Machine learning improves the prediction of significant fibrosis in Asian patients with metabolic dysfunction-associated steatotic liver disease - The Gut and Obesity in Asia (GO-ASIA) Study

Verma N 1 , Duseja A 1 , Mehta M 1 , De A 1 , Lin H 2 , Wong VW 2 Show all authors , Wong GL 2 , Rajaram RB 3 , Chan WK 3 , Mahadeva S 3 , Zheng MH 4 , Liu WY 5 , Treeprasertsuk S 6 , Prasoppokakorn T 6 , Kakizaki S 7 , Seki Y 8 , Kasama K 8 , Charatcharoenwitthaya P 9 , Sathirawich P 9 , Kulkarni A 10 , Purnomo HD 11 , Kamani L 12 , Lee YY 13 , Wong MS 13 , Tan EXX 14 , Young DY 14

Affiliations 

  • 1 Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
  • 2 Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
  • 3 Gastroenterology and Hepatology Unit, Department of Medicine, Faculty of Medicine, University of Malaya Medical Centre, Kuala Lumpur, Malaysia
  • 4 NAFLD Research Centre Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
  • 5 Department of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
  • 6 Division of Gastroenterology, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand
  • 7 Department of Clinical Research, National Hospital Organization Takasaki General Medical Centre, Takasaki, Japan
  • 8 Weight Loss and Metabolic Surgery Centre, Yotsuya Medical Cube, Tokyo, Japan
  • 9 Division of Gastroenterology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
  • 10 Asian Institute of Gastroenterology Hospital, Hyderabad, India
  • 11 Faculty of Medicine, Diponegoro University, Kariadi Hospital, Semarang, Indonesia
  • 12 National Medical Centre, Karachi, Pakistan
  • 13 School of Medical Sciences Universiti Sains Malaysia, Kota Bharu, Malaysia
  • 14 Department of Medicine, National University Singapore, Singapore, Singapore
Aliment Pharmacol Ther, 2024 Mar;59(6):774-788.
PMID: 38303507 DOI: 10.1111/apt.17891

Abstract

BACKGROUND: The precise estimation of cases with significant fibrosis (SF) is an unmet goal in non-alcoholic fatty liver disease (NAFLD/MASLD).

AIMS: We evaluated the performance of machine learning (ML) and non-patented scores for ruling out SF among NAFLD/MASLD patients.

METHODS: Twenty-one ML models were trained (N = 1153), tested (N = 283), and validated (N = 220) on clinical and biochemical parameters of histologically-proven NAFLD/MASLD patients (N = 1656) collected across 14 centres in 8 Asian countries. Their performance for detecting histological-SF (≥F2fibrosis) were evaluated with APRI, FIB4, NFS, BARD, and SAFE (NPV/F1-score as model-selection criteria).

RESULTS: Patients aged 47 years (median), 54.6% males, 73.7% with metabolic syndrome, and 32.9% with histological-SF were included in the study. Patients with SFvs.no-SF had higher age, aminotransferases, fasting plasma glucose, metabolic syndrome, uncontrolled diabetes, and NAFLD activity score (p  140) was next best in ruling out SF (NPV of 0.757, 0.724 and 0.827 in overall, test and validation set).

CONCLUSIONS: ML with clinical, anthropometric data and simple blood investigations perform better than FIB-4 for ruling out SF in biopsy-proven Asian NAFLD/MASLD patients.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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