% CI: 0.69–0.92), adding ALBA to ELF improved performance (AUC=0.82, 95% CI: 0.77–0.88). Results were independently validated. CONCLUSIONS: Optimal ELF cutoff for ≥F2 is 9.85 and 9.95 for ≥F3. ALT, BMI, and HbA1c (ALBA algorithm) can stratify patients at risk of ≥F2. ELF performance is improved by adding ALBA. |
format | Online Article Text |
id | pubmed-10318295 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | XIA & HE Publishing Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103182952023-07-05 Performance of the Enhanced Liver Fibrosis Score, Comparison with Vibration-controlled Transient Elastography Data, and Development of a Simple Algorithm to Predict Significant Liver Fibrosis in a Community-based Liver Service: A Retrospective Evaluation Reinson, Tina Patel, Janisha Mathews, Mead Fountain, Derek Buchanan, Ryan M. Byrne, Christopher D. J Clin Transl Hepatol Original Article BACKGROUND AND AIMS: Liver fibrosis is a key risk factor for cirrhosis, hepatocellular carcinoma and end stage liver failure. The National Institute for Health and Care Excellence guidelines for assessment for advanced (≥F3) liver fibrosis in people with nonalcoholic fatty liver disease recommend the use of enhanced liver fibrosis (ELF) test, followed by vibration-controlled transient elastography (VCTE). Performance of ELF at predicting significant (≥F2) fibrosis in real-world practice is uncertain. To assess the accuracy of ELF using VCTE; investigate the optimum ELF cutoff value to identify ≥F2 and ≥F3; and develop a simple algorithm, with and without ELF score, for detecting ≥F2. METHODS: Retrospective evaluation of patients referred to a Community Liver Service for VCTE, Jan-Dec 2020. Assessment included: body mass index (BMI), diabetes status, alanine aminotransferase (ALT) levels, ELF score and biopsy-validated fibrosis stages according to VCTE. RESULTS: Data from 273 patients were available. n=110 patients had diabetes. ELF showed fair performance for ≥F2 and ≥F3, area under the curve (AUC) = 0.70, 95% confidence interval (CI) 0.64–0.76 and AUC=0.72, 95% CI: 0.65–0.79 respectively. For ≥F2 Youden’s index for ELF=9.85 and for ≥F3, ELF=9.95. Combining ALT, BMI, and HbA1c (ALBA algorithm) to predict ≥F2 showed good performance (AUC=0.80, 95% CI: 0.69–0.92), adding ALBA to ELF improved performance (AUC=0.82, 95% CI: 0.77–0.88). Results were independently validated. CONCLUSIONS: Optimal ELF cutoff for ≥F2 is 9.85 and 9.95 for ≥F3. ALT, BMI, and HbA1c (ALBA algorithm) can stratify patients at risk of ≥F2. ELF performance is improved by adding ALBA. XIA & HE Publishing Inc. 2023-08-28 2023-02-24 /pmc/articles/PMC10318295/ /pubmed/37408822 http://dx.doi.org/10.14218/JCTH.2022.00335 Text en © 2023 Authors. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Reinson, Tina Patel, Janisha Mathews, Mead Fountain, Derek Buchanan, Ryan M. Byrne, Christopher D. Performance of the Enhanced Liver Fibrosis Score, Comparison with Vibration-controlled Transient Elastography Data, and Development of a Simple Algorithm to Predict Significant Liver Fibrosis in a Community-based Liver Service: A Retrospective Evaluation |
title | Performance of the Enhanced Liver Fibrosis Score, Comparison with Vibration-controlled Transient Elastography Data, and Development of a Simple Algorithm to Predict Significant Liver Fibrosis in a Community-based Liver Service: A Retrospective Evaluation |
title_full | Performance of the Enhanced Liver Fibrosis Score, Comparison with Vibration-controlled Transient Elastography Data, and Development of a Simple Algorithm to Predict Significant Liver Fibrosis in a Community-based Liver Service: A Retrospective Evaluation |
title_fullStr | Performance of the Enhanced Liver Fibrosis Score, Comparison with Vibration-controlled Transient Elastography Data, and Development of a Simple Algorithm to Predict Significant Liver Fibrosis in a Community-based Liver Service: A Retrospective Evaluation |
title_full_unstemmed | Performance of the Enhanced Liver Fibrosis Score, Comparison with Vibration-controlled Transient Elastography Data, and Development of a Simple Algorithm to Predict Significant Liver Fibrosis in a Community-based Liver Service: A Retrospective Evaluation |
title_short | Performance of the Enhanced Liver Fibrosis Score, Comparison with Vibration-controlled Transient Elastography Data, and Development of a Simple Algorithm to Predict Significant Liver Fibrosis in a Community-based Liver Service: A Retrospective Evaluation |
title_sort | performance of the enhanced liver fibrosis score, comparison with vibration-controlled transient elastography data, and development of a simple algorithm to predict significant liver fibrosis in a community-based liver service: a retrospective evaluation |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318295/ https://www.ncbi.nlm.nih.gov/pubmed/37408822 http://dx.doi.org/10.14218/JCTH.2022.00335 |
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