Prediction of target range of intact parathyroid hormone in hemodialysis patients with artificial neural network
Journal
Computer Methods and Programs in Biomedicine
Journal Volume
83
Journal Issue
2
Pages
111-119
Date Issued
2006
Author(s)
Abstract
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. ? 2006 Elsevier Ireland Ltd. All rights reserved.
Subjects
Hemodialysis; Neural network; Parathyroid hormone
SDGs
Other Subjects
Hemodialysis; Hemodialysis patients; Parathyroid hormone; Plasma iPTH; Biomedical engineering; Data reduction; Dialysis; Diseases; Neural networks; Hormones; albumin; calcium; hemoglobin; parathyroid hormone; adult; aged; article; artificial neural network; biochemistry; concentration (parameters); controlled study; diabetes mellitus; female; hemodialysis; human; hypertension; major clinical study; male; prediction; prospective study; receiver operating characteristic; retrospective study; statistics; validation process; Female; