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Mayo Clinic College of Medicine

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Presentation on theme: "Mayo Clinic College of Medicine"— Presentation transcript:

1 Mayo Clinic College of Medicine
Enhancement of MELD W. Ray Kim, MD Mayo Clinic College of Medicine Rochester, MN Plummer Building, Rochester, MN

2 Agenda How to make MELD better
As an indicator of pretransplant mortality Add more variable(s): MELDNa Optimize model: Refit MELD As an indicator of posttransplant outcome Limitations

3 Serum Sodium and Mortality
UNOS Registrants, 2005 N=6769 Risk of 3-Month Mortality (after adjusting for MELD) Na (mEq/L) Kim NEJM 2008;1018

4 MELDNa: Incorporating Na to MELD
MELDNa = MELD - Na *MELD*(140-Na) + 140 125 130 135 140 MELD Na (mEq/L) Kim NEJM 2008;1018

5 Validation: MELDNa vs MELD
< < MELD Observed/Expected Based on MELD Based on MELDNa Kim NEJM 2008;1018

6 Refitting Coefficients
Methods Waitlist data obtained from OPTN from : Model derivation set (05-06): n=14,214 Model validation set (07-08): n=13,945 All adult, primary LTx candidates with end stage liver disease included (HCC and status 1 excluded) The proportional hazards regression analysis predicting mortality within 90 days of listing Define lower and upper bounds Optimize coefficients Bilirubin, Creatinine, INR, and Na

7 Bilirubin 05-06 data

8 Bilirubin 05-06 data Current MELD LB=1.0

9 Bilirubin 05-06 data Refit LB=1.0

10 Creatinine 05-06 data

11 Creatinine 05-06 data Current MELD LB=1.0 UB=4.0

12 Creatinine 05-06 data Refit LB=0.8 UB=3.0

13 INR 05-06 data

14 INR 05-06 data Current MELD LB=1.0

15 INR 05-06 data Refit LB=1.0 UB=3.0

16 Sodium 05-06 data

17 Sodium 05-06 data Refit LB=125 UB=140

18 Validation: Bilirubin
07-08 data * *Sharma et al. Gastro. 2008;1575

19 Validation: Creatinine
07-08 data * *Sharma et al. Gastro. 2008;1575

20 Validation: INR 07-08 data * *Sharma et al. Gastro. 2008;1575

21 Validation: MELD 07-08 data
LB (6) and UB (40) ignored for comparison purpose

22 Validation: SRTR MELD 07-08 data

23 Validation: Refit MELD(Na)
07-08 data

24 The Bottom Line Validation Set (2007-2008) Model Chi-Square
Concordance (SE) MELD 2263 (0.0062) SRTR MELD 2055 (-8) (0.0063) MELDNa 2370 (+7) (0.0059) Refit MELDNa 2374 (+11) (0.0058)

25 ‘Kitchen Sink’ ≠ Crystal Ball
‘Unsupervised’ incorporation of all potentially relevant variables C-statistics * includes: refit MELDNa, age, BMI, albumin, HCV, diabetes, prior malignancies ** includes refit MELDNa, age, sex, race, albumin, HCV, HBV, diabetes, prior malignancies, life support, encephalopathy, ascites, previous abdominal surgery, portal vein thrombosis

26 MELD and Resource Utilization
pRBC LOS 29 26 25 18 15 Kim ATC. 2004

27 Transplant Benefit Survival after LTx C-statistics 0.57 0.88
90 Days Survival Survival w/o LTx Mean MELD Refit MELDNa Deciles

28 Conclusions Ways to improve prediction of waitlist mortality
Add more variable: MELD Na Represents a meaningful improvement Big change in mortality in a subgroup of patients Optimize model coeffcients: Refit MELDNa New upper and lower bounds: Creatinine: INR: Limit to predictability MELD and post-transplant outcome Statistically significant impact on survival and resource utilization Limited accuracy

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