Predicting technique survival in peritoneal dialysis patients: Comparing artificial neural networks and logistic regression

Nephrology Dialysis Transplantation

Volumn 23, Issue 9, 2008, Pages 2972-2981

  Tangri, Navdeep ^a   Ansell, David ^b   Naimark, David ^c,d  

^a MCGILL UNIVERSITY   (Canada)

^b UK RENAL REGISTRY   (United Kingdom)

^c SUNNYBROOK RESEARCH INSTITUTE   (Canada)

^d UNIVERSITY OF TORONTO   (Canada)

Author keywords

Artificial neural networks; Early technique failure; Logistic regression; Peritoneal dialysis; Technique survival

Indexed keywords

ACCURACY; ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CAUCASIAN; FEMALE; HEMODIALYSIS PATIENT; HUMAN; INTERMETHOD COMPARISON; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; PERITONEAL DIALYSIS; PRIORITY JOURNAL; SURVIVAL; TECHNIQUE;

ALGORITHMS; FEMALE; GREAT BRITAIN; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PERITONEAL DIALYSIS; REGISTRIES; ROC CURVE; TREATMENT FAILURE;

EID: 58549103311     PISSN: 09310509     EISSN: 14602385     Source Type: Journal    
DOI: 10.1093/ndt/gfn187     Document Type: Article

References (43)

