Hepatic Encephalopathy and Spontaneous Bacterial Peritonitis Improve Cirrhosis Outcome Prediction: A Modified Seven-Stage Model as a Clinical Alternative to MELD
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Source and Patient Selection
2.2. The Definition and Diagnosis of Each Cirrhotic Complication
2.3. The Stage/Status Models
2.4. The Primary Endpoint and Follow-Up
2.5. Covariates
2.6. Statistical Methods
3. Results
3.1. Flowchart and Demographics
3.2. The Incidence Rates of Death (Person-Years) for Each Clinical Stage and MELD Score
3.3. Prediction Power of Models
3.4. Cox Model Analysis
3.5. The Nomograms of the Prognostic Indexes
3.6. Sensitivity Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018, 392, 1736–1788. [Google Scholar] [CrossRef] [Green Version]
- GBD 2017 Cirrhosis Collaborators. The global, regional, and national burden of cirrhosis by cause in 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol. Hepatol. 2020, 5, 245–266. [Google Scholar] [CrossRef] [Green Version]
- Asrani, S.K.; Devarbhavi, H.; Eaton, J.; Kamath, P.S. Burden of liver diseases in the world. J. Hepatol. 2019, 70, 151–171. [Google Scholar] [CrossRef] [PubMed]
- D’Amico, G.; Garcia-Tsao, G.; Pagliaro, L. Natural history and prognostic indicators of survival in cirrhosis: A systematic review of 118 studies. J. Hepatol. 2006, 44, 217–231. [Google Scholar] [CrossRef]
- Heidelbaugh, J.J.; Sherbondy, M. Cirrhosis and chronic liver failure: Part II. Complications and treatment. Am. Fam. Phys. 2006, 74, 767–776. [Google Scholar]
- European Association for the Study of the Liver. EASL Clinical Practice Guidelines for the management of patients with decompensated cirrhosis. J. Hepatol. 2018, 69, 406–460. [Google Scholar] [CrossRef] [Green Version]
- Banerjee, R.; Das, A.; Ghoshal, U.C.; Sinha, M. Predicting mortality in patients with cirrhosis of liver with application of neural network technology. J. Gastroenterol. Hepatol. 2003, 18, 1054–1060. [Google Scholar] [CrossRef]
- Child, C.G.; Turcotte, J.G. Surgery and portal hypertension. Major Probl. Clin. Surg. 1964, 1, 1–85. [Google Scholar]
- Kamath, P.S.; Wiesner, R.H.; Malinchoc, M.; Kremers, W.; Therneau, T.M.; Kosberg, C.L.; D’Amico, G.; Dickson, E.R.; Kim, W.R. A model to predict survival in patients with end-stage liver disease. Hepatology 2001, 33, 464–470. [Google Scholar] [CrossRef]
- Asrani, S.K.; Kim, W.R. Model for end-stage liver disease: End of the first decade. Clin. Liver Dis. 2011, 15, 685–698. [Google Scholar] [CrossRef] [Green Version]
- Wiesner, R.; Edwards, E.; Freeman, R.; Harper, A.; Kim, R.; Kamath, P.; Kremers, W.; Lake, J.; Howard, T.; Merion, R.M.; et al. Model for end-stage liver disease (MELD) and allocation of donor livers. Gastroenterology 2003, 124, 91–96. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fink, M.A.; Angus, P.W.; Gow, P.J.; Berry, S.R.; Wang, B.Z.; Muralidharan, V.; Christophi, C.; Jones, R.M. Liver transplant recipient selection: MELD vs. clinical judgment. Liver Transpl. 2005, 11, 621–626. [Google Scholar] [CrossRef] [PubMed]
