Ignite Creation Date:
2026-03-26 @ 3:19 PM
Ignite Modification Date:
2026-03-31 @ 5:19 AM
Study NCT ID:
NCT07383194
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-02-03
First Post:
2026-01-15
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Prediction Models for LDLT Outcomes
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 3000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-03-10', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-01', 'completionDateStruct': {'date': '2027-07-02', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-02-02', 'studyFirstSubmitDate': '2026-01-15', 'studyFirstSubmitQcDate': '2026-02-02', 'lastUpdatePostDateStruct': {'date': '2026-02-03', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-03', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-07-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'LDLT prediction model', 'timeFrame': 'From initiation of LDLT-screening until 1 year post-donation', 'description': 'The primary outcome is a new, clinically applicable prediction model for LDLT outcomes.'}], 'secondaryOutcomes': [{'measure': 'External validity existing prediction models', 'timeFrame': 'From initiation of LDLT-screening until 1 year post-donation', 'description': '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.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Liver Transplantation', 'Living Donor', 'Graft Survival', 'Mortality', 'Prediction Algorithms'], 'conditions': ['Living Donor Liver Transplantation']}, 'referencesModule': {'references': [{'pmid': '35124188', 'type': 'BACKGROUND', 'citation': 'Haller MC, Aschauer C, Wallisch C, Leffondre K, van Smeden M, Oberbauer R, Heinze G. Prediction models for living organ transplantation are poorly developed, reported, and validated: a systematic review. J Clin Epidemiol. 2022 May;145:126-135. doi: 10.1016/j.jclinepi.2022.01.025. Epub 2022 Feb 4.'}, {'pmid': '41564353', 'type': 'BACKGROUND', 'citation': 'Li Z, Centonze L, Raptis D, Marquez KAH, Rammohan A, Gunasekaran V, Hong S, Chen IC, Kim J, Hsu SC, Kirimker EO, Akamatsu N, Shaked O, Finotti M, Yeow M, Genedy L, Dutkowski P, Nadalin S, Boehnert MU, Polak WG, Bonney GK, Mathur A, Samstein B, Emond JC, Testa G, Olthoff KM, Heimbach JK, Taner T, Wong TCL, Hasegawa K, Balci D, Cattral M, Sapisochin G, Selzner N, Jeng LB, Joh JW, Chen CL, Suh KS, Di Sandro S, Rela M, Broering D, Clavien PA. Early graft failure after adult living donor liver transplantation: A multicenter risk analysis and development of the early allograft failure in living donor liver transplantation (EAGLE-LDLT) model. Liver Transpl. 2026 Jan 21. doi: 10.1097/LVT.0000000000000816. Online ahead of print.'}, {'pmid': '39883022', 'type': 'BACKGROUND', 'citation': 'Li Z, Raptis D, Rammohan A, Gunasekaran V, Hong S, Chen IC, Kim J, Hervera Marquez KA, Hsu SC, Kirimker EO, Akamatsu N, Shaked O, Finotti M, Yeow M, Genedy L, Braun J, Yebyo H, Dutkowski P, Nadalin S, Boehnert MU, Polak WG, Bonney GK, Mathur A, Samstein B, Emond JC, Testa G, Olthoff KM, Rosen CB, Heimbach JK, Taner T, Wong TC, Lo CM, Hasegawa K, Balci D, Cattral M, Sapisochin G, Selzner N, Jeng LB, Joh JW, Chen CL, Suh KS, Rela M, Broering D, Clavien PA. Validation of a Pretransplant Risk Prediction Model for Early Allograft Dysfunction After Living-donor Liver Transplantation. Transplantation. 2025 Aug 1;109(8):1383-1392. doi: 10.1097/TP.0000000000005331. Epub 2025 Jan 28.'}, {'pmid': '28819092', 'type': 'BACKGROUND', 'citation': 'Babu R, Sethi P, Surendran S, Dhar P, Gopalakrishnan U, Balakrishnan D, Menon RN, Sivasankarapillai Thankamonyamma B, Othiyil Vayoth S, Thillai M. A New Score to Predict Recipient Mortality from Preoperative Donor and Recipient Characteristics in Living Donor Liver Transplantation (DORMAT Score). Ann Transplant. 2017 Aug 18;22:499-506. doi: 10.12659/aot.904350.'}, {'pmid': '38079264', 'type': 'BACKGROUND', 'citation': 'Giglio MC, Dolce P, Yilmaz S, Tokat Y, Acarli K, Kilic M, Zeytunlu M, Unek T, Karam V, Adam R, Polak WG, Fondevila C, Nadalin S, Troisi RI; European Liver and Intestine Transplant Association (ELITA). Development of a model to predict the risk of early graft failure after adult-to-adult living donor liver transplantation: An ELTR study. Liver Transpl. 2024 Aug 1;30(8):835-847. doi: 10.1097/LVT.0000000000000312. Epub 2023 Dec 12.'}, {'pmid': '25042283', 'type': 'BACKGROUND', 'citation': 'Goldberg DS, French B, Abt PL, Olthoff K, Shaked A. Superior survival using living donors and donor-recipient matching using a novel living donor risk index. Hepatology. 