Development of a Non-Invasive Diagnostic Algorithm and Prognostic Prediction Model for Steatotic Liver Disease in JAPAN: A Multicenter Retrospective Large-Scale Database Study

Non-Invasive Diagnosis and Prognostic Modeling Study in Steatotic Liver Disease in JAPAN - SLD-NILDA JAPAN Study

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000060977

Enrollment

2000

Registered

2026-03-31

Start date

2026-04-01

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Patients with Steatotic Liver Disease(SLD)

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patients diagnosed with SLD or suspected SLD Definition of suspected SLD: patients meeting at least one of the following criteria Patients with cardiometabolic risk factors Patients with liver function abnormalities

Exclusion criteria

Exclusion criteria: 1) Patients deemed ineligible by the attending physician 2) Patients who have declined participation following public disclosure of the study

Design outcomes

Primary

Measure	Time frame
Prognosis of Steatotic Liver Disease Using the NILDA Database	—

Secondary

Measure	Time frame
1) To develop predictive models for the progression of liver injury and hepatocarcinogenesis in patients with steatotic liver disease (SLD) using the NILDA database. 2) To evaluate the diagnostic performance of NILDA for liver fibrosis and to determine optimal cutoff values, using liver biopsy and magnetic resonance elastography (MRE) as reference standards. 3) To evaluate the diagnostic performance of NILDA for hepatic steatosis and to determine optimal cutoff values, using liver biopsy and magnetic resonance imaging-proton density fat fraction (MRI-PDFF) as reference standards. 4) To develop a predictive model for identifying patients with high-risk metabolic dysfunction-associated steatohepatitis (MASH), defined as a NAFLD activity score >=4 and liver fibrosis stage >=2.	—

Measure

Time frame

1) To develop predictive models for the progression of liver injury and hepatocarcinogenesis in patients with steatotic liver disease (SLD) using the NILDA database. 2) To evaluate the diagnostic performance of NILDA for liver fibrosis and to determine optimal cutoff values, using liver biopsy and magnetic resonance elastography (MRE) as reference standards. 3) To evaluate the diagnostic performance of NILDA for hepatic steatosis and to determine optimal cutoff values, using liver biopsy and magnetic resonance imaging-proton density fat fraction (MRI-PDFF) as reference standards. 4) To develop a predictive model for identifying patients with high-risk metabolic dysfunction-associated steatohepatitis (MASH), defined as a NAFLD activity score >=4 and liver fibrosis stage >=2.

—

Countries

Japan

Contacts

Public ContactAbe Tamami

Iwate Medical University Division of Gastroenterology and Hepatology, Department of Internal Medicine

mokosantokosan@yahoo.co.jp019-613-7111

Outcome results