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

Abstract

Background: Early 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.

Methods: The 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.

Results: There 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)

Conclusions: Using 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.

Fig. 1

Artificial neural network (ANN) architecture. ANNs consist of artificial neurons. Each artificial neuron has a processing node (‘body’) represented by circles in the figure as well as connections from (‘dendrites’) and connections to (‘axons’) other neurons which are represented as arrows in the figure. In a commonly used ANN architecture, the multilayer perceptron, the neurons are arranged in layers. An ordered set (a vector) of predictor variables is presented to the input layer. Each neuron of the input layer distributes its value to all of the neurons in the middle layer. Along each connection between input and middle neurons there is a connection weight so that the middle neuron receives the product of the value from the input neuron and the connection weight. Each neuron in the middle layer takes the sum of its weighted inputs and then applies a non-linear (usually logistic) function to the sum. The result of the function then becomes the output from that particular middle neuron. Each middle neuron is connected to the output neuron. Along each connection between a middle neuron and the output neuron there is a connection weight. In the final step, the output neuron takes the weighted sum of its inputs and applies the non-linear function to the weighted sum. The result of this function becomes the output for the entire ANN. More details are provided in the appendix.

Fig. 2

The Kaplan–Meier curve for probability of peritoneal dialysis (PD) technique failure after the initiation of PD. Survival until 31 December 2004, death, transplantation or loss to follow-up with functioning PD were considered to be censored observations.

Fig. 3

Receiver-operating characteristic (ROC) curves for the artificial neural network (ANN) and logistic regression bootstrap analyses (see the text for a description of the bootstrap procedure). The curves represent the average curves for the 20 ANN and 20 logistic models. The area under the ROC curve (AUROC) is an index of predictive performance: an AUROC of 1.0 represents perfect discrimination while a value of 0.5 indicates no discrimination between subjects with and without PD technique failure. The average AUROC values for the ANN and logistic regression models were 0.760 and 0.709, respectively (P = 0.0164).

Fig. 4

Histogram of the artificial neural network (ANN) model output when applied to the validation set for subjects who did and did not suffer PD technique failure. For each of 20 bootstrap samples, the data were randomly divided into a training set from which an ANN model was derived, and a validation set on which the ANN was validated. The histogram data represent one of the 20 sets of validation set predictions selected at random.

Fig. 5

Histogram of the logistic regression model output when applied to the validation set for subjects who did and did not suffer PD technique failure. For each of 20 bootstrap samples, the data were randomly divided into a ‘training set’ from which a regression model was derived, and a validation set on which the regression model was validated. The histogram data represent one of the 20 sets of validation set predictions selected at random.