American Journal of Transplantation 2008; 8: 2537–2546 Wiley Periodicals Inc.
C 2008 The Authors C 2008 The American Society of Journal compilation Transplantation and the American Society of Transplant Surgeons
doi: 10.1111/j.1600-6143.2008.02400.x
Survival Outcomes Following Liver Transplantation (SOFT) Score: A Novel Method to Predict Patient Survival Following Liver Transplantation A. Rana∗ , M. A. Hardy, K. J. Halazun, D. C. Woodland, L. E. Ratner, B. Samstein, J. V. Guarrera, R. S. Brown Jr. and J. C. Emond Division of Abdominal Organ Transplantation, Columbia University College of Physicians and Surgeons, New York, NY ∗ Corresponding author: Abbas Rana,
[email protected] It is critical to balance waitlist mortality against posttransplant mortality. Our objective was to devise a scoring system that predicts recipient survival at 3 months following liver transplantation to complement MELD-predicted waitlist mortality. Univariate and multivariate analysis on 21 673 liver transplant recipients identified independent recipient and donor risk factors for posttransplant mortality. A retrospective analysis conducted on 30 321 waitlisted candidates reevaluated the predictive ability of the Model for End-Stage Liver Disease (MELD) score. We identified 13 recipient factors, 4 donor factors and 2 operative factors (warm and cold ischemia) as significant predictors of recipient mortality following liver transplantation at 3 months. The Survival Outcomes Following Liver Transplant (SOFT) Score utilized 18 risk factors (excluding warm ischemia) to successfully predict 3-month recipient survival following liver transplantation. This analysis represents a study of waitlisted candidates and transplant recipients of liver allografts after the MELD score was implemented. Unlike MELD, the SOFT score can accurately predict 3-month survival following liver transplantation. The most significant risk factors were previous transplantation and life support pretransplant. The SOFT score can help clinicians determine in real time which candidates should be transplanted with which allografts. Combined with MELD, SOFT can better quantify survival benefit for individual transplant procedures. Key words: Liver transplantation, SOFT score, survival outcomes following liver transplantation, posttransplant mortality, risk factors for post-transplant
mortality, donor and recipient risk factors for death after liver transplant Received 14 April 2008, revised 30 June 2008 and accepted for publication 30 July 2008
Introduction The MELD (Model for End-Stage Liver Disease) scoring system (1,2) has transformed liver allograft allocation in the United States since it was implemented for prioritization of transplant candidates in 2002 (3). The MELD score is an accurate predictor of waitlist mortality, as demonstrated in the pioneering study by Wiesner et al. (4), with a c -statistic (5) of 0.83 when used to predict 3-month mortality of candidates on the waitlist. The score substituted effectively for candidate stratification based on subjective assessment. However, the MELD score is a poor predictor of mortality following transplantation (4,6,7). This observation was confirmed by Desai et al. in their analysis, which reports a c -statistic of only 0.54 with the use of the MELD to predict 3-month recipient mortality following liver transplantation (7). When mortality of recipients on the waitlist is compared with the highest and the lowest MELD scores, there is a 300-fold difference, in contrast to the 2-fold difference in survival of patients after liver transplantation (8). Methods other than the MELD score, such as the Child–Pugh score, also had a poor ability to predict posttransplant survival (6). The inability of existing methods to predict posttransplant survival prevents clinicians from effectively selecting potential recipients for transplantation from MELD scores alone. Because candidate factors alone are not predictive of survival following transplantation, a new model is required to accurately predict posttransplant survival. The lack of consideration of donor risk factors is one limitation of the existing standard (transplanting patients with a MELD greater than 15) (8). Recently, the donor risk index (DRI) has been proposed as a method to stratify outcomes associated with graft selection (9). However, the lack of contribution from recipient factors gives the DRI alone a poor predictive value (c -statistic 0.53 based on its application to the United Network for Organ Sharing [UNOS] database). In the present analysis, we combined both donor and recipient risk factors in constructing the Survival Outcomes Following Liver 2537
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Transplantation (SOFT) score to accurately predict recipient posttransplant survival at 3 months. This score would then allow clinicians to balance waitlist mortality at 3 months as predicted by the MELD score against 3-month mortality following liver transplantation as predicted by the SOFT score to determine which candidates should undergo liver transplantation. Since MELD has been proven to be an accurate predictor of 3-month waitlist mortality (4), we constructed the SOFT score to complement the MELD score by predicting 3-month posttransplant mortality. The SOFT score along with the MELD score allows clinicians to make a real-time go or no-go decision on a particular allograft. The SOFT score can also be used to avoid wasteful transplants where predicted survival is below acceptable standards. Furthermore, as the critical liver allograft shortage fuels more aggressive practices to utilize increasingly marginal donor allografts, the SOFT score can establish risk limits for particular liver transplant candidates.
