Matching Graft Quality to Recipient’s Disease Severity Based on the Survival Benefit in Liver Transplantation

Persistent shortage and heterogeneous quality of liver grafts encourages the optimization of donor-recipient matching in liver transplantation (LT). We explored whether or not there was a survival benefit (SB) of LT according to the quality of grafts assessed by the Donor Quality Index (DQI) and recipients’ disease severity, using the Model for End-Stage Liver Disease (MELD) in 8387 French patients wait-listed between 2009 and 2014. SB associated with LT was estimated using the sequential stratification method in different categories of MELD and DQI. For each transplantation, a stratum was created that matched one transplanted patient with all eligible control candidates. Strata were thereafter combined, and a stratified Cox model, adjusted for covariates, was fitted in order to estimate hazard ratios that qualified the SB according to each MELD and DQI sub-group. A significant SB was observed for all MELD and DQI sub-groups, with the exception of high MELD patients transplanted with “high-risk” grafts. More specifically, in decompensated-cirrhosis patients, “high-risk” grafts did not appear to be detrimental in medium MELD patients. Interestingly, in hepatocellular-carcinoma (HCC) patients, a significant SB was found for all MELD-DQI combinations. For MELD exceptions no SB was found. In terms of SB, “low-risk” grafts appeared appropriate for most severe patients (MELD > 30). Conversely, low/medium MELD and HCC patients presented an SB while allocated “high-risk” grafts. Thus, SB based matching rules for LT candidates might improve the survival of the LT population as a whole.

An external validation [8][9][10] was performed in the validation dataset, which contained 1048 LTs performed in France between January 1, 2014 and December 31, 2014. Both the apparent calibration and the discrimination were preserved.
The proportions were not respected in the different MELD and DQI categories during the sampling. Nevertheless, the sampling repetition generated a sample representative of the general population.
2-Another sampling was then performed for the control group. The number of patients depended on the stratum. For each stratum, we maintained the same number of control patients it originally contained.
3-The two databases were then merged in order to create the bootstrap database.
4-A stratified Cox model, as previously presented, was applied to the bootstrap database and the ( ),* were estimated.

Constancy of the HRs over time assumption
As in Schaubel et al. 11 , in order to verify this assumption, we estimated the HRs for transplants occurring in years 1 and 2+ of follow-up: ( " " , … , % " ) for year 1 and ( " ./ , … , % ./ ) for year 2+.

Consistency/ normality assumption
We then tested the consistency of ( using 200 simulations. The results of the 200 simulations obtained are given in Table 1S, as well as the P-values and confidence intervals obtained by normal approximation.
The results obtained are similar to those obtained in Figure 3. We then graphically verified the normality assumption which was consistent.

Constancy over time
We verified whether the HRs were constant across time. We then fitted a model by splitting each MELD and DQI categories into two parts; firstly, LT occurring in the first year on the WL; secondly, LT occurring in the second year or beyond. The results obtained are presented in Table 2S. For the first year we obtained results consistent with the one presented above, namely a significant survival benefit for each category of MELD and DQI except for the "high MELD and high DQI category" (i.e. non-significant survival benefit).
For the second year, the survival benefit was not constant for all categories along with the waiting period. Indeed, three scenarios occurred: -Persistent survival benefit for "Low MELD and High DQI", "Medium MELD and Low DQI", and "Medium MELD and Medium DQI" categories.
-Increasing survival benefit for "Low MELD and Low DQI", and "Low MELD and Medium DQI" categories.
-Non-significant HRs for four categories (i.e. all "high MELD" categories and "Medium MELD and High DQI" category).
This assumption was thus not valid for all MELD and DQI categories. HRs seemed dependent on the LT time. We were able to test this hypothesis only for LTs at more or less one year after listing. A lack of power did not enable an appropriate interpretation of these results. Indeed, splitting into two groups in order to consider LTs performed before or after 1-year post wait-listing led to few index patients for some MELD and DQI categories. Furthermore, a selection bias might be present since patients grafted after more than one year on the WL might be "less ill" than patients grafted in their first year on the WL.

Supplementary Table Legends
- categories for all Donor Quality Index (DQI) after 200 bootstrap loops. The reference is the control group that consists of patients who remained on the waiting-list (WL) waiting for a potential graft of "better quality" (i.e. a lower-DQI graft) than the one of the index patient. Hazard ratios are given with their 95% confidence intervals. ***p<0.001; **p<0.01; *p<0.05; p: NS otherwise.
- Table 3S    The reference is the control group that consists of patients who remained on the waiting-list (WL) waiting for a potential graft of "better quality" (i.e. a lower-DQI graft) than the one of the index patient. Hazard ratios are given with their 95% confidence intervals. ***p<0.001; **p<0.01; *p<0.05; p: NS otherwise. The reference is the control group that consists of patients who remained on the waiting-list (WL) waiting for a potential graft of "better quality" (i.e. a lower-DQI graft) than the one of the index patient. Hazard ratios are given with their 95% confidence intervals. ***p<0.001; **p<0.01; *p<0.05; p: NS otherwise.  Figure 1S -Survival curve using Kaplan Meier estimate for the three risk groups of the Donor Quality Index score.