Prediction of Graft Survival of Living-Donor Kidney Transplantation: Nomograms or Artificial Neural Networks?

Transplantation

Volumn 86, Issue 10, 2008, Pages 1401-1406

  Akl, Ahmed ^a   Ismail, Amani M ^a   Ghoneim, Mohamed ^a  

^a MANSOURA UNIVERSITY   (Egypt)

Author keywords

Artificial neural networks; Kidney transplantation; Nomogram; Predicting graft survival; Prognostic models

Indexed keywords

ACCURACY; ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; GRAFT SURVIVAL; HUMAN; INTERMETHOD COMPARISON; KIDNEY TRANSPLANTATION; LIVING DONOR; MAJOR CLINICAL STUDY; NOMOGRAM; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; SENSITIVITY AND SPECIFICITY; UNIVARIATE ANALYSIS; AGE; ALGORITHM; FAMILY; HISTOCOMPATIBILITY TEST; NERVE CELL NETWORK; PATIENT SELECTION; PHYSIOLOGY; PREDICTION AND FORECASTING; REPRODUCIBILITY; RETROSPECTIVE STUDY; STATISTICAL MODEL;

AGE FACTORS; ALGORITHMS; FAMILY; GRAFT SURVIVAL; HISTOCOMPATIBILITY TESTING; HUMANS; KIDNEY TRANSPLANTATION; LIVING DONORS; MODELS, STATISTICAL; NERVE NET; NOMOGRAMS; PATIENT SELECTION; PREDICTIVE VALUE OF TESTS; REPRODUCIBILITY OF RESULTS; RETROSPECTIVE STUDIES;

EID: 58149358848     PISSN: 00411337     EISSN: None     Source Type: Journal    
DOI: 10.1097/TP.0b013e31818b221f     Document Type: Article

References (17)

1. Grossberg JA, Reinert SE, Monaco AP, et al. Utility of a mathematical nomogram to predict delayed graft function: A single-center experience. Transplantation 2006; 81: 155.
2. Djavan B, Remzi M, Zlotta A, et al. Novel artificial neural network for early detection of prostate cancer. J Clin Oncol 2002; 20: 921.
3. Kattan MW. Comparison of Cox regression with other methods for determining prediction models and nomograms. J Urol 2003; 170: S6.
4. Kattan MW. When and how to use informatics tools in caring for urologic patients. Nat Clin Pract Urol 2005; 2: 183.
5. Akl A, Mostafa A, Ghoneim MA. Nomogram that predicts graft survival probability following living-donor kidney transplant. Exp Clin Transplant 2008; 6: 30.
6. Freeman RV, Eagle KA, Bates ER, et al. Comparison of artificial neural networks with logistic regression in prediction of in-hospital death after percutaneous transluminal coronary angioplasty. Am Heart J 2002; 140: 511.
7. Poli F, Scalamogna M, Cardillo M, et al. An algorithm for cadaver kidney allocation based on a multivariate analysis of factors impacting on cadaver kidney graft survival and function. Transpl Int 2000; 13(suppl 1): S259.
8. Zapletal C, Lorenz MW, Woeste G, et al. Predicting creatinine clearance by a simple formula following live- donor kidney transplantation. Transpl Int 2004; 17: 490.
9. Goldfarb-Rumyantzev AS, Scandling JD, Pappas L, et al. Prediction of 3-year cadaveric graft survival based on pre-transplant variables in a large national dataset. Clin Transplant 2003;17: 485.
10. Krikov S, Khan A, Baird BC, et al. Predicting kidney transplant survival using tree-based modeling. ASAIO J 2007; 53: 592.
11. Brier ME, Ray PC, Klein JB. Prediction of delayed renal allograft function using an artificial neural network. Nephrol Dial Transplant 2003; 18: 2655.
12. Kattan MW. When and how to use informatics tools in caring for urologic patients. Nat Clin Pract Urol 2005; 2: 183.
13. Kattan MW, Stapleton AMF, Wheeler TM, et al. Evaluation of a nomogram usedto predict the pathologic stage of clinically localized prostate carcinoma. Cancer 1997; 79: 528.
14. Graefen BM, Karakiewicz PI, Cagiannos I, et al. Validation study of the accuracy of a postoperative nomogram for recurrence after radical prostatectomy for localized prostate cancer. J Clin Oncol 2002; 20: 951.
15. Eilber FC, Brennan MF, Eilber FR, et al. Validation of the postoperative nomogram for 12-year sarcoma-specific mortality. Cancer 2004; 101: 2270.
16. Peeters KC, Kattan MW, Hartgrink HH, et al. Validation of a nomogram for predicting disease specific survival after an R0 resection for gastric carcinoma. Cancer 2005; 103: 702.
17. Ripley RM, Harris AL, Tarassenko L.Non-linear survival analysis using neural networks. Stat Med 2004; 23: 825.