- Gotthardt, D.; Weiss, K.H.; Baumgartner, M.; Zahn, A.; Stremmel, W.; Schmidt, J.; Bruckner, T.; Sauer, P. Limitations of the MELD score in predicting mortality or need for removal from waiting list in patients awaiting liver transplantation. BMC Gastroenterol. 2009, 9, 72. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Biselli, M.; Gitto, S.; Gramenzi, A.; Di Donato, R.; Brodosi, L.; Ravaioli, M.; Grazi, G.L.; Pinna, A.D.; Andreone, P.; Bernardi, M. Six score systems to evaluate candidates with advanced cirrhosis for orthotopic liver transplant: Which is the winner? Liver Transpl. 2010, 16, 964–973. [Google Scholar] [CrossRef] [PubMed]
- Botta, F.; Giannini, E.; Romagnoli, P.; Fasoli, A.; Malfatti, F.; Chiarbonello, B.; Testa, E.; Risso, D.; Colla, G.; Testa, R. MELD scoring system is useful for predicting prognosis in patients with liver cirrhosis and is correlated with residual liver function: A European study. Gut 2003, 52, 134–139. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lau, T.; Ahmad, J. Clinical applications of the Model for End-Stage Liver Disease (MELD) in hepatic medicine. Hepatic Med. 2013, 5, 1–10. [Google Scholar] [CrossRef] [Green Version]
- Yoo, H.Y.; Edwin, D.; Thuluvath, P.J. Relationship of the model for end-stage liver disease (MELD) scale to hepatic encephalopathy, as defined by electroencephalography and neuropsychometric testing, and ascites. Am. J. Gastroenterol. 2003, 98, 1395–1399. [Google Scholar] [CrossRef]
- De Franchis, R. Evolving consensus in portal hypertension. Report of the Baveno IV consensus workshop on methodology of diagnosis and therapy in portal hypertension. J. Hepatol. 2005, 43, 167–176. [Google Scholar] [CrossRef]
- Arvaniti, V.; D’Amico, G.; Fede, G.; Manousou, P.; Tsochatzis, E.; Pleguezuelo, M.; Burroughs, A.K. Infections in patients with cirrhosis increase mortality four-fold and should be used in determining prognosis. Gastroenterology 2010, 139, 1246–1256. [Google Scholar] [CrossRef]
- Bohra, A.; Worland, T.; Hui, S.; Terbah, R.; Farrell, A.; Robertson, M. Prognostic significance of hepatic encephalopathy in patients with cirrhosis treated with current standards of care. World J. Gastroenterol. 2020, 26, 2221–2231. [Google Scholar] [CrossRef]
- Sharma, P.; Sarin, S.K. Improved survival with the patients with variceal bleed. Int. J. Hepatol. 2011, 2011, 356919. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tsai, M.S.; Lin, M.H.; Lee, C.P.; Yang, Y.H.; Chen, W.C.; Chang, G.H.; Tsai, Y.T.; Chen, P.C.; Tsai, Y.H. Chang Gung Research Database: A multi-institutional database consisting of original medical records. Biomed. J. 2017, 40, 263–269. [Google Scholar] [CrossRef]
- Shao, S.C.; Chan, Y.Y.; Kao Yang, Y.H.; Lin, S.J.; Hung, M.J.; Chien, R.N.; Lai, C.C.; Lai, E.C. The Chang Gung Research Database-A multi-institutional electronic medical records database for real-world epidemiological studies in Taiwan. Pharmacoepidemiol. Drug Saf. 2019, 28, 593–600. [Google Scholar] [CrossRef] [PubMed]
