Affiliation:
1. Guangdong Provincial Key Laboratory of Viral Hepatitis Research Guangdong Provincial Clinical Research Center for Viral Hepatitis Key Laboratory of Infectious Diseases Research in South China Ministry of Education Department of Infectious Diseases Nanfang Hospital Southern Medical University Guangzhou China
2. Medical Data Analytics Center Department of Medicine and Therapeutics The Chinese University of Hong Kong Hong Kong China
3. State Key Laboratory of Digestive Disease Institute of Digestive Disease The Chinese University of Hong Kong Hong Kong China
4. Department of Internal Medicine Virginia Commonwealth University Richmond Virginia United States
5. Department of Endocrinology The First Affiliated Hospital of Wenzhou Medical University Wenzhou China
6. MAFLD Research Center Department of Hepatology The First Affiliated Hospital of Wenzhou Medical University Wenzhou China
7. Department of Anatomical and Cellular Pathology The Chinese University of Hong Kong Hong Kong China
8. Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province Wenzhou China
9. Division of Gastroenterology Virginia Commonwealth University Richmond Virginia USA
Abstract
AbstractBackground & AimsaMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B‐related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction‐associated steatotic liver disease (MASLD; formerly NAFLD)‐related fibrosis diagnosis and establish a machine‐learning (ML) model to improve the diagnostic performance.MethodsA total of 946 biopsy‐proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (N = 703). The performance of ML models was evaluated in the external validation set (N = 125).ResultsThe AUROCs of aMAP versus fibrosis‐4 index (FIB‐4) and aspartate aminotransferase‐platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [P = 0.734], 0.735 [P = 0.001]) and (0.759 vs. 0.795 [P = 0.027], 0.709 [P = 0.049]). When using dual cut‐off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB‐4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM‐plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM‐plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM‐plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively).ConclusionsThe aMAP score is capable of diagnosing MASLD‐related fibrosis. The LSM‐plus model could accurately identify MASLD‐related cirrhosis and advanced fibrosis.
Funder
National Basic Research Program of China
National Natural Science Foundation of China
Cited by
2 articles.
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