Prediction Models for LDLT Outcomes (PREDICTLDLT)

February 2, 2026 updated by: Robert Minnee, Erasmus Medical Center

A Clinically Applicable Prediction Model for Living Donor Liver Transplantation Outcomes Using the International LDLT Registry

Rationale:

Living donor liver transplantation (LDLT) has emerged as an important option for patients with end-stage liver disease. To facilitate international and meaningful comparisons, our institution participates in the International LDLT Registry. Several models to predict outcomes post-LDLT have been developed to council and justify the major surgery that the living liver donors undergo. However, most proposed models are at high risk of bias and demonstrate suboptimal discriminative ability.

This study aims to externally validate the most promising prediction models and subsequently, develop a new, clinically applicable prediction model for LDLT outcomes, using the International LDLT Registry.

Objective(s):

The main objective of this study is to develop a new, clinically applicable prediction model for LDLT outcomes, using the International LDLT Registry.

The secondary objective is to externally validate the most promising existing prediction models for LDLT outcomes, using the International LDLT Registry.

Study type:

This is an observational, multicenter cohort study using prospectively collected data from the International LDLT Registry. Registry data will be analyzed retrospectively for the purposes of external model validation and prediction model development.

Study population:

The study population consists of living liver donors and their corresponding recipients recorded in the International LDLT registry.

Methods:

For external validation, parameters will be entered in the existing prediction models resulting in the predicted risks. Model discrimination will be measured using the area under the curve (AUC) and by the discrimination slope. The DeLong test will be used to test for difference between the AUC of the different prediction models. Calibration will be evaluated by comparing the observed with the predicted rate of events and graphically represented by calibration plots.

For the development of a new prediction model, the outcome of interest is early graft failure, defined as graft loss within 90 days after transplantation. A multivariable logistic regression model will be developed to estimate the individual risk of early graft failure. Internal validation will be performed using bootstrapping, and model performance will be assessed in terms of discrimination and calibration. Model performance will also be tested in subgroups.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Research questions:

  1. Can a new preoperative prediction model based on donor- and recipient-related variables be developed using the International LDLT Registry to reliably predict LDLT outcomes?
  2. How do the most promising existing prediction models for LDLT outcomes perform when externally validated in the International LDLT Registry?

Sample size calculation:

Sample size for prediction model development was estimated using an events-per-parameter (EPP/EPV) approach. Based on previous research, the anticipated event rate for early graft failure was set at 17.5%. We prespecified 20 model parameters (including dummy variables for categorical predictors and any interaction terms) and targeted 15 events per parameter to reduce overfitting. This yields a minimum of 300 events, corresponding to a total sample size of approximately 1,715 patients.

Statistical Analysis:

Statistical analyses will be carried out using RStudio 2024.09.1, GraphPad Prism 10.6.1, and Microsoft Excel version 16.90.2. P <0.05 indicates statistical significance.

Normality will be tested using the Shapiro-Wilk test. Baseline characteristics will be divided based on the presence of early graft failure. Homogeneity of variances will be tested using the F-test. These results will be presented in a table.

For external validation of existing prediction models, parameters will be entered in the prediction models resulting in the predicted risks. Model discrimination will be measured using the AUC and by the discrimination slope. The DeLong test will be used to test for difference between the AUC of the different prediction models. Calibration will be evaluated by comparing the observed with the predicted rate of events and graphically represented by calibration plots.

For the development of a new prediction model, the outcome of interest is early graft failure, defined as graft loss within 90 days after transplantation (binary outcome). A multivariable logistic regression model will be developed to estimate the individual risk of early graft failure. Candidate predictors will be selected a priori based on clinical relevance, biological plausibility, and availability before transplantation, informed by existing literature and expert opinion. To reduce the risk of overfitting, the number of model parameters will be limited to approximately 20, in accordance with an events-per-parameter approach. Data-driven predictor selection procedures will be avoided. Internal validation will be performed using bootstrapping, and model performance will be assessed in terms of discrimination and calibration.

Model performance will also be tested in the following subgroups: recipient sex, recipient continent of residence, indication for liver transplantation, actual donor hepatectomy performed, and approach to donor hepatectomy.

Study Type

Observational

Enrollment (Estimated)

3000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Netherlands
    • South Holland
      • Rotterdam, South Holland, Netherlands, 3015 GD
        • Erasmus Medical Center
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

The study population consists of living liver donors and their corresponding recipients recorded in the International LDLT registry.

Description

Inclusion Criteria:

- All LDLT donor-recipient pairs registered in the International LDLT Registry from September 1, 2023 to present.

Exclusion Criteria:

  • Two stage LDLT
  • Dual grafts LDLT

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Living liver donors and recipients
The study population consists of living liver donors and their corresponding recipients recorded in the International LDLT registry.
Procedure: Living donor liver transplantation
Donors who underwent living donor hepatectomy and recipients who underwent living donor liver transplantation.
Other Names:
  • Live donor liver transplantation
  • Living donor hepatectomy

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
LDLT prediction model
Time Frame: From initiation of LDLT-screening until 1 year post-donation
The primary outcome is a new, clinically applicable prediction model for LDLT outcomes.
From initiation of LDLT-screening until 1 year post-donation

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
External validity existing prediction models
Time Frame: From initiation of LDLT-screening until 1 year post-donation
The secondary outcome is the external validity of the most promising existing prediction models for LDLT outcomes extracted from the literature. This is assessed using the area under the curve from a receiver operating characteristic curve.
From initiation of LDLT-screening until 1 year post-donation

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Erasmus Medical Center

Investigators

  • Principal Investigator: Robert C. Minnee, MD, PhD, MSc, Erasmus Medical Center

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

March 10, 2026

Primary Completion (Estimated)

July 1, 2026

Study Completion (Estimated)

July 2, 2027

Study Registration Dates

First Submitted

January 15, 2026

First Submitted That Met QC Criteria

February 2, 2026

First Posted (Actual)

February 3, 2026

Study Record Updates

Last Update Posted (Actual)

February 3, 2026

Last Update Submitted That Met QC Criteria

February 2, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • PREDICTLDLT

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

Study protocol, data management plan, data analysis plan, script to assess data, scripts to analyze data, scripts to generate tables and figures in the publication.

IPD Sharing Time Frame

(Underlying) data will be made available alongside with the publication. It will be available for 10 years. Access to the data and images is approved by the head of department and principal investigator. This access is temporarily. To whom and when access is approved is registered.

Access to the registry can be requested by contacting the International LDLT registry (LDLTregistry.org).

IPD Sharing Access Criteria

The PI will verify the authenticity of the requesting researcher and will check whether the intended methodology is suitable and will approve the request before providing access to the data. Other researchers could express their interest in the dataset through countersigned DTA. After meeting the sharing and reuse conditions as described above and approval of the PI, data access will be provided through the data repository.

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • SAP
  • ANALYTIC_CODE

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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