Metabolic Dysfunction-associated Fatty Liver Disease
Conditions
Brief summary
This is a case-control study that aims to build a predictive model for MAFLD based on machine learning.
Detailed description
Metabolic dysfunction-associated fatty liver disease (MAFLD) also known as non-alcoholic fatty liver disease (NAFLD), is one of the most prevalent liver diseases worldwide with high prevalence and economic burden, which affects 25% of global adult population. Despite extensive research on understanding the inner pathophysiology of MAFLD, it still keep growing with no approval therapy. Therefore, preventive measures are particularly important in diagnosing MAFLD. So far the liver biopsy is still the gold standard for diagnosis of MAFLD, however considering the invasive process and potential risks, it still has low acceptance for asymptomatic patients, thus non-invasive methods are necessary for this reason. The purpose of this study is to establish a prediction model to identify MAFLD patients, which can accurately predict whether the participants have MAFLD according to the relevant metabolic indicators of the participants, without the need for invasive examinations such as tissue biopsy.
Interventions
None listed
Sponsors
Zhejiang Chinese Medical University
The First Affiliated Hospital of Zhejiang Chinese Medical University
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
Yes
Inclusion criteria
1. aged 18 to 75 years; 2. meeting the diagnostic criteria of MAFLD; 3. no other organic lesions were found in imaging examination; 4. willing and able to sign informed consent.
Exclusion criteria
1. significant drinking history (weekly alcohol consumption ≥ 140g for male, or weekly alcohol consumption ≥ 70g for female); 2. presence of evidence for having hepatic steatosis, viral hepatitis, history of hepatic cancer, drug-induced liver injury, liver cirrhosis and other liver and biliary tract diseases; 3. major organ malfunction, severe systemic illnesses, mental health issues, or inability to complete examination; 4. pregnant or pregnancy planning female; 5. missing of important clinical data.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area under cure | 2022-2024 | Area under cure(AUC) was defined as the area enclosed by the coordinate axis under the receiver operating characteristic curve, with values ranging from 0.5 to 1.0. The closer the AUC is to 1.0, the higher the authenticity of the detection method; the closer to 0.5, the lower the authenticity of the detection method; when equal to 0.5, the authenticity is the lowest and has no application value. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy | 2022-2024 | The proportion of the number of correctly classified samples to the total number of samples. |
| Precision | 2022-2024 | The proportion of data that is actually positive among the data that is determined to be positive. |
Countries
China
Outcome results
None listed