Prediction of target range of intact parathyroid hormone in hemodialysis patients with artificial neural network

The application of artificial neural network (ANN) to predict outcome and explore potential relationships among clinical data is increasing being used in many clinical scenarios. The aim of this study was to validate whether an ANN is a useful tool for predicting the target range of plasma intact parathyroid hormone (iPTH) concentration in hemodialysis patients. An ANN was constructed with input variables collected retrospectively from an internal validation group (n = 129) of hemodialysis patients. Plasma iPTH was the dichotomous outcome variable, either target group (150 ng/L ≤ iPTH ≤ 300 ng/L) or non-target group (iPTH < 150 ng/L or iPTH hormone >300 ng/L). After internal validation, the ANN was prospectively tested in an external validation group (n = 32) of hemodialysis patients. The final ANN was a multilayer perceptron network with six predictors including age, diabetes, hypertension, and blood biochemistries (hemoglobin, albumin, calcium). The externally validated ANN provided excellent discrimination as appraised by area under the receiver operating characteristic curve (0.83 ± 0.11, p = 0.003). The Hosmer–Lemeshow statistic was 5.02 (p = 0.08 > 0.05) which represented a good-fit calibration. These results suggest that an ANN, which is based on limited clinical data, is able to accurately forecast the target range of plasma iPTH concentration in hemodialysis patients.