Construction and analysis of a noninvasive diagnostic model for steatohepatitis in metabolic associated fatty liver disease

Zhiheng CHEN; Bowen GAO; Bei GUI; Meijie SHI; Huanming XIAO; Yubao XIE; Sheng LI; Xiaoling CHI

doi:10.3969/j.issn.1001-5256.2023.08.014

Volume 39 Issue 8

Aug. 2023

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Article Navigation > Journal of Clinical Hepatology > 2023 > 39(8): 1857-1866

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Construction and analysis of a noninvasive diagnostic model for steatohepatitis in metabolic associated fatty liver disease

DOI: 10.3969/j.issn.1001-5256.2023.08.014

Zhiheng CHEN¹,
Bowen GAO¹,
Bei GUI¹,
Meijie SHI^{2, 3},
Huanming XIAO^{2, 3},
Yubao XIE^{2, 3},
Sheng LI^{2, 3, 4},
Xiaoling CHI^{2, 3
,
,
,}

1.
The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou 510006, China
2.
Department of Hepatology, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou 510006, China
3.
The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510006, China
4.
State Key Laboratory of Dampness Syndrome of Traditional Chinese Medicine Jointly Built by Province and Ministry, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510120, China

Research funding:

The Thirteenth Five-Year Plan for Major and Special Programs of the National Science and Technology of China (2018ZX10725506-003);

The Thirteenth Five-Year Plan for Major and Special Programs of the National Science and Technology of China (2018ZX10725505-004);

The Specific Research Fund for TCM Science and Technology of Guangdong Provincial Hospital of Chinese Medicine (YN2022DB04);

The Specific Research Fund for TCM Science and Technology of Guangdong Provincial Hospital of Chinese Medicine (YN10101903);

Chi Xiaoling National Famous Traditional Chinese Medicine Expert Inheritance Studio (Teaching Letter from State Traditional Chinese Medicine Office (2022-75));

Open Project of State Key Laboratory of Dampness Syndrome of Chinese Medicine (SZ2021KF08)

More Information

Corresponding author: CHI Xiaoling, chixiaolingqh@163.com (ORCID: 0000-0003-3193-1943)
Received Date: 2022-12-30
Accepted Date: 2023-02-13
Published Date: 2023-08-20

Abstract

Abstract

Objective To establish a diagnostic model for steatohepatitis in metabolic associated fatty liver disease (MAFLD) based on LASSO and logistic regression analyses by using general clinical data, serological parameters, and noninvasive liver elastography, and to evaluate the diagnostic value of this model. Methods A total of 299 patients who were diagnosed with MAFLD and underwent liver biopsy in Guangdong Provincial Hospital of Traditional Chinese Medicine from January 2018 to December 2021 were enrolled as subjects, and according to NAS score, they were divided into steatohepatitis group with 170 patients and non-steatohepatitis group with 129 patients. The LASSO regression analysis and the multivariate logistic regression analysis were used to identify the influencing factors for steatohepatitis in MAFLD, and a noninvasive diagnostic model was established, visualized in the form of nomogram, and internally validated by the enhanced Bootstrap method. The receiver operating characteristic (ROC) curve and the calibration curve were plotted for the model, and its diagnostic efficacy was observed in the MAFLD+NAFLD and MAFLD+cHBVi subgroups, which was then compared with other diagnostic models. The chi-square test was used for comparison of categorical data between groups; the independent-samples t test was used for comparison of normally distributed continuous data between groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups. A multivariate logistic regression analysis was used to determine optimal diagnostic factors, and a nomogram diagnostic model was established; the ROC curve was plotted, and the area under the ROC curve (AUC) was calculated; the enhanced Bootstrap method was used for internal validation of the model, and the calibration curve was plotted to show the level of calibration. Results There were significant differences between the two groups in body mass index (BMI), alanine aminotransferase, aspartate aminotransferase (AST), adenosine deaminase, alkaline phosphatase, gamma-glutamyl transpeptidase, total bile acid, total carbon dioxide concentration, uric acid, HbA1c (all P < 0.05). As for FibroScan, there were significant differences between the two groups in liver stiffness measurement (LSM) and controlled attenuation parameter (CAP) (both P < 0.001); as for pathology, there were significant differences between the two groups in fibrosis degree, steatosis score, lobular inflammation score, ballooning degeneration score, and total NAS score (all P < 0.001). In the subgroup analysis, there were 63 patients with steatohepatitis and 48 patients without steatohepatitis in the MAFLD+NAFLD group, and there were 90 patients with steatohepatitis and 71 patients without steatohepatitis in the MAFLD+cHBVi group. The LASSO regression analysis showed that LSM, CAP, BMI, and AST were the best diagnostic factors for the presence or absence of steatohepatitis in MAFLD patients, and the LCBA model was established based on these indices. The LCBA model showed an AUC of 0.816 in the total MAFLD population, 0.866 in the MAFLD+NAFLD population, and 0.764 in the MAFLD+cHBVi population (all P < 0.001), and comparisons based on the ROC curve showed that they were superior to the acNASH, HSI, and NFS models. Conclusion The LCBA model has a stable performance in the diagnosis of steatohepatitis in patients with MAFLD and is superior to acNASH, HSI, and NFS. Therefore, it holds promise for clinical application.
- Non-alcoholic Fatty Liver Disease,
- Metabolic Associated Fatty Liver Disease,
- Diagnosis

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References(23)

References

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Construction and analysis of a noninvasive diagnostic model for steatohepatitis in metabolic associated fatty liver disease

DOI: 10.3969/j.issn.1001-5256.2023.08.014