Johnny T. K. Cheung, Xinrong Zhang, Grace Lai-Hung Wong, Terry Cheuk-Fung Yip, Huapeng Lin, Guanlin Li, Howard Ho-Wai Leung, Jimmy Che-To Lai, Sanjiv Mahadeva, Nik Raihan Nik Mustapha, Xiao-Dong Wang, Wen-Yue Liu, Vincent Wai-Sun Wong, Wah-Kheong Chan, Ming-Hua Zheng
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Cheung, Xinrong Zhang, Grace Lai-Hung Wong, Terry Cheuk-Fung Yip, Huapeng Lin, Guanlin Li, Howard Ho-Wai Leung, Jimmy Che-To Lai, Sanjiv Mahadeva, Nik Raihan Nik Mustapha, Xiao-Dong Wang, Wen-Yue Liu, Vincent Wai-Sun Wong, Wah-Kheong Chan, Ming-Hua Zheng","doi":"10.1111/apt.17722","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Early screening may prevent fibrosis progression in metabolic-associated fatty liver disease (MAFLD).</p>\n </section>\n \n <section>\n \n <h3> Aims</h3>\n \n <p>We developed and validated MAFLD fibrosis score (MFS) for identifying advanced fibrosis (≥F3) among MAFLD patients.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>This cross-sectional, multicentre study consecutively recruited MAFLD patients receiving tertiary care (Malaysia as training cohort [<i>n</i> = 276] and Hong Kong and Wenzhou as validation cohort [<i>n</i> = 431]). Patients completed liver biopsy, vibration-controlled transient elastography (VCTE), and clinical and laboratory assessment within 1 week. We used machine learning to select ‘highly important’ predictors of advanced fibrosis, followed by backward stepwise regression to construct MFS formula.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>MFS was composed of seven variables: age, body mass index, international normalised ratio, aspartate aminotransferase, gamma-glutamyl transpeptidase, platelet count, and history of type 2 diabetes. MFS demonstrated an area under the receiver-operating characteristic curve of 0.848 [95% CI 0.800–898] and 0.823 [0.760–0.886] in training and validation cohorts, significantly higher than aminotransferase-to-platelet ratio index (0.684 [0.603–0.765], 0.663 [0.588–0.738]), Fibrosis-4 index (0.793 [0.735–0.854], 0.737 [0.660–0.814]), and non-alcoholic fatty liver disease fibrosis score (0.785 [0.731–0.844], 0.750 [0.674–0.827]) (DeLong's test <i>p</i> < 0.05). MFS could include 92.3% of patients using dual cut-offs of 14 and 15, with a correct prediction rate of 90.4%, resulting in a larger number of patients with correct diagnosis compared to other scores. A two-step MFS-VCTE screening algorithm demonstrated positive and negative predictive values and overall diagnostic accuracy of 93.4%, 89.5%, and 93.2%, respectively, with only 4.0% of patients classified into grey zone.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>MFS outperforms conventional non-invasive scores in predicting advanced fibrosis, contributing to screening in MAFLD patients.</p>\n </section>\n </div>","PeriodicalId":121,"journal":{"name":"Alimentary Pharmacology & Therapeutics","volume":"58 11-12","pages":"1194-1204"},"PeriodicalIF":6.6000,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/apt.17722","citationCount":"2","resultStr":"{\"title\":\"MAFLD fibrosis score: Using routine measures to identify advanced fibrosis in metabolic-associated fatty liver disease\",\"authors\":\"Johnny T. K. 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Patients completed liver biopsy, vibration-controlled transient elastography (VCTE), and clinical and laboratory assessment within 1 week. We used machine learning to select ‘highly important’ predictors of advanced fibrosis, followed by backward stepwise regression to construct MFS formula.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>MFS was composed of seven variables: age, body mass index, international normalised ratio, aspartate aminotransferase, gamma-glutamyl transpeptidase, platelet count, and history of type 2 diabetes. MFS demonstrated an area under the receiver-operating characteristic curve of 0.848 [95% CI 0.800–898] and 0.823 [0.760–0.886] in training and validation cohorts, significantly higher than aminotransferase-to-platelet ratio index (0.684 [0.603–0.765], 0.663 [0.588–0.738]), Fibrosis-4 index (0.793 [0.735–0.854], 0.737 [0.660–0.814]), and non-alcoholic fatty liver disease fibrosis score (0.785 [0.731–0.844], 0.750 [0.674–0.827]) (DeLong's test <i>p</i> < 0.05). MFS could include 92.3% of patients using dual cut-offs of 14 and 15, with a correct prediction rate of 90.4%, resulting in a larger number of patients with correct diagnosis compared to other scores. 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MAFLD fibrosis score: Using routine measures to identify advanced fibrosis in metabolic-associated fatty liver disease
Background
Early screening may prevent fibrosis progression in metabolic-associated fatty liver disease (MAFLD).
Aims
We developed and validated MAFLD fibrosis score (MFS) for identifying advanced fibrosis (≥F3) among MAFLD patients.
Methods
This cross-sectional, multicentre study consecutively recruited MAFLD patients receiving tertiary care (Malaysia as training cohort [n = 276] and Hong Kong and Wenzhou as validation cohort [n = 431]). Patients completed liver biopsy, vibration-controlled transient elastography (VCTE), and clinical and laboratory assessment within 1 week. We used machine learning to select ‘highly important’ predictors of advanced fibrosis, followed by backward stepwise regression to construct MFS formula.
Results
MFS was composed of seven variables: age, body mass index, international normalised ratio, aspartate aminotransferase, gamma-glutamyl transpeptidase, platelet count, and history of type 2 diabetes. MFS demonstrated an area under the receiver-operating characteristic curve of 0.848 [95% CI 0.800–898] and 0.823 [0.760–0.886] in training and validation cohorts, significantly higher than aminotransferase-to-platelet ratio index (0.684 [0.603–0.765], 0.663 [0.588–0.738]), Fibrosis-4 index (0.793 [0.735–0.854], 0.737 [0.660–0.814]), and non-alcoholic fatty liver disease fibrosis score (0.785 [0.731–0.844], 0.750 [0.674–0.827]) (DeLong's test p < 0.05). MFS could include 92.3% of patients using dual cut-offs of 14 and 15, with a correct prediction rate of 90.4%, resulting in a larger number of patients with correct diagnosis compared to other scores. A two-step MFS-VCTE screening algorithm demonstrated positive and negative predictive values and overall diagnostic accuracy of 93.4%, 89.5%, and 93.2%, respectively, with only 4.0% of patients classified into grey zone.
Conclusion
MFS outperforms conventional non-invasive scores in predicting advanced fibrosis, contributing to screening in MAFLD patients.
期刊介绍：
Alimentary Pharmacology & Therapeutics is a global pharmacology journal focused on the impact of drugs on the human gastrointestinal and hepato-biliary systems. It covers a diverse range of topics, often with immediate clinical relevance to its readership.
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