Methods Study population We performed a retrospective analysis of UNOS deidentified patient-level data of all recipients of liver transplantation between March 1, 2002, the date of implementation of the MELD prioritization system, and August 1, 2006. Our analysis employed the liver registry with data collected by the Organ Procurement and Transplantation Network. We included all transplant recipients aged 18 years or older. Donor and recipient characteristics were reported at the time of transplant. Follow-up information was collected at 6 months and then yearly after transplantation. Patients undergoing combined or multivisceral transplants (n = 1402) and recipients of a live-donor transplant (n = 1163) were excluded from the study. All patients were followed from the date of transplant until either death (n = 6004) or the date of last known follow-up (n = 15 669); we analyzed 21 673 recipients. We performed a retrospective analysis of waitlisted candidates to determine their mortality rate at 3 months and to reassess the predictive value of the MELD score. We excluded patients under the age of 18 years (n = 2383). The analysis included patients with an initial date of registration between March 1, 2002 and August 1, 2006. Patients who were transplanted within 3 months upon registering on the waitlist were excluded (n = 14 721). This exclusion was only used for our waitlist mortality analysis, since we needed 90 days at-risk to determine 3-month waitlist mortality. This was not an exclusion used in our analysis of posttransplant survival. The final analysis included 30 321 waitlisted candidates.
Risk factors The recipient and donor risk factors considered in this analysis are listed in Table 1. The characteristics that were present in the plurality of transplants were used as the reference groups. Serum creatinine was utilized instead of calculated creatinine clearance because serum creatinine is readily accessible for rapid assessment of donor allograft quality. This analysis only included recipients who were transplanted after the MELD scoring system was instituted for liver allocation in 2002, resulting in high entry completion (99.9%). Since MELD scores were analyzed as a recipient risk factor, recipient creatinine, bilirubin and international normalized ratio (INR) were not included as individual predictors. Separately, the components of the MELD score were each significant predictors of 3-month posttransplant mortality: total recipient bilirubin ≥ 8 mg/dL (OR 1.2), INR ≥ 2.5 (OR 1.2), creatinine between 1.5 and 2.0 mg/dL (OR 1.3) and creatinine ≥ 2.0 mg/dL (OR 1.5). Patients with malignancy were known to have cancer prior to transplantation and did not reflect incidentally discovered cancer at transplantation.
Risk score Logistic regression analysis determined the predictors of patient death at 3 months posttransplantation. Donor and recipient variables were first analyzed with univariate analysis and are listed in Table 1. Variables found to be significant in univariate analysis were then subjected to multivariate analysis. Points were assigned to each risk factor based on its odds ratio for patient death at 3 months. One point was awarded to each risk factor for every 10% increase in risk for death at 3 months. Negative points were also awarded for every 10% decrease in risk for death at 3 months. We assigned four risk groups based on MELD-predicted 3-month waitlist mortality. We formulated two distinct scores: the preallocation score to predict survival outcomes following liver transplantation (P-SOFT) designed to evaluate patients on the waitlist and the score to predict survival outcomes following liver transplantation (SOFT) that includes both donor and recipient factors to evaluate transplants at the time of transplantation. Model discrimination was assessed using the area under the receiver operating characteristic (ROC) curve.
Results Study population The study population included 21 673 patients. Analysis included 41 504 years-at-risk for the liver transplant recipients. Mean graft survival was 4.2 years. Mean follow-up was 2.0 years. Demographic and clinical characteristics are summarized in Table 2.