- Charlson, M.E.; Pompei, P.; Ales, K.L.; MacKenzie, C.R. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J. Chronic Dis. 1987, 40, 373–383. [Google Scholar] [CrossRef]
- Quan, H.; Li, B.; Couris, C.M.; Fushimi, K.; Graham, P.; Hider, P.; Januel, J.M.; Sundararajan, V. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am. J. Epidemiol. 2011, 173, 676–682. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- D’Amico, G.; Morabito, A.; D’Amico, M.; Pasta, L.; Malizia, G.; Rebora, P.; Valsecchi, M.G. Clinical states of cirrhosis and competing risks. J. Hepatol. 2018, 68, 563–576. [Google Scholar] [CrossRef] [Green Version]
- Ahmed, A.; Aronow, W.S.; Fleg, J.L. Higher New York Heart Association classes and increased mortality and hospitalization in patients with heart failure and preserved left ventricular function. Am. Heart J. 2006, 151, 444–450. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hawkins, R.C. Laboratory turnaround time. Clin. Biochem. Rev. 2007, 28, 179–194. [Google Scholar]
- Wedd, J.; Bambha, K.M.; Stotts, M.; Laskey, H.; Colmenero, J.; Gralla, J.; Biggins, S.W. Stage of cirrhosis predicts the risk of liver-related death in patients with low Model for End-Stage Liver Disease scores and cirrhosis awaiting liver transplantation. Liver Transpl. 2014, 20, 1193–1201. [Google Scholar] [CrossRef] [Green Version]
- Zipprich, A.; Garcia-Tsao, G.; Rogowski, S.; Fleig, W.E.; Seufferlein, T.; Dollinger, M.M. Prognostic indicators of survival in patients with compensated and decompensated cirrhosis. Liver Int. 2012, 32, 1407–1414. [Google Scholar] [CrossRef] [Green Version]
- Wong, R.J.; Gish, R.G.; Ahmed, A. Hepatic encephalopathy is associated with significantly increased mortality among patients awaiting liver transplantation. Liver Transpl. 2014, 20, 1454–1461. [Google Scholar] [CrossRef] [PubMed]
- Fichet, J.; Mercier, E.; Genee, O.; Garot, D.; Legras, A.; Dequin, P.F.; Perrotin, D. Prognosis and 1-year mortality of intensive care unit patients with severe hepatic encephalopathy. J. Crit. Care 2009, 24, 364–370. [Google Scholar] [CrossRef] [PubMed]
- Montagnese, S.; De Rui, M.; Schiff, S.; Ceranto, E.; Valenti, P.; Angeli, P.; Cillo, U.; Zanus, G.; Gatta, A.; Amodio, P.; et al. Prognostic benefit of the addition of a quantitative index of hepatic encephalopathy to the MELD score: The MELD-EEG. Liver Int. 2015, 35, 58–64. [Google Scholar] [CrossRef] [PubMed]
- Bac, D.J. Spontaneous bacterial peritonitis: An indication for liver transplantation? Scand. J. Gastroenterol. Suppl. 1996, 218, 38–42. [Google Scholar] [CrossRef]
- Hung, T.H.; Tsai, C.C.; Hsieh, Y.H.; Tsai, C.C. The long-term mortality of spontaneous bacterial peritonitis in cirrhotic patients: A 3-year nationwide cohort study. Turk. J. Gastroenterol. 2015, 26, 159–162. [Google Scholar] [CrossRef] [PubMed]
- Stanley, M.M.; Ochi, S.; Lee, K.K.; Nemchausky, B.A.; Greenlee, H.B.; Allen, J.I.; Allen, M.J.; Baum, R.A.; Gadacz, T.R.; Camara, D.S. Peritoneovenous shunting as compared with medical treatment in patients with alcoholic cirrhosis and massive ascites. Veterans Administration Cooperative Study on Treatment of Alcoholic Cirrhosis with Ascites. N. Engl. J. Med. 1989, 321, 1632–1638. [Google Scholar] [CrossRef]
