A major limitation of liver transplantation is organ shortage. To avoid exposing patients to death on the waiting list, organs are used that would have been discarded few years ago. Graft allocation is regulated by the "agence de biomedecine" which establishes a national score. Each liver graft is proposed to the patient presenting the higher score. Acceptance or rejection of the graft only depends on the decision of each centre. We propose to submit a more efficient allocation model (enabling each proposed liver graft to be transplanted in the candidate whose transplantation will afford the greatest survival benefit after registration), by collecting and analysing variables from donors and candidates/recipients.
Study Type
OBSERVATIONAL
Enrollment
9,000
Survival analysis
In view of the complexity of our project, different parallel approaches will be performed in order to establish predictive models
Time frame: 5 years
Multi-state models
In view of the complexity of our project, different parallel approaches will be performed in order to establish predictive models
Time frame: 5 years
Decision tree analysis
In view of the complexity of our project, different parallel approaches will be performed in order to establish predictive models
Time frame: 5 years
Number of each criterion of ECD (Donor population)
Time frame: 5 years
Percentage of each criterion of ECD (Donor population)
Time frame: 5 years
Donor scores (DRI, ELTR) (Donor population)
Time frame: 5 years
mean score (Donor population)
Time frame: 5 years
number of ECD criteria (Donor population)
Time frame: 5 years
frequency of ECD criteria (Donor population)
Time frame: 5 years
mean age of donors (Donor population)
Time frame: 5 years
graft failure (Donor population)
Time frame: 5 years
grafts with correct primary function (Donor population)
Time frame: 5 years
epidemiological characteristics (Candidate population)
Time frame: 5 years
indication for transplantation (Candidate population)
Time frame: 5 years
severity of disease (Candidate population)
Time frame: 5 years
comorbidities (Candidate population)
Time frame: 5 years
time on waiting list (Candidate population)
Time frame: 5 years
Proportion of early deaths after transplantation (Candidate population)
Time frame: 5 years
drop-outs for worsening (Candidate population)
Description of events occurring on waiting list
Time frame: 5 years
deaths on the waiting list (Candidate population)
Time frame: 5 years
median time of occurrence (Candidate population)
Description of events occurring on waiting list
Time frame: 5 years
number of drop-outs for improvement (Candidate population)
Time frame: 5 years
percentages of drop-outs for improvement (Candidate population)
Time frame: 5 years
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