MAFLD fibrosis score: Using routine measures to identify advanced fibrosis in metabolic-associated fatty liver disease

被引:11
作者
Cheung, Johnny T. K. [1 ]
Zhang, Xinrong [1 ,2 ]
Wong, Grace Lai-Hung [1 ,2 ]
Yip, Terry Cheuk-Fung [1 ,2 ]
Lin, Huapeng [1 ,2 ]
Li, Guanlin [1 ,2 ]
Leung, Howard Ho-Wai [3 ]
Lai, Jimmy Che-To [1 ,2 ]
Mahadeva, Sanjiv [4 ]
Mustapha, Nik Raihan Nik [5 ]
Wang, Xiao-Dong [6 ]
Liu, Wen-Yue [7 ]
Wong, Vincent Wai-Sun [1 ,2 ,11 ]
Chan, Wah-Kheong [4 ,10 ]
Zheng, Ming-Hua [6 ,8 ,9 ]
机构
[1] Chinese Univ Hong Kong, Med Data Analyt Ctr, Dept Med & Therapeut, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Inst Digest Dis, State Key Lab Digest Dis, Hong Kong, Peoples R China
[3] Chinese Univ Hong Kong, Dept Anat & Cellular Pathol, Hong Kong, Peoples R China
[4] Univ Malaya, Fac Med, Dept Med, Gastroenterol & Hepatol Unit, Kuala Lumpur, Malaysia
[5] Hosp Sultanah Bahiyah, Dept Pathol, Alor Setar, Malaysia
[6] Key Lab Diag & Treatment Dev Chron Liver Dis Zhej, Wenzhou, Peoples R China
[7] Wenzhou Med Univ, Affiliated Hosp 1, Dept Endocrinol, Wenzhou, Peoples R China
[8] Wenzhou Med Univ, Affiliated Hosp 1, NAFLD Res Ctr, Dept Hepatol, Wenzhou, Peoples R China
[9] Wenzhou Med Univ, Affiliated Hosp 1, MAFLD Res Ctr, Dept Hepatol, 2 Fuxue Lane, Wenzhou 325000, Peoples R China
[10] Univ Malaya, Fac Med, Dept Med, Gastroenterol & Hepatol Unit, Kuala Lumpur 50603, Malaysia
[11] Chinese Univ Hong Kong, Dept Med & Therapeut, 9/F Prince Wales Hosp 30-32 Ngan Shing Str, Hong Kong, Peoples R China
关键词
SIMPLE NONINVASIVE INDEX; NONALCOHOLIC STEATOHEPATITIS; PREVALENCE; MANAGEMENT; PREDICT; SYSTEM;
D O I
10.1111/apt.17722
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
Background: Early screening may prevent fibrosis progression in metabolic-associated fatty liver disease (MAFLD).AimsWe developed and validated MAFLD fibrosis score (MFS) for identifying advanced fibrosis (& GE;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.
引用
收藏
页码:1194 / 1204
页数:11
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