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

Author:

Cheung Johnny T. K.1,Zhang Xinrong12,Wong Grace Lai‐Hung12ORCID,Yip Terry Cheuk‐Fung12ORCID,Lin Huapeng12,Li Guanlin12,Leung Howard Ho‐Wai3,Lai Jimmy Che‐To12,Mahadeva Sanjiv4ORCID,Nik Mustapha Nik Raihan5,Wang Xiao‐Dong6,Liu Wen‐Yue7,Wong Vincent Wai‐Sun12ORCID,Chan Wah‐Kheong4ORCID,Zheng Ming‐Hua68ORCID

Affiliation:

1. Medical Data Analytics Centre, Department of Medicine and Therapeutics The Chinese University of Hong Kong Hong Kong China

2. State Key Laboratory of Digestive Disease, Institute of Digestive Disease The Chinese University of Hong Kong Hong Kong China

3. Department of Anatomical and Cellular Pathology The Chinese University of Hong Kong Hong Kong China

4. Gastroenterology and Hepatology Unit, Department of Medicine, Faculty of Medicine University of Malaya Kuala Lumpur Malaysia

5. Department of Pathology Hospital Sultanah Bahiyah Alor Setar Malaysia

6. Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province Wenzhou China

7. Department of Endocrinology the First Affiliated Hospital of Wenzhou Medical University Wenzhou China

8. MAFLD Research Centre, Department of Hepatology the First Affiliated Hospital of Wenzhou Medical University Wenzhou China

Abstract

SummaryBackgroundEarly screening may prevent fibrosis progression in metabolic‐associated fatty liver disease (MAFLD).AimsWe developed and validated MAFLD fibrosis score (MFS) for identifying advanced fibrosis (≥F3) among MAFLD patients.MethodsThis 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.ResultsMFS 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.ConclusionMFS outperforms conventional non‐invasive scores in predicting advanced fibrosis, contributing to screening in MAFLD patients.

Funder

Chinese University of Hong Kong

Publisher

Wiley

Subject

Pharmacology (medical),Gastroenterology,Hepatology

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