A good prognostic predictor for liver transplantation recipients with benign end-stage liver cirrhosis

Qiang Wei， Rahmeet Singh Nemdharry， Run-Zhou Zhuang， Jie Li， Qi Ling， Jian Wu， Tian Shen， Lin Zhou， Hai-Yang Xie， Min Zhang， Xiao Xu and Shu-Sen Zheng

Hangzhou， China

A good prognostic predictor for liver transplantation recipients with benign end-stage liver cirrhosis

Qiang Wei， Rahmeet Singh Nemdharry， Run-Zhou Zhuang， Jie Li， Qi Ling， Jian Wu， Tian Shen， Lin Zhou， Hai-Yang Xie， Min Zhang， Xiao Xu and Shu-Sen Zheng

Hangzhou， China

BACKGROUND: Post-transplant model for predicting mortality (PMPM， calculated as -5.359+1.988×ln (serum creatinine [mg/dL])+1.089×ln (total bilirubin [mg/dL])) score has been proved to be a simple and accurate model for predicting the prognosis after liver transplantation (LT) in a single center study. Here we aim to verify this model in a large cohort of patients.

METHODS: A total of 2727 patients undergoing LT with endstage liver cirrhosis from January 2003 to December 2010 were included in this retrospective study. Data were collected from the China Liver Transplant Registry (CLTR). PMPM score was calculated at 24-h and 7-d following LT. According to the PMPM score at 24-h， all patients were divided into the low-risk group (PMPM score ≤-1.4， n=2509) and the high-risk group (PMPM score >-1.4， n=218). The area under receiver operator characteristic curve (AUROC) was calculated for evaluating the prognostic accuracy.

RESULTS: The 1-， 3-， and 5-year patient survival rates in the low-risk group were significantly higher than those in the high-risk group (90.23%， 88.01%， and 86.03% vs 63.16%， 59.62%， and 56.43%， respectively， P<0.001). In the high-risk group， 131 patients had a decreased PMPM score (≤-1.4) at 7-d， and their cumulative survival rate was significantly higher than the other 87 patients with sustained high PMPM score (>-1.4) (P<0.001). For predicting 3-month mortality， PMPM score showed a much higher AUROC than post-transplant MELD score (P<0.05).

CONCLUSION: PMPM score is a simple and effective tool to predict short-term mortality after liver transplantation in patients with benign liver diseases， and an indicator for prompt salvaging treatment as well.

(Hepatobiliary Pancreat Dis Int 2017;16:164-168)

cirrhosis;

liver transplantation;

post-transplant model for predicting mortality score; prognosis

Introduction

There is a general consensus that the model for end-stage liver disease (MELD) scoring systems is a good predictor of mortality for a broad range of patients with end-stage liver diseases， including candidates on the waiting list for liver transplantation (LT). MELD has been used as criteria for the allocation of liver grafts since 2002.[1-4]The MELD scoring system is currently the most widely accepted method for predicting mortality in patients with cirrhosis awaiting LT.[5]However， the predictive ability of the MELD score on outcomes after LT remains controversial.[6]Selzner’s study showed that there were no significant differences in survival rate after living donor liver transplantation (LDLT) between patients with high and low MELDscores; other studies showed that high MELD score was an independent risk factor of poor outcomes after LT.[7-11]In recent years， researchers have tried to search other pre-transplantation models to predict post-transplant survival， some of which were based on MELD.[12-14]However， some surgical elements， usually bringing accessional factors for post-transplant mortality， were not taken into account， which greatly limited its predictive efficacy.[15]Although including as many as more than 7 parameters， some models were not good predictors with the area under receiver operator characteristic curve (AUROC) of <0.7.[16，17]Post-transplant model for predicting mortality (PMPM)， which is defined based on serum levels of total bilirubin and creatinine at 24-h post-transplantation， has been proved to be an accurate predictor of post-transplant mortality among patients with benign underlying liver diseases.[18]

The present study aimed to further evaluate the efficacy of PMPM score in the prediction of post-transplant survival by using a large database extracted from the China Liver Transplant Registry (CLTR).

Methods

Patient characteristics

Data were extracted from CLTR database. The study population included adult patients (>18 years old) who underwent LT between January 2003 and December 2010 for benign end-stage liver cirrhosis (n=2900). Retransplantation (n=102)， combined liver-kidney transplantation (n=68) and patients who died within 1 week(n=3) were excluded from this study. The remaining 2727 patients were enrolled. Patients’ characteristics are shown in Table. All enrolled patients were routinely followed up at outpatient clinic for at least 3 months. This study complied with the guidelines of Chinese Ethics Committee and the Declaration of Helsinki. Written informed consent was acquired from all donors and recipients before transplantation.