2014 Nov;60(5):1717-26. doi: 10.1002/hep.27307. Epub 2014 Oct 2.'}], 'seeAlsoLinks': [{'url': 'https://ldltregistry.org/', 'label': 'Related Info'}]}, 'descriptionModule': {'briefSummary': 'Rationale:\n\nLiving 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.\n\nThis 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.\n\nObjective(s):\n\nThe main objective of this study is to develop a new, clinically applicable prediction model for LDLT outcomes, using the International LDLT Registry.\n\nThe secondary objective is to externally validate the most promising existing prediction models for LDLT outcomes, using the International LDLT Registry.\n\nStudy type:\n\nThis 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.\n\nStudy population:\n\nThe study population consists of living liver donors and their corresponding recipients recorded in the International LDLT registry.\n\nMethods:\n\nFor 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.\n\nFor 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.', 'detailedDescription': 'Research questions:\n\n1. 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?\n2. How do the most promising existing prediction models for LDLT outcomes perform when externally validated in the International LDLT Registry?\n\nSample size calculation:\n\nSample 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.\n\nStatistical Analysis:\n\nStatistical 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.\n\nNormality 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.\n\nFor 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.\n\nFor 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.\n\nModel 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study population consists of living liver donors and their corresponding recipients recorded in the International LDLT registry.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n\\- All LDLT donor-recipient pairs registered in the International LDLT Registry from September 1, 2023 to present.\n\nExclusion Criteria:\n\n* Two stage LDLT\n* Dual grafts LDLT'}, 'identificationModule': {'nctId': 'NCT07383194', 'acronym': 'PREDICTLDLT', 'briefTitle': 'Prediction Models for LDLT Outcomes', 'organization': {'class': 'OTHER', 'fullName': 'Erasmus Medical Center'}, 'officialTitle': 'A Clinically Applicable Prediction Model for Living Donor Liver Transplantation Outcomes Using the International LDLT Registry', 'orgStudyIdInfo': {'id': 'PREDICTLDLT'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Living liver donors and recipients', 'description': 'The study population consists of living liver donors and their corresponding recipients recorded in the International LDLT registry.', 'interventionNames': ['Procedure: Living donor liver transplantation']}], 'interventions': [{'name': 'Living donor liver transplantation', 'type': 'PROCEDURE', 'otherNames': ['Live donor liver transplantation', 'Living donor hepatectomy'], 'description': 'Donors who underwent living donor hepatectomy and recipients who underwent living donor liver transplantation.', 'armGroupLabels': ['Living liver donors and recipients']}]}, 'contactsLocationsModule': {'locations': [{'zip': '3015 GD', 'city': 'Rotterdam', 'state': 'South Holland', 'country': 'Netherlands', 'contacts': [{'name': 'Hayo W. ter Burg, MD', 'role': 'CONTACT', 'email': 'h.terburg@erasmusmc.nl', 'phone': '+316 83233210'}], 'facility': 'Erasmus Medical Center', 'geoPoint': {'lat': 51.9225, 'lon': 4.47917}}], 'centralContacts': [{'name': 'Hayo W. ter Burg, MD, MSc', 'role': 'CONTACT', 'email': 'h.terburg@erasmusmc.nl', 'phone': '+316 83233210'}], 'overallOfficials': [{'name': 'Robert C. Minnee, MD, PhD, MSc', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Erasmus Medical Center'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ANALYTIC_CODE'], 'timeFrame': '(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.\n\nAccess to the registry can be requested by contacting the International LDLT registry (LDLTregistry.org).', 'ipdSharing': 'YES', '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.', 'accessCriteria': '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.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Erasmus Medical Center', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator, Hepato-Pancreato-Biliary/Transplant Surgeon, Epidemiologist', 'investigatorFullName': 'Robert Minnee', 'investigatorAffiliation': 'Erasmus Medical Center'}}}}