Statistical analysis R Data were analyzed using a standard statistical software package, Stata 9 (Stata Corp, College Station, TX). Continuous variables were reported as a mean ± standard deviation and compared using the Student’s t -test. Contingency table analysis was used to compare categorical variables. Results were considered significant at a p-value of 70 years∗ Regional allocation∗ National allocation∗ Race—Asian∗ Race—African American∗ Race—Latino∗ Race—multiracial, other∗ Weight (>75th%tile)∗ Weight (20 h∗ Partial or split liver∗ Female Creatinine > 1.5 mg/dL Creatinine > 2.0 mg/dL Height (>75th%tile) Height ( 140 IU/L ALT or SGPT < 60 IU/L ALT or SGPT > 100 IU/L Diabetes mellitus (type unspecified) Insulin-dependent diabetes mellitus Hypertension—less than 10 yr duration Hypertension—greater than 10 yr duration History of alcohol dependency History of cigarette use > 20 pack years History of cocaine use in the past History of IV drug use Tattoos Hepatitis B (core Ab positive) Hepatitis C (positive serology) Total bilirubin 1–1.8 mg/dL Total bilirubin > 1.8 mg/dL Deceased donor—three or more inotropic agents Warm ischemia time ≤30 min Warm ischemia time 60–70 min Warm ischemia time 70–80 min Warm ischemia time 80–90 min Warm ischemia time >90 min
Recipient risk factors ABO incompatible transplant∗ Diagnosis—acute hepatic necrosis∗ Diagnosis—cholestatic liver disease∗ Diagnosis—metabolic liver disease∗ Diagnosis—malignancy∗ Diagnosis—other∗ Portal vein thrombosis at transplant∗ Age 18–30∗ Age 30–40∗ Age 60–70∗ Age > 70∗ Ascites pretransplant∗ Diabetes mellitus∗ Height > 75th%tile∗ Height < 25th%tile∗ Female ∗ Incidental tumor found at transplant∗ Intensive care unit pretransplant∗ Admitted to hospital pretransplant∗ Life support pretransplant∗ Previous abdominal surgery∗ Race—Asian∗ Race—African American∗ Race—Latino∗ Race—multiracial, other∗ Body mass index 30–35 Body mass index > 35 Hepatitis B (core Ab positive) Hepatitis C (positive serology) One previous transplant Two previous transplants History of angina or coronary artery disease Hypertension ALT or SGPT > 100 IU/L Albumin 2.0–2.5 g/dL Albumin < 2.0 g/dL Dialysis prior to transplantation UNOS status 1 MELD score < 9 MELD score 10–19 MELD score 20–29 MELD score 30–39 MELD score > 40 Encephalopathy at transplant 1–2 yr on waitlist >2 yr on waitlist Transplant performed between 4-1-1994 to 1-1998 Transplant performed between 1-1-1998 to 1-1-2002 Ventilator-dependent pretransplant Hx of peripheral vascular disease Hx of COPD Portal bleed 48 h pretransplant Any previous malignancy Variceal bleeding within 2 weeks of registration Spontaneous bacterial peritonitis pretransplant Pulmonary embolus within 6 months of registration TIPS at transplant
CVA = cerebrovascular accident; AST = aspartate aminotransferase; SGOT = serum glutamic-oxaloacetic transaminase; ALT = alanine aminotransferase; SGPT = serum glutamate-pyruvate transaminase; UNOS = United Network of Organ Sharing; TIPS = transjugular intrahepatic portosytemic shunts; COPD = chronic obstructive pulmonary disease. ∗ Covariates from the SRTR 1-year patient survival model.
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Rana et al. Table 2: Demographic characteristics of donors and recipients
Age (years) % Female % African American Height (cm) Weight (kg) INR Creatinine (mg/dL) MELD Cause of liver failure • Acute hepatic necrosis • Cholestatic liver disease • Metabolic liver disease • Malignancy • Hepatitis C • Hepatitis B • Alcoholic cirrhosis Cold ischemia time (hours) Cause of death • CVA • Trauma
Recipient
Donor
52.0 ± 10.2 32.3% 8.8% 172.4 ± 10.6 83.2 ± 19.5 1.9 ± 1.8 1.4 ± 1.1 20.6 ± 9.6
41.1 ± 17.6 40.9% 14.1% 171.7 ± 11.0 77.9 ± 19.1 NA 1.4 ± 1.5 NA
7.00% 9.20% 2.80% 10.70% 38.00% 18.90% 17.90% NA
NA NA NA NA NA NA NA 7.7 ± 3.6
NA NA
44.6% 40.5%
NA = not applicable to this group of patients; CVA = cerebrovascular accident.