1. Grassmann A, Gioberge S, Moeller S. ESRD patients in 2004: global overview of patient numbers, treatment modalities and associated trends. Nephrol Dial Transplant 2005; 20: 2587-2593
2. Sennfalt K, Magnusson M, Carlsson P. Comparison of hemodialysis and peritoneal dialysis - a cost-utility analysis. Perit Dial Int 2002; 22: 39-47
3. Liem YS, Wong JB, Hunink MG et al. Comparison of hemodialysis and peritoneal dialysis survival in The Netherlands. Kidney Int 2007; 71: 153-158
4. Rubin HR, Fink NE, Plantinga LC et al. Patient ratings of dialysis care with peritoneal dialysis versus hemodialysis [see comment]. JAMA 2004; 291: 697-703
5. Little J, Irwin A, Marshall T et al. Predicting a patient's choice of dialysis modality: experience in a United Kingdom renal department. Am J Kidney Dis 2001; 37: 981-986
6. Ansell D, Feest TG, Tomson C et al. UK Renal Registry Report 2006. Bristol, UK: UK Renal Registry, 2007
7. Jager KJ, Korevaar JC, Dekker FW et al. Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD) Study Group. The effect of contraindications and patient preference on dialysis modality selection in ESRD patients in The Netherlands [see comment]. Am J Kidney Dis 2004; 43: 891-899
8. Rumpsfeld M, McDonald SP, Johnson DW. Higher peritoneal transport status is associated with higher mortality and technique failure in the Australian and New Zealand peritoneal dialysis patient populations. J Am Soc Nephrol 2006; 17: 271-278
9. Tonelli M, Hemmelgarn B, Manns B et al. Use and outcomes of peritoneal dialysis among Aboriginal people in Canada. J Am Soc Nephrol 2005; 16: 482-488
10. McDonald SP, Collins JF, Johnson DW. Obesity is associated with worse peritoneal dialysis outcomes in the Australia and New Zealand patient populations. J Am Soc Nephrol 2003; 14: 2894-2901
11. Huisman RM, Nieuwenhuizen MGM, Th de Charro F. Patient-related and centre-related factors influencing technique survival of peritoneal dialysis in The Netherlands. Nephrol Dial Transplant 2002; 17: 1655-1660
12. Churchill DN, Thorpe KE, Nolph KD et al. The Canada-USA (CANUSA) Peritoneal Dialysis Study Group. Increased peritoneal membrane transport is associated with decreased patient and technique survival for continuous peritoneal dialysis patients. J Am Soc Nephrol 1998; 9: 1285-1292
13. Penny W, Frost D. Neural networks in clinical medicine [Review] [91 refs]. Med Decis Making 1996; 16: 386-398
14. Itchhaporia D, Snow PB, Almassy RJ et al. Artificial neural networks: current status in cardiovascular medicine [Review] [39 refs]. J Am Col Cardiol 1996; 28: 515-521
15. Lisboa PJ. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw 2002; 15: 11-39
16. Varghese SA, Powell TB, Budisavljevic MN et al. Urine biomarkers predict the cause of glomerular disease. J Am Soc Nephrol 2007; 18: 913-922
17. Wang YF, Hu TM, Wu CC et al. Prediction of target range of intact parathyroid hormone in hemodialysis patients with artificial neural network. Computer Methods Programs Biomed 2006; 83: 111-119
18. Gabutti L, Ferrari N, Mombelli G et al. Does cystatin C improve the precision of Cockcroft and Gault's creatinine clearance estimation? J Nephrol 2004; 17: 673-678
19. Gabutti L, Lotscher N, Bianda J et al. Would artificial neural networks implemented in clinical wards help nephrologists in predicting epoetin responsiveness? BMC Nephrol 2006; 7: 13
20. Chen CA, Lin SH, Hsu YJ et al. Neural network modeling to stratify peritoneal membrane transporter in predialytic patients. Intern Med 2006; 45: 663-664
21. Parekattil SJ, Kumar U, Hegarty NJ et al. External validation of outcome prediction model for ureteral/renal calculi. J Urol 2006; 175: 575-579
22. Oates JC, Varghese S, Bland AM et al. Prediction of urinary protein markers in lupus nephritis [see comment]. Kidney Int 2005; 68: 2588-2592
23. Nielsen M, Granerus G, Ohlsson M et al. Interpretation of captopril renography using artificial neural networks. Clin Physiol Funct Imaging 2005; 25: 293-296
24. Dimitrov BD, Ruggenenti P, Stefanov R et al. Chronic nephropathies: individual risk for progression to end-stage renal failure as predicted by an integrated probabilistic model. Nephron 2003; 95: c47-c59
25. Brier ME, Ray PC, Klein JB. Prediction of delayed renal allograft function using an artificial neural network. Nephrol Dial Transplant 2003; 18: 2655-2659
26. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. New York: Chapman and Hall, 1993
27. Metz CE, Jiang Y, MacMahon H et al. ROCKIT: maximum likelihood estimation to fit a binormal ROC curve to continuously-distributed data and/or ordinal category data, 2001. http://www-radiology.uchicago.edu/krl/KRL-ROC/ software-index6.htm
28. Metz CE, CLABROC. Statistical analysis of ROC data in evaluating diagnostic performance (1.9.1) Computer Software. 1998
29. Naimark DMJ. A comparison of regression methods with artificial neural networks for the prediction of the outcome of patients on chronic dialysis. 1-1-2000, masters thesis, University of Toronto
30. Hanley JA. The use of the 'binormal' model for parametric ROC analysis of quantitative diagnostic tests. Stat Med 1996; 15: 1575-1585
31. Hanley JA, McNeil B. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983; 148: 839-843
32. Hanley JA, McNeil B. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 29-36
33. Swets JA. Measuring the accuracy of diagnostic systems. Science 1988; 240: 1285-1293
34. Green M, Bjork J, Forberg J et al. Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room. Artif Intell Med 2006; 38: 305-318
35. Peng SY, Wu KC, Wang JJ et al. Predicting postoperative nausea and vomiting with the application of an artificial neural network. Br J Anaesth 2007; 98: 60-65
36. Lowrie EG, Huang WH, Lew NL. Death risk predictors among peritoneal dialysis and hemodialysis patients: a preliminary comparison. Am J Kidney Dis 1995; 26: 220-228
37. Lowrie EG, Lew NL, Huang WH. Race and diabetes as death risk predictors in hemodialysis patients. Kidney Int Suppl 1992; 38: S22-S31
38. Lowrie EG, Lew NL. Death risk in hemodialysis patients: the predictive value of commonly measured variables and an evaluation of death rate differences between facilities. Am J Kidney Dis 1990; 15: 458-482
39. Hemmelgarn BR, Culleton BF, Ghali WA. Derivation and validation of a clinical index for prediction of rapid progression of kidney dysfunction. Q J Med 2007; 100: 87-92
40. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by Error propagation. In Rumelhart DE, McLelland JL (eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press Cambridge, MA, 1994, pp 318-364
41. Dorfman DE, Alf E. Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervals - rating-method data. J Math Psychol 1969; 6: 487-496
42. Peng F, Hall WJ. Bayesian analysis of ROC curves using Markovchain Monte Carlo methods. Med Decis Making 1996; 16: 404-411
43. Van Der Schouw YT, Straatman H, Verbeek AL. ROC curves and the areas under them for dichotomized tests: empirical findings for logistically and normally distributed diagnostic test results. Med Decis Making 1994; 14: 374-381