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Liver Transplant Outcomes in the United States : Effect of Preservation Solution DKFC Symposium July 16, 2012 John Fung, MD, PhD Cleveland Clinic Disclosure:

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Presentation on theme: "Liver Transplant Outcomes in the United States : Effect of Preservation Solution DKFC Symposium July 16, 2012 John Fung, MD, PhD Cleveland Clinic Disclosure:"— Presentation transcript:

1 Liver Transplant Outcomes in the United States : Effect of Preservation Solution DKFC Symposium July 16, 2012 John Fung, MD, PhD Cleveland Clinic Disclosure: I have been a past consultant for both Dupont and Odyssey

2 Recent Retrospective Database Reviews Theme of 3 studies: These results suggest that the increasing use of HTK for abdominal organ preservation should be reexamined

3 Liver Preservation

4 Indiana University, 2001 to 2008 All adult, deceased donor n=1013 HTK 632, UW 381 Simultaneous, retrospective

5 Liver Preservation Indiana University, 2001 to 2008 All adult, deceased donor Simultaneous, retrospective n=1013HTK 632 UW 381 Serum ALT Serum Bilirubin

6

7

8 Using the SRTR Database Only adult first liver-only transplants from were included and only for those whom flush and storage solutions were the same All patients had minimum one year follow up 25,616 patients, 20,901 (82%) with UW and 4,715 (18%) with HTK Mean follow-up: 2.7 ± 1.7 years (2.9 ± 1.7 for UW and 1.8 ± 1.1 for HTK)

9 Statistical Analysis Three comparisons: Unadjusted graft survival Bootstrapping hazard modeling using risk factors for graft survival determined using non-proportional, multiphase, multivariable hazard methodology with >100 clinically relevant recipient, donor, and procedure variables Propensity-matched comparison for 50 most important variables

10 Bootstrapping A random sample of patients is drawn from the original data - patients are drawn one at a time, with replacement, until a new dataset of the same size has been created When the new dataset has been created, the stepwise regression technique is run again to see what significant predictors it finds and the process is repeated multiple times The bootstrap percentage is the percent of runs in which the variable appeared, so the higher the percentage, the more certain is the impact of that variable - those appearing in >50% of runs were considered reliably statistically significant at p<0.001

11 Adjusting for Multiple Tests No. of independent tests Probability of one or more p < 0.05 by chance 10%23%40%64%92% To keep alpha = 0.05 accept as significant only p less than Use p = 0.05 / no. of tests

12 Results Validation of reported significant recipient factors of graft failure in the early and later phases after DDLT OPS did not appear as a statistically significant predictor of graft failure –hospital death, re-transplant rates and relisting rates were not different

13 UW n = 20,901 HTK n = 4,715 PS: p = 0.90 log rank test GS: p = 0.60 Unadjusted Patient and Graft Survival - HTK vs UW Adult LTX from

14 7,883 UW10,484 UW 1,826 HTK 2,314 HTK DRI 2.5: p = 0.20 Unadjusted Patient and Graft Survival - HTK vs UW Adult LTX from : By DRI - 2.5

15 14,053 UW6,119 UW 3,279 HTK 1,177 HTK CIT 8 hr: p = 0.50 Unadjusted Patient and Graft Survival - HTK vs UW Adult LTX from : By CIT - 8 hrs (non-DCD)

16 19,082 UW1,090 UW 4,253 HTK 203 HTK CIT 12 hr: p = 0.60 Unadjusted Patient and Graft Survival - HTK vs UW Adult LTX from : By CIT - 12 hrs (non-DCD)

17 Risk FactorPBootstrap % Early hazard phase Older recipient age (years)< Recipient race White or Black< Recipient portal vein thrombosis< Recipient previous abdominal surgery< Candidate last creatinine (used for MELD)< Candidate last MELD< Recipient on life support just prior to tx< Recipient previous kidney transplant< Donor race non-White< Donor donation after cardiac death< Donor risk index< Risk Factors for Graft Failure - Early Phase

18 Risk Factors for Graft Failure - Constant Phase Risk FactorPBootstrap % Late hazard phase African American recipient< Recipient primary diagnosis for tumors< Recipient hepatitis C virus< Donor age (years)< Donor history of diabetes<

19 Limitations of the Hopkins UNOS Analysis Used case-wise deletion of missing data, i.e. used only patients for whom all variables were reported - the actual number of cases deleted not provided Last case included was 2/28/08 - the paper was submitted on 7/17/08. Allowing a minimum of 45 days to analyze and write the paper, the latest data cutoff was 6/1/08. Using UNOS timelines for a 6/1/08 cutoff, there would only have been data for transplants performed before 11/1/07

20 Unadjusted 1-year Graft Survival Rates by Year of Transplant

21 Liver Transplant Graft Survival SRTR Data, , N=55110, Age 18+ By Years and Preservation Solution: vs and UW vs HTK SRTR Data, , N=55110, Age 18+ By Years and Preservation Solution: vs and UW vs HTK HTK UW UW HTK

22 Liver Transplant Patient Survival SRTR Data, , N=55110, Age 18+ By Years and Preservation Solution: vs and UW vs HTK SRTR Data, , N=55110, Age 18+ By Years and Preservation Solution: vs and UW vs HTK HTK UW UW HTK

23 Comparing HTK Users UNOS Report - ADDLT CenterPatient SurvivalGraft Survival United States Methodist - Memphis 92.1 (+1.0)87.4 (+0.5) University of Indiana 90.0 (+0.7)87.4 (+1.5) Cleveland Clinic91.6 (+1.7)87.9 (+1.3)

24 Comparing UW Users – 2010 UNOS Report - ADDLT CenterPatient SurvivalGraft Survival Johns Hopkins75.6 (-13.9)69.7 (-14.2) MUSC87.5 (-1.1)85.0 (-2.4) Univ. Pennsylvania 86.7 (-2.1)84.8 (-1.1) Univ. Wisconsin90.0 (+4.4)85.2 9(+3.7)

25 Conclusions Discrepancies between published reports and clinical experience: –Flawed analysis –Learning curve –Changing practices Excellent outcomes can be obtained with either solution


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