ArticleNephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association2008
Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression.
Article in Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence and machine learning in peritoneal dialysis: a systematic review of clinical outcomes and predictive modeling.International urology and nephrology · 2024Pooled it
- Peritoneal dialysis effluent biomarkers from a multi-omics and artificial intelligence perspective: advances and challenges.Clinical kidney journal · 2026Review
- CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks.Journal of healthcare informatics research · 2024Article
- Improving Diagnostics with Deep Forest Applied to Electronic Health Records.Sensors (Basel, Switzerland) · 2023Article
- Artificial intelligence in peritoneal dialysis: general overview.Renal failure · 2022Article
- Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining.Annals of translational medicine · 2020Article
- An Equation Based on Fuzzy Mathematics to Assess the Timing of Haemodialysis Initiation.Scientific reports · 2019Article
- Mortality predicted accuracy for hepatocellular carcinoma patients with hepatic resection using artificial neural network.TheScientificWorldJournal · 2013Article
- Patterns Prediction of Chemotherapy Sensitivity in Cancer Cell lines Using FTIR Spectrum, Neural Network and Principal Components Analysis.Iranian journal of pharmaceutical research : IJPR · 2012Article
- Predicting mortality in incident dialysis patients: an analysis of the United Kingdom Renal Registry.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2011Article
- The Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS): Unifying Efforts to Inform Practice and Improve Global Outcomes in Peritoneal Dialysis.Peritoneal dialysis international : journal of the International Society for Peritoneal DialysisArticle
- Simultaneous Catheter Replacement for Infectious and Mechanical Complications Without Interruption of Peritoneal Dialysis.Peritoneal dialysis international : journal of the International Society for Peritoneal DialysisArticle
- Impact of hernias on peritoneal dialysis technique survival and residual renal function.Peritoneal dialysis international : journal of the International Society for Peritoneal DialysisArticle
- Is transition between peritoneal dialysis and hemodialysis really a gradual process?Peritoneal dialysis international : journal of the International Society for Peritoneal DialysisArticle
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly technique failure has been a major limitation on the wider adoption of peritoneal dialysis (PD). The objectives of this study were to use data from a large, multi-centre, prospective database, the United Kingdom Renal Registry (UKRR), in order to determine the ability of an artificial neural network (ANN) model to predict early PD technique failure and to compare its performance with a logistic regression (LR)-based approach.
methodsThe analysis included all incident PD patients enrolled in the UKRR from 1999 to 2004. The event of interest was technique failure. For both the ANN and LR analyses a bootstrap approach was used: the data were divided into 20 random training (75%) and validation (25%) sets. Models were derived on the latter and then used to make predictions on the former. Predictive accuracy was assessed by area under the ROC curve (AUROC). The 20 AUROC values and their standard errors were then averaged.
resultsThere were 3269 patients included in the analysis with a mean age of 59.9 years and a mean observation time of 430 days. Of the patients, 38.3% were female and 90.8% were Caucasian. 1458 patients (44.6%) suffered technique failure. The AUROC for the ANN model was 0.760 +/- 0.0167 and the LR model was 0.709 and 0.0208. (P = 0.0164)
conclusionsUsing UKRR data, both ANN and LR models predicted early PD technique failure with moderate accuracy. In this study, an ANN outperformed an LR-based approach. As the scope and the completeness of the UKRR increases, the question of whether more sophisticated ANN models will perform even better remains for further study.
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