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Machine intelligence to predict death in patients with acute-on-chronic liver failure

Integrated immuno-proteomic, cell death, and clinical trajectories with machine learning to predict outcomes in Acute-on-Chronic Liver Failure patients (IMP-ACLF) - IMP-ACLF

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2022/01/039552
Enrollment
200
Registered
2022-01-19
Start date
Unknown
Completion date
Unknown
Last updated
2022-10-17

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

Conditions

Health Condition 1: K74- Fibrosis and cirrhosis of liver

Interventions

None listed

Sponsors

Post Graduate Institute of Medical Education and Research PGIMER
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: ACLF patients aged 18-80 years according to either APASL criteria or EASL definition will be recruited after informed consent

Exclusion criteria

Exclusion criteria: Patients having HIV infection, pregnant or lactating women, patients with known immunosuppressed state, and having undergone previous organ transplants, and refusing to give consent will be excluded.

Design outcomes

Primary

MeasureTime frame
To understand the dynamic trajectories and pathophysiology of ACLFTimepoint: Baseline, 7 days and 30 days—

Secondary

MeasureTime frame
Derivation and validation of novel AI- based model for the prediction of mortalityTimepoint: Recruitment of validation cohort, data capture & model performance assessment in next 1 year—

Countries

India

Contacts

Public ContactPratibha

PGIMER, Chandigarh

nipun29j@gmail.com9914208562

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026