Table. Clinical characteristics of recipients

Data collection

The following variables were recorded for the study population: age， gender， blood groups， etiologies (hepatitis B virus [HBV]， hepatitis C virus [HCV]， alcohol and others)， complications (ascites， hepatic encephalopathy， hepatorenal syndrome， gastrointestinal bleeding， and spontaneous peritonitis)， comorbidities (diabetes mellitus， hypertension， and sepsis)， serum biochemistry (albumin， bilirubin， creatinine， international normalized ratio， prothrombin time， and sodium)， hemodialysis， and operation history. Severity of ascites (absent， slight， moderate) and encephalopathy (I-IV stages) were assessed using the standard ordinal scale of the Child-Turcotte-Pugh (CTP) score. Hepatorenal syndrome was diagnosed based on the criteria of International Ascites Club.[19]All laboratory data were the last recorded values before transplantation.

Calculation of prognostic scores

The PMPM score was calculated as -5.359+1.988× ln (serum creatinine [mg/dL])+1.089×ln (total bilirubin [mg/dL]). In our previous study， recipients with PMPM score ≤-1.4 achieved better survival than those with PMPM score >-1.4. Serum creatinine and total bilirubin were obtained at 24-h and 7-d post-transplantation. According to their PMPM scores calculated at 24-h post-transplantation， the recipients were divided into the low-risk group (PMPM score ≤-1.4， n=2509) and the high-risk group (PMPM score >-1.4， n=218).

The MELD score was calculated as 3.78×ln (bilirubin [mg/dL])+9.57×ln (creatinine [mg/dL])+11.20×ln (international normalized ratio)+6.43×(0 if cholestatic or alcoholic， 1 otherwise). Minimal value for parameter of MELD score was 1 and maximal value for creatinine was 4. Creatinine was set at 4 if the patient was receiving renal replacement therapy.

Statistical analysis

Quantitative variables were expressed as mean±SD and categorical variables were presented as values. The AUROC was calculated to evaluate the predictive accuracy of prognostic scores of post-transplant mortality.Kaplan-Meier method with log-rank test was used for cumulative survival comparison. SAS software version 9.2 (SAS institute， Cary， NC， USA) was used for the analyses， and a P value of <0.05 was considered statistically significant.

Results

Patients in the low-risk group achieved better survival than those in the high-risk group (Fig. 1A， P<0.001). The 1-， 3- and 5-year survival rates were 90.23%， 88.01% and 86.03% in the low-risk group and 63.16%， 59.62% and 56.43% in the high-risk group， respectively.

For 218 patients in the high-risk group the PMPM score was calculated again at day 7 post-transplantation. Of these， 131 patients with a decreased PMPM score (≤-1.4) showed better survival than the other 87 patients with sustained high PMPM score (>-1.4) (Fig. 1B， P<0.001). The 1-， 3- and 5-year overall survival rates in the two subgroups were 78.42% and 40.3% (P<0.001)， 74.4% and 37.4% (P<0.001)， and 72.8% and 32.7% (P<0.001)， respectively.

The AUROC of PMPM score was 0.721 and 0.761 at 24-h and 7-day post-transplantation， respectively， for predicting 3-month mortality (Fig. 2). PMPM score showed much higher AUROC than the post-transplant MELD score for predicting 3-month mortality (0.721 vs 0.682， P=0.028) (Fig. 2A).

Fig. 1. A: Cumulative survival of patients in the low-risk group (PMPM score at 24-h post-transplantation ≤1.4) and the highrisk group (PMPM score at 24-h post-transplantation >1.4); B: Cumulative survival of patients with different PMPM scores at 7-d post-transplantation in the high-risk group.

Discussion

The present study showed that the PMPM score， which includes indices of liver and kidney functions， is a better predictor of post-transplant mortality. As far as we know， this is the first study to evaluate the efficacy of PMPM score in the prediction of post-transplant survival by using a national database. With the development of surgical technique， perioperative management， organ preservation， and immunosuppression， the recipient survival after LT have improved dramatically in the last few decades， and the number of LT candidates is now growing rapidly.[11，20，21]However， the increasing demand for LT has resulted in a worldwide liver grafts shortage， it is crucial to construct a prognostic model to optimize allocation of the limited number of liver grafts to appropriate recipients.

We previously found that kidney and liver functions after LT were predictors of the recipients’ mortality and established PMPM formula which is valuable to predict short- and medium-term mortality after LT. We conduct this large-sample study to further verify the role of PMPM in predicting the prognosis of liver recipients post-transplantation. Our data showed that PMPM score was a good prognostic predictor for patients with cirrho-sis， better than MELD score.

Fig. 2. AUROC of post-transplant MELD score and PMPM score for predicting 3-month mortality. A: PMPM score and post-transplant MELD score were calculated at 24-h post-transplatation; B: PMPM score was calculated at 7-d post-transplantation.

