Self-organizing maps can determine outcome and match recipient and donors at orthotopic liver transplantation

Transplantation

Volumn 79, Issue 2, 2005, Pages 213-218

  Haydon, Geoffrey H ^a   Hiltunen, Yrjo ^b   Lucey, Michael R ^c   Collett, David ^d   Gunson, Bridget ^a   Murphy, Nick ^a   Nightingale, Peter G ^a   Neuberger, James ^a  

^a QUEEN ELIZABETH HOSPITAL   (United Kingdom)

^b UNIVERSITY OF EASTERN FINLAND   (Finland)

^c UNIVERSITY OF WISCONSIN MADISON   (United States)

^d United Kingdom Transplant   (United Kingdom)

Author keywords

Liver transplantation; Neural networks; Self organizing maps

Indexed keywords

ADOLESCENT; ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHRONIC LIVER DISEASE; COMORBIDITY; DEMOGRAPHY; FEMALE; GRAFT SURVIVAL; HOSPITAL ADMISSION; HUMAN; LABORATORY TEST; LIVER GRAFT; LIVER TRANSPLANTATION; MAJOR CLINICAL STUDY; MALE; ORGAN DONOR; PRIORITY JOURNAL; RECIPIENT; TREATMENT OUTCOME; UNITED KINGDOM; VALIDATION PROCESS;

COMORBIDITY; ETHNIC GROUPS; FEMALE; HEPATECTOMY; HISTOCOMPATIBILITY TESTING; HUMANS; LIVER TRANSPLANTATION; MALE; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; PROBABILITY; SURVIVAL ANALYSIS; TISSUE AND ORGAN HARVESTING; TISSUE DONORS; TREATMENT OUTCOME;

EID: 12744260032     PISSN: 00411337     EISSN: None     Source Type: Journal    
DOI: 10.1097/01.TP.0000146193.02231.E2     Document Type: Article

References (24)

1. Adam R, McMaster P, O'Grady J, et al. Evolution of liver transplantation in Europe: report of the European Liver Transplant Registry. Liver Transplantation 2003; 9: 1231-1243.
2. Kamath PS, Wiesner RH, Malinchoc M, et al. A model to predict survival in patients with end-stage liver disease. Hepatology 2001; 33: 464-470.
3. Forman LM, Lucey MR. Predicting the prognosis of chronic liver disease: an evolution from child to MELD. Mayo End-stage Liver Disease. Hepatology 2001; 33: 473-475.
4. Neuberger J, James O. Guidelines for selection of patients for liver transplantation in the era of donor-organ shortage. Lancet 1999; 354: 1636-1639.
5. Kohonen T. Self-organizing maps. Springer Series in Information Sciences, Vol. 30. Third Extended Edition. Berlin, Heidelberg, New York, Springer 1995, 1997, 2001.
6. Kaartinen J, Hiltunen Y, Kovanen PT, et al. Application of self-organizing maps for the detection and classification of human blood plasma lipoprotein lipid profiles on the basis of 1H NMR spectroscopy data. NMR Biomed 1998; 11: 168-176.
7. Hyvonen MT, Hiltunen Y, El-Deredy W, et al. Application of self-organizing maps in conformational analysis of lipids. J Am Chem Soc 2001; 123: 810-816.
8. Onaca NN, Levy MF, Sanchez EQ, et al. A correlation between the pretransplantation MELD score and mortality in the first two years after liver transplantation. Liver Transpl 2003; 9: 117-123.
9. Saab S, Wang V, Ibrahim AB, et al. MELD score predicts 1-year patient survival post-orthotopic liver transplantation. Liver Transpl 2003; 9: 473-476.
10. Onaca NN, Levy MF, Netto GJ, et al. Pretransplant MELD score as a predictor of outcome after liver transplantation for chronic hepatitis C. Am J Transplant 2003; 3: 626-630.
11. Wali M, Harrison RF, Gow PJ, et al. Advancing donor liver age and rapid fibrosis progression following transplantation for hepatitis C. Gut 2002; 51: 248-252.
12. Berenguer M, Crippin J, Gish R, et al. A model to predict severe HCV-related disease following liver transplantation. Hepatology 2003; 38: 34-41.
13. Parmanto B, Doyle HR. Recurrent neural networks for predicting outcomes after liver transplantation: representing temporal sequence of clinical observations. Methods Inf Med 2001; 40: 386-391.
14. Arabi Y, Abbasi A, Goraj R, et al. External validation of a modified model of Acute Physiology and Chronic Health Evaluation (APACHE) II for orthotopic liver transplant patients. Crit Care 2002; 6: 245-250.
15. Bein T, Frohlich D, Pomsl J, et al. The predictive value of four scoring systems in liver transplant recipients. Intensive Care Med 1995; 21: 32-37.
16. Chung SW, Kirkpatrick AW, Kim HL, et al. Correlation between physiological assessment and outcome after liver transplantation. Am J Surg 2000; 179: 396-399.
17. Talwalkar JA, Seaberg E, Kim WR, et al. Predicting clinical and economic outcomes after liver transplantation using the Mayo primary sclerosing cholangitis model and Child-Pugh score. National Institutes of Diabetes and Digestive and Kidney Diseases Liver Transplantation Database Group. Liver Transpl 2000; 6: 753-758.
18. Wiesner RH. Liver transplantation for primary biliary cirrhosis and primary sclerosing cholangitis: predicting outcomes with natural history models. Mayo Clin Proc 1998; 73: 575-588.
19. Markmann JF, Markmann JW, Markmann DA, et al. Preoperative factors associated with outcome and their impact on resource use in 1148 consecutive primary liver transplants. Transplantation 2001; 72: 1113-1122.
20. Markmann JF, Gornbein J, Markowitz JS, et al. A simple model to estimate survival after retransplantation of the liver. Transplantation 1999; 67: 422-430.
21. Doyle HR, Dvorchik I, Mitchell S, et al. Predicting outcomes after liver transplantation. A connectionist approach. Ann Surg 1994; 219: 408-415.
22. Ghobrial RM, Gornbein J, Steadman R, et al. Pretransplant model to predict posttransplant survival in liver transplant patients. Ann Surg 2002; 236: 315-322; discussion 322-323.
23. Dvorchik I, Subotin M, Marsh W, et al. Performance of multi-layer feed forward neural networks to predict liver transplantation outcome. Methods Inf Med 1996; 35: 12-18.
24. Merion RM. When is a patient too well and when is a patient too sick for a liver transplant? Liver Transpl 2004; 10(Suppl 2): s69-73.