- Sundaram, V.; Manne, V.; Al-Osaimi, A.M. Ascites and spontaneous bacterial peritonitis: Recommendations from two United States centers. Saudi J. Gastroenterol. 2014, 20, 279–287. [Google Scholar] [CrossRef]
- Wu, V.C.; Chen, S.W.; Ting, P.C.; Chang, C.H.; Wu, M.; Lin, M.S.; Hsieh, M.J.; Wang, C.Y.; Chang, S.H.; Hung, K.C.; et al. Selection of beta-Blocker in Patients with Cirrhosis and Acute Myocardial Infarction: A 13-Year Nationwide Population-Based Study in Asia. J. Am. Heart Assoc. 2018, 7, e008982. [Google Scholar] [CrossRef] [Green Version]
Original Five-Stage Prognostic System # | Interpretation |
---|---|
Compensated LC: | |
Stage 1 (no complication) | EV−, EVB−, Ascites−, Sepsis− |
Stage 2 (EV) | EV+; EVB−, Ascites−, Sepsis− |
Decompensated LC | |
Stage 3 (ascites) | Ascites+, EV±, EVB−, Sepsis− |
Stage 4 (EVB) | EV+ & EVB+; Ascites±; Sepsis− |
Stage 5 (sepsis) | Sepsis+, EV±, EVB±, Ascites± |
Innovated Seven-Stage Prognostic System | Interpretation |
Compensated LC | |
Stage 1 (no complication) | Without complication and CTP score ≤ 6 |
Stage 2 (EV) | EV+; EVB−; Ascites−; Sepsis−; HE−; SBP− |
Decompensated LC | |
Stage 3 (EVB) | EVB+; EV±; Ascites−; Sepsis−; HE−; SBP− |
Stage 4 (ascites) | Ascites+; EV±; EVB±; Sepsis−; HE−; SBP− |
Stage 5 (sepsis) | Sepsis+; EV±; EVB±; Ascites±; HE−; SBP− |
Stage 6 (HE) | HE+; EV±; EVB±; Ascites±; Sepsis±; SBP− |
Stage 7 (SBP) | SBP+; EV±; EVB±; Ascites±; Sepsis±; HE± |
Variable | Statistics |
---|---|
Age | 56.58 ± 14.72 |
Sex | |
Male | 14,095 (67.82) |
Female | 6687 (32.18) |
Etiologies of LC * | |
Hepatitis B | 6928 (33.33) |
Hepatitis C | 3114 (14.98) |
Alcoholic liver | 2409 (11.59) |
Non-B/C/ALC | 8326 (40.09) |
Biochemistry | |
Creatinine (Cr), mg/dL | 0.82 (0.64–1.11) |
Na, mEq/L | 139 (136–141) |
alanine aminotransferase (ALT), U/L | 36 (22–66) |
aspartate aminotransferase (AST), U/L | 52 (32–92) |
Bilirubin Total, mg/dL | 1.2 (0.7–2.4) |
Albumin, g/dL | 3.2 (2.6–3.87) |
Hemogram | |
White blood cells (WBC), ×1000/µL | 5.9 (4.2–8.2) |
International normalized ratio (INR) | 1.2 (1.04–1.4) |
Platelet (PLT), ×1000/µL | 118 (71–197) |
Clinical Index | |
Model for End-Stage Liver Disease, MELD score | 11.38 (7.55, 16.91) |
Charlson comorbidity index (CCI) | 4 (2–6) |
Median follow-up time (months) | 67.10 (32.59–102.18) |
Outcome | |
Mortality | 4427 (21.30) |
LT | 889 (4.28) |
Baseline | Follow-Up | |||
---|---|---|---|---|
N (%) | Number of Deaths | Total Years Observed | Incidence of Death (Person-Years) | |
Five-stage clinical score | ||||
Compensated LC | ||||
Stage 1 (no complication) | 10,179 (48.98) | 986 | 52,304.81 | 1.9% |
Stage 2 (EV) | 1609 (7.74) | 187 | 7106.94 | 2.6% |
Decompensated LC | ||||
Stage 3 (ascites) | 4235 (20.38) | 907 | 19,890.90 | 4.6% |
Stage 4 (EVB) | 2199 (10.58) | 426 | 10,106.92 | 4.2% |
Stage 5 (sepsis) | 2560 (12.32) | 1216 | 14,695.32 | 8.3% |
Seven-stage clinical score | ||||
