Would Artificial Neural Networks Implemented in Clinical Wards Help Nephrologists in Predicting Epoetin Responsiveness?

Luca Gabutti; Nathalie Lötscher; Josephine Bianda; Claudio Marone; Giorgio Mombelli; Michel Burnier

Disclosures

BMC Nephrology 

In This Article

Results

Population Characteristics

The 12-month follow-up of the AIMS survey was initiated in June 2002. 340 patients from 26 centres were included in the final analysis. The mean age of the participating patients was 63 ± 15 y. The most common causes of end-stage renal disease were glomerulonephritis (23%), diabetic nephropathy (21%) and hypertension (21%). The most prevalent baseline co-morbidities were cardiac-related. Hypertension occurred in 61% of the patients and diabetes was reported in 27%. The mean haemoglobin concentration was 11.8 ± 1.4 g/dl with approximately 80% of the included patients achieving the target haemoglobin of at least 11 g/dL. The mean epoetin beta dose was 149 ± 104 IU/kg/week.

Ninety-two patients (1104 monthly clinical and biochemical data) of the 3 dialysis units of the renal services of the Ente Ospedaliero Cantonale (EOC), Bellinzona, Switzerland, all meeting the requisite criteria, were also included in the analysis.

The basic information including comorbidity incidence, ACE-I or ARB treatment and mean results of the monthly biochemical parameters of the AIMS and EOC data pools are listed in Table 1 (recurrent abbreviations in the tables and figures are summarized in Table 2 ).

Intra- and Inter-Individual Variability of the Epoetin Dose

In the 2 databases pooled together (AIMSEOC data) the intra- and inter-individual variability in the epoetin dose prescribed, expressed by the mean absolute deviation from the mean, were 24.7 ± 27.1 and 49.8 ± 48.0 U/Kg/week (P < 0.001) respectively.

Linear Correlations Between Variables and Epoetin Dose

In the same data pool the variables correlating significantly in a multiple linear regression with the epoetin dose (after intra- or extrapolation for a haemoglobin of 11.5 g/dL) were weight (standardized coefficient beta (β): -1.673; p < 0.001), ferritin (β: 0.079; P < 0.001), age (β: -0.800; P < 0.05), epoetin administration route (subcutaneous vs. intravenous) (β: -22.730; P < 0.05) and presence or absence of a cardiomyopathy (β: 33.050; P < 0.01). The obtained linear model with a constant of 301.686 (P < 0.001) explained 40.3 % of the variability in the epoetin dose (1.6 % imputable to the epoetin administration route; the intravenous one being associated with an epoetin dose 22.73 U/Kg/week higher). Taking the hemoglobin (before the intra- or extrapolation process) as a dependent variable, the epoetin dose (β: -0.003) and the epoetin administration route (subcutaneous vs. intravenous) (β:-0.528) were the only two significant variables (P < 0.001 in both cases) in building a linear model (constant: 12.980, R: 0.405). The epoetin administration route explained 10.2% of the haemoglobin variability in the model; the intravenous one being associated with a lower haemoglobin by 0.528 g/dL.

Non-Linear Correlations Between Variables and Epoetin Dose

The results of the data analysis, performed with both the AIMSEOC and the EOC data to evaluate the non-linear impact of individual variables in the prediction of the mean epoetin dose required for an individual patient to reach the haemoglobin target of 11.5 g/dL are depicted graphically in Figure 2 (Panels A and B). In both Panels the prediction power of individual and grouped variables is compared in a performance gradient using the ratio between the correlation r and the normalized mean square error (the higher the value the better the performance). In Panel A the prediction power of the variables of the AIMSEOC data pool is compared with the result of the best performing linear regression (based on weight and serum ferritin). In Panel B the prediction power of the variables of the EOC data pool is shown. The final network structure (either Multilayer Perceptrons (MLP) or Generalized Feedforward Networks (GFN) and number of processing elements in the hidden layer) is specified, in both figures, in the label of the ANN used for the prediction.

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).

Prediction of the Epoetin Requirement in an Individual Patient

The linear regressions, performed for each individual patient, plotting epoetin dose against haemoglobin with the aim of estimating the epoetin dose that should have been prescribed in order to obtain a haemoglobin of 11.5 g/dL, allowed, with a r value of 0.247 ± 0.237, to obtain an intrapolated value in 91% of the patients and an extrapolated one in 9%.

The prediction ability of the best performing linear regression (using as input variables weight and ferritin; β:-1.865 and 0.113 respectively; P < 0.001 for both; R:0.431; constant: 235.141) and ANN (using as input variables weight, age, presence or absence of an impaired left ventricular ejection fraction, serum creatinine and ferritin) is further compared using ROC curves in Figure 3 (epoetin dose cut-off 100 IU/Kg/week).

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).

Prediction of the Monthly Epoetin Dose Adjustments

For the prediction of the monthly epoetin dose adjustments required to maintain the haemoglobin in the target range the training, cross-validation, testing and validation data pool consisted respectively of 200, 40, 140 and 110 monthly clinical and biochemical data taken from the AIMSEOC pool, including for each individual the haemoglobin and epoetin dose of the two previous months and the haemoglobin of the following one. The mean haemoglobin from the current observation (Hb), the 2 previous (Hb-1 and Hb-2) and the following (Hb+1) month and the mean epoetin dose from the current observation (EPO) and the 2 previous (EPO-1 and EPO-2) months were as follows: Hb: 11.51 ± 1.14, Hb-1: 11.55 ± 1.14, Hb-2: 11.54 ± 1.13, Hb+1: 11.52 ± 1.04, EPO: 83.97 ± 60.67, EPO-1: 87.05 ± 60.15, EPO-2: 85.34 ± 58.00. The remaining variables did not give a significant contribution in building an efficient non linear model and were excluded from the final algorithm.

The accuracy, the agreement and the r/NMSE ratio in predicting the haemoglobin of one month later (follow-up haemoglobin) by the nephrologists in charge of the patients and the best performing ANN (using as input variables the haemoglobin and epoetin dose from the current and the 2 previous months and being structured as a Generalized Feedforward Network with 6 hidden neurons) is depicted in Table 3 . The prediction ability of both the nephrologists and the ANN is demonstrated using ROC curves in Figure 4 (haemoglobin cut-off 11.0 g/dL).

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).

The sensitivity, the specificity, the positive and negative predictive value in predicting a follow-up haemoglobin < 11.0 g/dL of the best performing ANN compared with the nephrologists were 0.48 (27/56) versus 0.25 (14/56) P < 0.05, 0.92 (132/143) versus 0.83 (119/143) P < 0.05, 0.71 (27/38) vs. 0.39 (14/36) P < 0.01 and 0.82 (132/161) vs. 0.73 (119/162) n.s. respectively.

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