Compared with other formulas， the advance of PMPM score is， no matter how complicated the perioperative circumstances is， the mortality after LT can be simply and accurately evaluated merely by two variables at one time point after LT.[16，17]

Moreover， PMPM is also an indicator for prompt salvaging treatment. Patients who had very high PMPM score need timely therapy such as artificial liver support system to improve liver and kidney function. As an effective treatment to improve liver function， artificial liver support system has been considered as a bridge to liver transplantation.[22-24]After these treatments， international normalized ratio， serum creatinine and total bilirubin could be effectively improved which can decrease PMPM score.[25]If PMPM score drops down which means liver and/or kidney function recover well within 1 week， the patient would be associated with relative good prognosis. In our score system， a cut-off line of -1.4 for PMPM was chosen to classify the recipients into the low-risk and high-risk groups. The recipient who had a PMPM score>-1.4 at 24-h post-transplantation is at a high-risk of poor prognosis. On day 7 after LT， the fluctuation of PMPM score reflects the prognosis of the patients. The recipients with down-regulated PMPM scores have better survival compared with those who still have high PMPM scores. The improvement of PMPM score suggested that a few recipients in the high-risk group still had a chance to survive.

There was a limitation in this study that HBV-induced cirrhosis comprises the majority of LT etiology in this study， and our data need further validation with various cohorts of different etiologies.

In conclusion， PMPM score has a great value of predicting the outcomes of liver recipients and planning prompt intervention; PMPM score also helps to evaluate the therapeutic efficacy and therefore， an effective indicator to predict patient prognosis after LT for benign liver disease.

Acknowledgments: We gratefully acknowledge the China Liver Transplant Registry (CLTR) for the data collection and statistical analysis. We also acknowledge the transplant centers from the Mainland of China for their major contribution to the CLTR database.

Contributors: WQ and XX proposed the study and wrote the first draft. All authors contributed to the design and interpretation of the study and to further drafts. ZSS is the guarantors.

Funding: This work was supported by grants from the Cheung Kong Scholars Program， the Youth Science and Technology Innovation Leader Program of Science and Technology Ministry and the Projects of Medical and Health Technology Program in Zhejiang Province (2017RC002).

Ethical approval: This study complied with the guidelines of Chinese Ethics Committee and the Declaration of Helsinki. Written informed consent was acquired from all donors and recipients before transplantation.

Competing interest: No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.

1 Kamath PS， Wiesner RH， Malinchoc M， Kremers W， Therneau TM， Kosberg CL， et al. A model to predict survival in patients with end-stage liver disease. Hepatology 2001;33:464-670.

2 Wiesner R， Edwards E， Freeman R， Harper A， Kim R， Kamath P， et al. Model for end-stage liver disease (MELD) and allocation of donor livers. Gastroenterology 2003;124:91-96.

3 Biselli M， Gitto S， Gramenzi A， Di Donato R， Brodosi L， Ravaioli M， et al. Six score systems to evaluate candidates with advanced cirrhosis for orthotopic liver transplant: Which is the winner? Liver Transpl 2010;16:964-973.

4 Sotiropoulos GC， Vernadakis S， Paul A， Hoyer DP， Saner FH， Gallinat A. Single-center experience on liver transplantation for model for end-stage liver disease score 40 patients. Dig Dis Sci 2016;61:3346-3353.

5 Toso C， Mazzaferro V， Bruix J， Freeman R， Mentha G， Majno P. Toward a better liver graft allocation that accounts for candidates with and without hepatocellular carcinoma. Am J Transplant 2014;14:2221-2227.

6 Huo TI， Lin HC， Wu JC， Hou MC， Lee FY， Lee PC， et al. Limitation of the model for end-stage liver disease for outcome prediction in patients with cirrhosis-related complications. Clin Transplant 2006;20:188-194.

7 Selzner M， Kashfi A， Cattral MS， Selzner N， McGilvray ID， Greig PD， et al. Live donor liver transplantation in high MELD score recipients. Ann Surg 2010;251:153-157.

8 Cywinski JB， Mascha EJ， You J， Sessler DI， Kapural L， Argalious M， et al. Pre-transplant MELD and sodium MELD scores are poor predictors of graft failure and mortality after liver transplantation. Hepatol Int 2011;5:841-849.

9 Weismüller TJ， Fikatas P， Schmidt J， Barreiros AP， Otto G， Beckebaum S， et al. Multicentric evaluation of model for endstage liver disease-based allocation and survival after liver transplantation in Germany--limitations of the ‘sickest first’-concept. Transpl Int 2011;24:91-99.

10 Batista TP， Sabat BD， Melo PS， Miranda LE， Fonseca-Neto OC， Amorim AG， et al. Employment of MELD score for the prediction of survival after liver transplantation. Rev Col Bras Cir 2012;39:105-111.