Univariate and multivariate analysis Table 1 lists all of the risk factors that were considered. Risk factors that were significant in univariate analysis were then subjected to multivariate analysis. The significant risk factors in multivariate analysis are presented in Table 3. The most significant risk factors were two previous transplants (OR 2.4, confidence interval (CI) 1.52–3.67), warm ischemia time > 90 min (OR 2.3, CI 1.58–3.41), one previous transplant (OR 1.9, CI 1.56–2.24), life support (OR 1.9, CI 1.54–2.35) and warm ischemia time 80–90 min (OR 1.8, CI 1.10–3.00). Risk score Table 3 summarizes donor and recipient risk factors and their assigned points. Table 4 presents two different risk scores: the preallocation score to predict survival outcomes following liver transplantation (P-SOFT) and the score to predict survival outcomes following liver transplantation (SOFT). The SOFT score is formulated from combining the recipient waitlist score in addition to donor factors and cold ischemia times. Warm ischemia was excluded since it cannot be reliably predicted prior to transplantation. Table 5 illustrates the population distribution and odds ratios based on the group with less than five points. The odds ratios for 3-month survival for groups with 6–15 points, 16–35 points, 36–40 points and > 40 points were 2.2, 5.8, 15.4 and 30.5 respectively. Figures 1 and 2 illustrate the Kaplan–Meier curves and life-table analysis of immediate patient survival post liver transplantation based on risk point totals from the P-SOFT and SOFT scores. Using the SOFT score, the 3-month patient survival of recipients with 40 2540
points was 38%. These groups were then labeled according to 3-month mortality risk, with 25th%tile Weight > 25th%tile COD (anoxia, trauma) Cr < 1.5 No cocaine use Local procurement CIT 6–12 h WIT 30–60 min
Study group
Percent entry Percent of filled patients OR
10–20 >70 Height 90 min
99.9% 99.9% 100.0% 100.0% 99.9% 99.7% 98.6% 100.0% 100.0% 86.8% 75.7% 75.7% 75.7% 75.7%
15.5% 10.8% 23.3% 21.2% 44.6% 10.4% 11.8% 22.8% 6.9% 31.6% 4.9% 1.6% 0.7% 1.1%
0.8 1.3 NS NS 1.2 1.2 NS NS 1.2 0.7 1.1 1.5 1.8 2.3
Age > 70 Female Height < 25th %tile BMI > 35 Acute hepatic necrosis Cholestatic disease Malignancy HCV 1 Previous transplant 2 Previous transplants Previous abdominal surgery
100.0% 100.0% 99.2% 99.2% 100.0% 100.0% 100.0% 87.1% 100.0% 100.0% 100.0%
19.7% 32.3% 21.9% 12.2% 7.0% 9.2% 10.7% 38.0% 7.2% 0.7% 36.8%
1.4 NS NS 1.2 NS NS NS NS 1.9 2.4 1.2
Diabetes mellitus ALT > 100 2.0 < Albumin < 2.5 Albumin < 2.0 Dialysis pretransplant ICU pretransplant Hospitalized pretransplant UNOS Status 1 MELD ≤ 9 MELD 30–39 MELD ≥ 40 Life support pretransplant
99.9% 97.1% 99.9% 99.9% 99.9% 100.0% 100.0% 100.0% 99.9% 99.9% 99.9% 100.0%
23.0% 26.2% 21.3% 10.7% 6.1% 12.7% 15.5% 6.4% 9.5% 13.2% 5.1% 7.1%
NS NS NS 1.2 1.3 1.6 1.3 NS NS 1.4 1.4 1.9
Encephalopathy at transplant Portal vein thrombosis at transplant
100.0% 93.9%
19.7% 3.9%
1.2 1.5
Incidental tumor found at transplant 1–2 years on waitlist ABO incompatible Ventilator-dependent pretransplant
99.9% 99.9% 100.0% 100.0%
4.0% 9.6% 0.5% 3.9%
NS NS NS NS
Portal bleed within 48 h pretransplant
50.7%
3.1%
1.5
Variceal bleeding within 2 weeks of registration
92.9%
5.4%
NS
100.0%
83.9%
1.3
94.8% 93.4%
6.7% 0.3%
NS NS
pValue
CI
Points
0.002 0.002
0.64–0.91 1.09–1.51