Compensated LC | ||||
Stage 1 (no complication) | 9265 (44.58) | 730 | 47,899.30 | 1.5% |
Stage 2 (EV) | 1462 (7.03) | 161 | 6250.69 | 2.6% |
Decompensated LC | ||||
Stage 3 (EVB) | 1349 (6.49) | 180 | 5851.21 | 3.1% |
Stage 4 (ascites) | 3831 (18.43) | 637 | 17,365.39 | 3.7% |
Stage 5 (sepsis) | 1593 (7.67) | 529 | 9609.12 | 5.5% |
Stage 6 (HE) | 2212 (10.64) | 941 | 11,135.16 | 8.5% |
Stage 7 (SBP) | 1070 (5.15) | 544 | 5994.03 | 9.1% |
MELD score § | ||||
≤10 | 5126 (24.67) | 426 | 26,244.01 | 1.6% |
11~15 | 2825 (13.59) | 522 | 13,206.03 | 4.0% |
16~20 | 1525 (7.34) | 405 | 7089.04 | 5.7% |
21~25 | 1086 (5.23) | 345 | 5009.86 | 6.9% |
26~30 | 568 (2.73) | 265 | 2609.55 | 10.2% |
31~35 | 278 (1.34) | 160 | 1194.09 | 13.4% |
35~40 | 310 (1.49) | 217 | 1192.69 | 18.2% |
Variable | Model I | Model II | Model III | |||
---|---|---|---|---|---|---|
aHR (95% C.I.) | p-Value | aHR (95% C.I.) | p-Value | aHR (95% C.I.) | p-Value | |
Age | 1.02 (1.02–1.03) | <0.001 | 1.02 (1.02–1.02) | <0.001 | 1.03 (1.02–1.03) | <0.001 |
CCI | 1.09 (1.08–1.10) | <0.001 | 1.10 (1.10–1.12) | <0.001 | 1.08 (1.07–1.10) | <0.001 |
MELD | 1.06 (1.05–1.06) | <0.001 | ||||
Stage # | ||||||
1 | ||||||
2 | 0.98 (0.84–1.14) | 0.8387 | 0.89 (0.78–1.01) | 0.073 | ||
3 | 1.21 (1.04–1.42) | 0.0159 | 1.85 (1.70–2.01) | <0.001 | ||
4 | 1.81 (1.64–2.00) | <0.001 | 1.40(1.25–1.56) | <0.001 | ||
5 | 2.81 (2.52–3.15) | <0.001 | 3.45 (3.18–3.75) | <0.001 | ||
6 | 4.11 (3.75–4.51) | <0.001 | ||||
7 | 4.25 (3.80–4.74) | <0.001 | ||||
C-Index = 0.751 | C-Index = 0.727 | C-Index = 0.797 |
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Huang, C.-H.; Tseng, H.-J.; Amodio, P.; Chen, Y.-L.; Wang, S.-F.; Chang, S.-H.; Hsieh, S.-Y.; Lin, C.-Y. Hepatic Encephalopathy and Spontaneous Bacterial Peritonitis Improve Cirrhosis Outcome Prediction: A Modified Seven-Stage Model as a Clinical Alternative to MELD. J. Pers. Med. 2020, 10, 186. https://doi.org/10.3390/jpm10040186
Huang C-H, Tseng H-J, Amodio P, Chen Y-L, Wang S-F, Chang S-H, Hsieh S-Y, Lin C-Y. Hepatic Encephalopathy and Spontaneous Bacterial Peritonitis Improve Cirrhosis Outcome Prediction: A Modified Seven-Stage Model as a Clinical Alternative to MELD. Journal of Personalized Medicine. 2020; 10(4):186. https://doi.org/10.3390/jpm10040186
Chicago/Turabian StyleHuang, Chien-Hao, Hsiao-Jung Tseng, Piero Amodio, Yu-Ling Chen, Sheng-Fu Wang, Shang-Hung Chang, Sen-Yung Hsieh, and Chun-Yen Lin. 2020. "Hepatic Encephalopathy and Spontaneous Bacterial Peritonitis Improve Cirrhosis Outcome Prediction: A Modified Seven-Stage Model as a Clinical Alternative to MELD" Journal of Personalized Medicine 10, no. 4: 186. https://doi.org/10.3390/jpm10040186
APA StyleHuang, C. -H., Tseng, H. -J., Amodio, P., Chen, Y. -L., Wang, S. -F., Chang, S. -H., Hsieh, S. -Y., & Lin, C. -Y. (2020). Hepatic Encephalopathy and Spontaneous Bacterial Peritonitis Improve Cirrhosis Outcome Prediction: A Modified Seven-Stage Model as a Clinical Alternative to MELD. Journal of Personalized Medicine, 10(4), 186. https://doi.org/10.3390/jpm10040186