11 Agopian VG， Petrowsky H， Kaldas FM， Zarrinpar A， Farmer DG， Yersiz H， et al. The evolution of liver transplantation during 3 decades: analysis of 5347 consecutive liver transplants at a single center. Ann Surg 2013;258:409-421.

12 Toso C， Dupuis-Lozeron E， Majno P， Berney T， Kneteman NM， Perneger T， et al. A model for dropout assessment of candidates with or without hepatocellular carcinoma on a common liver transplant waiting list. Hepatology 2012;56:149-156.

13 Marvin MR， Ferguson N， Cannon RM， Jones CM， Brock GN. MELDEQ : An alternative Model for End-Stage Liver Disease score for patients with hepatocellular carcinoma. Liver Transpl 2015;21:612-622.

14 Hamaguchi Y， Kaido T， Okumura S， Kobayashi A， Shirai H，Yagi S， et al. Proposal of muscle-meld score， including muscularity， for prediction of mortality after living donor liver transplantation. Transplantation 2016;100:2416-2423.

15 Rice HE， O’Keefe GE， Helton WS， Johansen K. Morbid prognostic features in patients with chronic liver failure undergoing nonhepatic surgery. Arch Surg 1997;132:880-885.

16 Ghobrial RM， Gornbein J， Steadman R， Danino N， Markmann JF， Holt C， et al. Pretransplant model to predict posttransplant survival in liver transplant patients. Ann Surg 2002;236:315-323.

17 Thuluvath PJ， Yoo HY， Thompson RE. A model to predict survival at one month， one year， and five years after liver transplantation based on pretransplant clinical characteristics. Liver Transpl 2003;9:527-532.

18 Xu X， Ling Q， Wu J， Chen J， Gao F， Feng XN， et al. A novel prognostic model based on serum levels of total bilirubin and creatinine early after liver transplantation. Liver Int 2007;27:816-824.

19 Arroyo V， Ginès P， Gerbes AL， Dudley FJ， Gentilini P， Laffi G， et al. Definition and diagnostic criteria of refractory ascites and hepatorenal syndrome in cirrhosis. International Ascites Club. Hepatology 1996;23:164-176.

20 Suzuki H， Bartlett AS， Muiesan P， Jassem W， Rela M， Heaton N. High model for end-stage liver disease score as a predictor of survival during long-term follow-up after liver transplantation. Transplant Proc 2012;44:384-388.

21 Kim WR， Lake JR， Smith JM， Skeans MA， Schladt DP， Edwards EB， et al. OPTN/SRTR 2013 Annual Data Report: liver. Am J Transplant 2015;15:1-28.

22 Li LJ， Zhang YM， Liu XL， Du WB， Huang JR， Yang Q， et al. Artificial liver support system in China: a review over the last 30 years. Ther Apher Dial 2006;10:160-167.

23 Chen Z， Wen T， Zeng Y， Wang L， Lu JJ， Gong S， et al. A single institution experience with living donor liver transplantation for acute-on-chronic hepatitis B liver failure. Hepatogastroenterology 2011;58:1267-1273.

24 Meier P， Meier R， Turini P， Friolet R， Blanc E. Prolonged catheter survival in patients with acute kidney injury on continuous renal replacement therapy using a less thrombogenic micropatterned polymer modification. Nephrol Dial Transplant 2011;26:628-635.

25 Chen YS， Wu ZW， He JQ， Yu J， Yang SG， Zhang YM， et al. The curative effect of ALSS on 1-month mortality in AoCLF patients after 72 to 120 hours. Int J Artif Organs 2007;30:906-914.

Received November 6， 2016

Accepted after revision January 25， 2017

Author Affiliations: Key Lab of Combined Multi-Organ Transplantation， Ministry of Public Health， Division of Hepatobiliary and Pancreatic Surgery， Department of Surgery， First Affiliated Hospital， Zhejiang University School of Medicine， Hangzhou 310003， China (Wei Q， Nemdharry RS， Zhuang RZ， Li J， Ling Q， Wu J， Shen T， Zhou L， Xie HY， Zhang M， Xu X and Zheng SS); Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases， Hangzhou 310003， China (Xu X and Zheng SS)

Prof. Shu-Sen Zheng， MD， PhD， FACS， Key Lab of Combined Multi-Organ Transplantation， Ministry of Public Health， Division of Hepatobiliary and Pancreatic Surgery， Department of Surgery， First Affiliated Hospital， Zhejiang University School of Medicine， 79 Qingchun Road， Hangzhou 310003， China (Tel/Fax: +86-571-87236567; Email: zyzss@zju.edu.cn)

This article has been presented as abstract in the ILTS 18th Annual International Congress， San Francisco， California， USA， May 2012.

? 2017， Hepatobiliary Pancreat Dis Int. All rights reserved.

10.1016/S1499-3872(16)60187-X

Published online March 13， 2017.