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Artificial neural network analysis in predicting mortality in patients with ACLF

Role of neural network technology in predicting mortality in patients with acute on chronic liver failure

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2021/05/033425
Enrollment
120
Registered
2021-05-06
Start date
Unknown
Completion date
Unknown
Last updated
2021-11-24

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

Conditions

Health Condition 1: K721- Chronic hepatic failure

Interventions

None listed

Sponsors

Balaji
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: All inpatients admitted with diagnosis of ACLF

Exclusion criteria

Exclusion criteria: Patients diagnosed with other organ failures unrelated to present ailment which can influence the outcome of this study o Patients diagnosed with HIV/Malignancy or pregnancy

Design outcomes

Primary

MeasureTime frame
Use of artificial neural network analysis in prediction of mortality based on various scores in patients with ACLFTimepoint: 3 months—

Secondary

MeasureTime frame
Prediction of mortality of patients with ACLFTimepoint: 1 month and 3 months—

Countries

India

Contacts

Public ContactBalaji M

Department of Gastroenterology

balajimbmc@gmail.com

Outcome results

None listed

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