Prediction Models for LDLT Outcomes

Plain-language summary

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.

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