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
Issued Date
2024-01-01
Resource Type
ISSN
02692813
eISSN
13652036
Scopus ID
2-s2.0-85184258058
Pubmed ID
38303507
Journal Title
Alimentary Pharmacology and Therapeutics
Rights Holder(s)
SCOPUS
Bibliographic Citation
Alimentary Pharmacology and Therapeutics (2024)
Suggested Citation
Verma N., Duseja A., Mehta M., De A., Lin H., Wong V.W.S., Wong G.L.H., Rajaram R.B., Chan W.K., Mahadeva S., Zheng M.H., Liu W.Y., Treeprasertsuk S., Prasoppokakorn T., Kakizaki S., Seki Y., Kasama K., Charatcharoenwitthaya P., Sathirawich P., Kulkarni A., Purnomo H.D., Kamani L., Lee Y.Y., Wong M.S., Tan E.X.X., Young D.Y. 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. Alimentary Pharmacology and Therapeutics (2024). doi:10.1111/apt.17891 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97185
Title
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
Author(s)
Verma N.
Duseja A.
Mehta M.
De A.
Lin H.
Wong V.W.S.
Wong G.L.H.
Rajaram R.B.
Chan W.K.
Mahadeva S.
Zheng M.H.
Liu W.Y.
Treeprasertsuk S.
Prasoppokakorn T.
Kakizaki S.
Seki Y.
Kasama K.
Charatcharoenwitthaya P.
Sathirawich P.
Kulkarni A.
Purnomo H.D.
Kamani L.
Lee Y.Y.
Wong M.S.
Tan E.X.X.
Young D.Y.
Duseja A.
Mehta M.
De A.
Lin H.
Wong V.W.S.
Wong G.L.H.
Rajaram R.B.
Chan W.K.
Mahadeva S.
Zheng M.H.
Liu W.Y.
Treeprasertsuk S.
Prasoppokakorn T.
Kakizaki S.
Seki Y.
Kasama K.
Charatcharoenwitthaya P.
Sathirawich P.
Kulkarni A.
Purnomo H.D.
Kamani L.
Lee Y.Y.
Wong M.S.
Tan E.X.X.
Young D.Y.
Author's Affiliation
Siriraj Hospital
The First Affiliated Hospital of Wenzhou Medical University
Universitas Diponegoro
King Chulalongkorn Memorial Hospital
National University of Singapore
Asian Institute of Gastroenterology India
University of Malaya Medical Centre
Chinese University of Hong Kong
School of Medical Sciences, Universiti Sains Malaysia
Postgraduate Institute of Medical Education & Research, Chandigarh
Weight Loss and Metabolic Surgery Center
National Hospital Organization Takasaki General Medical Centre
National Medical Centre
The First Affiliated Hospital of Wenzhou Medical University
Universitas Diponegoro
King Chulalongkorn Memorial Hospital
National University of Singapore
Asian Institute of Gastroenterology India
University of Malaya Medical Centre
Chinese University of Hong Kong
School of Medical Sciences, Universiti Sains Malaysia
Postgraduate Institute of Medical Education & Research, Chandigarh
Weight Loss and Metabolic Surgery Center
National Hospital Organization Takasaki General Medical Centre
National Medical Centre
Corresponding Author(s)
Other Contributor(s)
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 < 0.001, each). ML models showed 7%–12% better discrimination than FIB-4 to detect SF. Optimised random forest (RF) yielded best NPV/F1 in overall set (0.947/0.754), test set (0.798/0.588) and validation set (0.852/0.559), as compared to FIB4 in overall set (0.744/0.499), test set (0.722/0.456), and validation set (0.806/0.507). Compared to FIB-4, RF could pick 10 times more patients with SF, reduce unnecessary referrals by 28%, and prevent missed referrals by 78%. Age, AST, ALT fasting plasma glucose, and platelet count were top features determining the SF. Sequential use of SAFE < 140 and FIB4 < 1.2 (when SAFE > 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.