0.003 0.012
1.06–1.34 1.05–1.46
0.18 0.00 0.49 0.023 0.022 0.00
0.94–1.42 0.63–0.81 0.85–1.40 1.06–2.19 1.09–3.00 1.58–3.41
–2 3 0 0 2 2 0 0 2 –3 NA NA NA NA
0.00
1.26–1.63
0.032
1.01–1.38
0.00 0.00 0.002
1.56–2.24 1.53–3.69 1.07–1.36
Recipient factors Age 40–60 Male Height > 25th %tile BMI < 30 All other diagnoses
No prior transplants No previous abdominal surgery Nondiabetic ALT < 100 Albumin > 2.5 No dialysis Home pretransplant Non-UNOS status 1 MELD score: (10–19)
Not on life support Pre transplant Nonencephalopathic No portal vein thrombosis No incidental tumors < 1 year on waitlist ABO compatible Not ventilator dependent No portal bleed within 48 h pretransplant No variceal bleeding within 2 weeks of registration No ascites pretransplant No SBP pretransplant No PE within 6 months of registration
Ascites pretransplant SBP pretransplant PE within 6 months of registration
4 0 0 2 0 0 0 0 9 14 2
0.023 0.003 0.00 0.001
1.03–1.42 1.10–1.60 1.34–1.99 1.12–1.53
0.00 0.007 0.00
1.19–1.62 1.09–1.69 1.54–2.35
0 0 0 2 3 6 3 0 0 4 4 9
0.026 0.001
1.02–1.33 1.16–1.84
2 5 0 0 0 0
0.001
1.18–1.89
6 0
0.004
1.08–1.51
3 0 0
COD = cause of death; Cr = serum creatinine; CIT = cold ischemia time; WIT = warm ischemia time; CVA = cerebrovascular accident; %tile = percentile; BMI = body mass index; HCV = hepatitis C Virus; ALT = alanine aminotransferase; ICU = intensive care unit; UNOS = United Network for Organ Sharing; MELD = model for end-stage liver disease.
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Survival 1.00
Patient Survival by SOFT Score
0.75 0.50 0.25
0.00 0
2
Months
Low High-Moderate Futile
4
6
Low-Moderate High
Actuarial Survival for Liver Allograft Recipients Divided According to SOFT Score Month 1 3 6 Low Risk 99% 97% 96% Low-Moderate 97% 94% 92% High-Moderate 90% 84% 80% High 72% 62% 56% Futile 46% 38% 37% * All groups p 1.5 mg/dL • National allocation • Cold ischemia time 0–6 h
Total from above 6 –2 3 2 2
Conclusion
2 –3
BMI = body mass index; MELD = model for end-stage liver disease.
In comparison, the SRTR model for 1-month posttransplant survival had an index of concordance of 0.66.
Limitations Since the passage of the National Transplantation Act of 1984, data entry has been mandatory for all US trans-
This analysis represents the largest study of waitlisted candidates and transplant recipients of liver allografts after the MELD Score was implemented for allocation in 2002. Survival after liver transplantation must be greater than survival on the waitlist to justify liver transplantation. The SOFT score, newly formulated from the same cohort of patients, can accurately predict 3-month survival following liver transplantation. It can then be compared with MELDpredicted waitlist mortality to determine which patients should be transplanted. The SOFT score can also be used to improve donor–recipient matching.
Table 5: Risk groups generated from P-SOFT and SOFT scores
SOFT Score
P-SOFT score
Risk group
Point range
Percentage of patients
Low Low-moderate High-moderate High Futile Low Low-moderate High-moderate High Futile
0–5 6–15 16–35 36–40 > 40 0–5 6–15 16–35 36–40 > 40
35.8 46.8 16.3 0.72 0.41 40.29 44.13 14.54 0.68 0.36
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Odds ratio (CI)(low risk is Ref)
p-Value
2.16 (1.86–2.51) 5.82 (4.98–6.81) 15.44 (10.81–22.04) 30.48 (19.73–47.08)
