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Development of a computer-powered risk score to predict success after living donor liver transplantation

Development of a Machine Learning-based Risk Index for Living Donor Liver Transplantation (LDLT) Outcomes - Nil

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2026/04/109662
Enrollment
7500
Registered
2026-04-28
Start date
Unknown
Completion date
Unknown
Last updated
2026-06-01

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

Conditions

Health Condition 1: K77- Liver disorders in diseases classified elsewhere

Interventions

Intervention1: Nil: Nil Intervention2: Nil: Nil

Sponsors

Aster CMI Hospital
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: 1 Adult recipients greater than or equal to 18 years of living donor liver transplants. 2 Availability of detailed donor, recipient, and graft data. 3 Documented outcomes post-surgically upto 7,30 days and 3,6,12 months.

Exclusion criteria

Exclusion criteria: 1 Recipients less than 18 years. 2 Recipients of combined organ transplants. 3 Incomplete or missing key outcome or predictor variables.

Design outcomes

Primary

MeasureTime frame
To develop and validate a machine learning-based Living Donor Liver Transplantation Risk Index to predict the composite risk of early allograft dysfunction,vascular complications and biliary complications, recipient death at 30 days and 1 year post-transplant or retransplantation.Timepoint: 7 days , 30 days , 3 months, 6 months, 12 months—

Secondary

MeasureTime frame
To identify and rank donor, recipient, and graft-related and operative variables that are most predictive of transplant outcomes using SHAP (SHapley Additive exPlanations) analysis. To compare performance of multiple machine learning models, including advanced neural networks and conventional supervised learning methods, for outcome prediction. To build and deploy an interactive, clinician-facing dashboard for individualized donor-recipient risk prediction using real-time input data. To assess the utility of the developed model in improving donor-recipient matching and decision-making across Indian liver transplant centers. variables include graft factors, surgical details , blood product requirement ,Intra-op death etc. program factors include volume , graft failures,re-transplants, labs,malignancies , NODAT, Dialysis dependency, CRRT , renal failure, reequirement of renal transplant etc,Timepoint: 30 days upto 1 year—

Countries

India

Contacts

Public ContactDr Sonal Asthana

Aster CMI

drsonal.asthana@asterhospital.in09686976379

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Jun 11, 2026