Discrimination of DILI and AIH by Artificial Intelligence

Study Overview
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Brief Summary
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A retrospective, multi-center, non-interventional cohort study has been going to explore whether artificial intelligence can discriminate Drug-induced liver injury and Autoimmune hepatitis. A machine learning-based tool will be developed and validated to help clinicians to differentiate between Drug-induced liver injury and Autoimmune hepatitis

Detailed Description
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Research Objectives: To develop a machine learning-based model from retrospective data. To validate the machine learning-based model from internal dataset and external datasets nationwide. To setup a website or application based on the above model to discriminate Drug-induced liver injury and Autoimmune hepatitis.

Official Title
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Development and Validation of a Machine Learning Model to Differentiate Drug-induced Liver Injury and Autoimmune Hepatitis

Conditions
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Drug-induced Liver Injury, Autoimmune Hepatitis

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Drug-induced liver injury RUCAM ≥6 and met one of the following biochemical conditions: a.) ALT≥5 ULN, b.) or ALP ≥2 ULN, iii) or ALT≥3 ULN and TBil≥2 ULN. RUCAM was between 3-5, the medical records were further reviewed by the three authors to determine the eligibility. Autoimmune hepatitis The revised International Autoimmune Hepatitis Group (IAIHG) diagnostic score≥6 points. Liver biopsy available, which is compatible with typical features of AIH. If liver biopsy was unavailable, patients who achieved biochemical resolution after sustained immunosuppressive therapy. Exclusion Criteria: Drug-induced liver injury Hepatotropic viral infection: hepatitis A, B, C, D and E. Non-hepatotropic viral infection: cytomegalovirus (CMV) and Epstein-Barr virus (EBV), etc. Hypoxic ischemic hepatitis and congestive liver disease. Alcohol consumption: male >40g/d, female >20g/d, and ≥5 years. Biliary obstruction, primary biliary cholangitis; primary sclerosing cholangitis. Autoimmune hepatitis. Parasitic infection. Sepsis. Previous liver transplantation or bone marrow transplantation. Pregnancy or lactation. Genetic and metabolic liver diseases. Autoimmune hepatitis Hepatotropic viral infection: hepatitis A, B, C, D and E. Non-hepatotropic viral infection: cytomegalovirus (CMV) and Epstein-Barr virus (EBV), etc. Hypoxic ischemic hepatitis and congestive liver disease. Alcohol consumption: male >40g/d, female >20g/d, and ≥5 years. Biliary obstruction, primary biliary cholangitis; primary sclerosing cholangitis. Drug-induced liver injury. Parasitic infection. Sepsis. Previous liver transplantation or bone marrow transplantation. Pregnancy or lactation. Genetic and metabolic liver diseases.
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Development group, retrospectively collecting<br>The training set includes 80% of the development dataset from Beijing Friendship Hospital (Retrospectively collecting DILI 649 cases and AIH 180 cases in total) | |
| Validation group, retrospectively collecting<br>The internal validation group includes 20% of the development dataset from Beijing Friendship Hospital (Retrospectively collecting DILI 649 cases and AIH 180 cases in total). External validation groups include Fifth Medical Center of Chinese PLA Medical Center(Retrospectively collecting DILI 585 cases and AIH 400 cases in total), Beijing You'an Hospital (Retrospectively collecting DILI 266 cases and AIH 100 cases in total) and additional seven tertiary hospitals throughout China (Retrospectively collecting DILI 182 cases and AIH 92 cases in total, including Tianjin Second People's Hospital, Heilongjiang Provincial Hospital, Affiliated Hospital of Qingdao University, the First Affiliated Hospital of Xiamen University, Traditional Chinese Medical Hospital of Xinjiang Uygur Autonomous Region, Lanzhou University Second Hospital, Qinghai Provincial People's Hospital). | |
| Real-World validation group, prospectively and retrospectively collecting<br>Real-World validation data will be prospectively and retrospectively collected from Beijing Friendship Hospital (78 cases for DILI and 51 cases for AIH) | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accuracy of the model in the differential diagnosis of DILI and AIH | The ratio of the correct number of forecasts to the total number of forecasts | May 31, 2023 | 
| The confidence of the model in the differential diagnosis of DILI and AIH | The confidence and 95% confidence internal of the model in determining whether each case is DILI or AIH | May 31, 2023 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial intelligence, Differential diagnose