Would artificial neural networks implemented in clinical wards help nephrologists in predicting epoetin responsiveness?

Abstract

Background: Due to its strong intra- and inter-individual variability, predicting the ideal erythropoietin dose is a difficult task. The aim of this study was to re-evaluate the impact of the main parameters known to influence the responsiveness to epoetin beta and to test the performance of artificial neural networks (ANNs) in predicting the dose required to reach the haemoglobin target and the monthly dose adjustments.

Methods: We did a secondary analysis of the survey on Anaemia Management in dialysis patients in Switzerland; a prospective, non-randomized observational study, enrolling 340 patients of 26 centres and in order to have additional information about erythropoietin responsiveness, we included a further 92 patients from the Renal Services of the Ente Ospedaliero Cantonale, Bellinzona, Switzerland. The performance of ANNs in predicting the epoetin dose was compared with that of linear regressions and of nephrologists in charge of the patients.

Results: For a specificity of 50%, the sensitivity of ANNs compared with linear regressions in predicting the erythropoietin dose to reach the haemoglobin target was 78 vs. 44% (P < 0.001). The ANN built to predict the monthly adaptations in erythropoietin dose, compared with the nephrologists' opinion, allowed to detect 48 vs. 25% (P < 0.05) of the patients treated with an insufficient dose with a specificity of 92 vs. 83% (P < 0.05).

Conclusion: In predicting the erythropoietin dose required for an individual patient and the monthly dose adjustments ANNs are superior to nephrologists' opinion. Thus, ANN may be a useful and promising tool that could be implemented in clinical wards to help nephrologists in prescribing erythropoietin.

Figure 1

Schematic representation of an artificial neural network. A typical ANN consisting of one input layer, two hidden layers and one output layer is represented. The basic structure, fed forwards and trained by back-propagation is called Multilayer Perceptron (MLP) while models designed with connections jumping over hidden layers (---) are called Generalized Feedforward Networks (GFN).

Figure 2

Performance ability of individual and combined variables in predicting the epoetin dose. Performance ability of individual and combined variables in predicting the mean epoetin beta dose required for an individual patient to reach the haemoglobin target of 11.5 g/dL using ANNs and linear regressions. The performance ability is expressed by the r/NMSE (the higher the value the better the performance). The network structure (either Multilayer Perceptron (MLP) or Generalized Feedforward Network (GFN) and number of processing elements in the hidden layer) is specified in the label of the ANN used for the prediction. Panel A: data from the AIMSEOC (training, cross-validation, testing and validation data pool: 170, 30, 122 and 110 patients respectively); the column of the linear regression is in black; individual variables are highlighted in grey. Panel B: data from the EOC alone (training, testing and validation data pool: 60, 10 and 22 patients respectively).

Figure 3

Prediction of the epoetin dose required to reach the haemoglobin target. ROC curves plotting sensitivity against 1 minus specificity for a epoetin dose cut-off of 100 IU/Kg/week in the prediction of the dose required for an individual patient to reach the haemoglobin target of 11.5 g/dL obtained from the best performing linear regression (dotted line; using as input variables weight and ferritin) and the best performing ANN (continuous line; using as input variables weight, age, presence or absence of an impaired left ventricular ejection fraction, serum creatinine and ferritin). The areas under the curves, the 95% confidence intervals and the significance P for the linear regression and the ANN were respectively: 0.491 (0.416–0.565), P:n.s. and 0.728 (0.663–0.794), P < 0.001 (P < 0.001 for the difference between the two curves).

Figure 4

Prediction of the follow-up haemoglobin. ROC curves plotting sensitivity against 1 minus specificity for a cut-off of 11.0 g/dL in the prediction of the haemoglobin one month later obtained from the nephrologists (dotted line) and from the best performing ANN (continuous line) (using as input variables the haemoglobin and epoetin dose from the currently and the 2 previous months). The areas under the curves, the 95% confidence intervals and the significance P for the Nephrologists and the ANN were respectively: 0.772 (0.702–0.881), P < 0.001 and 0.822 (0.758–0.887), P < 0.001(the difference between the two curves was not significant).