MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease)
Conditions
Keywords
MASLD, Liver Fibrosis, Deep Learning, Gated Recurrent Unit, Non-invasive Triage, Longitudinal EHR
Brief summary
This study looks at a new computer program called NIMIT-AI (Neural Inference for Metabolic-liver Integrated Trajectories, Artificial Intelligence) that helps doctors find liver scarring early in patients with fatty liver disease. Fatty liver disease, also called metabolic dysfunction-associated steatotic liver disease (MASLD), is a common condition where fat builds up in the liver. Over time, this can cause scarring (fibrosis). Finding scarring early helps doctors treat it before it gets worse. Right now, doctors use a blood test score called FIB-4 to check for scarring. But this score misses many patients and cannot be calculated when blood test results are incomplete. NIMIT-AI works differently. It reads a patient's blood test results over multiple visits, not just one visit, to spot patterns that suggest liver scarring. It was tested on 969 patients seen at Siriraj Hospital in Bangkok, Thailand between 2018 and 2022. In testing, NIMIT-AI found liver scarring more accurately than FIB-4. It also worked even when some blood test results were missing, which happens often in real clinics. This study did not ask patients to do anything extra. It used health records that were already collected as part of regular care.
Interventions
DIAGNOSTIC_TESTLongitudinal electronic health record analysis
NIMIT-AI, a gated recurrent unit deep learning model, analyzed serial outpatient laboratory results from electronic health records collected over a 5-year observation window (2018-2022) at Siriraj Hospital. The model processed up to 10 sequential visits per patient using 18 clinical features including liver enzymes, metabolic markers, comorbidity flags, and medication exposures to predict liver fibrosis stage without requiring elastography.
Sponsors
Siriraj Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Inclusion criteria
* Age ≥18 years at index visit * Confirmed MASLD diagnosis per Delphi consensus criteria * At least one outpatient visit with concurrent laboratory data and FibroScan liver stiffness measurement within observation window (2018-2022) * Receiving care at Division of Gastroenterology, Faculty of Medicine Siriraj Hospital, Mahidol University
Exclusion criteria
* Alternative chronic liver disease aetiology (autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, Wilson's disease, haemochromatosis) * Chronic viral hepatitis (hepatitis B or C surface antigen positivity) * Prior liver transplantation * Active extrahepatic malignancy at baseline * Insufficient longitudinal data for outcome ascertainment
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Area under the receiver operating characteristic curve (AUROC) for significant fibrosis (F≥2) identification | Assessed at end of observation period (December 2022) |
Secondary
| Measure | Time frame |
|---|---|
| Sensitivity-constrained positive predictive value (PPV) for significant fibrosis (F≥2) at optimised classification threshold | Assessed at end of observation period (December 2022) |
| Diagnostic performance for compensated advanced chronic liver disease (F3-F4 cACLD) reported as one-vs-rest AUROC | Assessed at end of observation period (December 2022) |
| Net reclassification improvement (NRI) of NIMIT-AI versus FIB-4 at guideline-recommended threshold (1.30) | Assessed at end of observation period (December 2022) |
| Integrated discrimination improvement (IDI) of NIMIT-AI versus FIB-4 | Assessed at end of observation period (December 2022) |
| Attention weight distribution across visit positions for temporal interpretability of NIMIT-AI predictions | Assessed at end of observation period (December 2022) |
| SHAP (SHapley Additive exPlanations) feature importance values for global model interpretability across fibrosis classes | Assessed at end of observation period (December 2022) |
Countries
Thailand
Outcome results
None listed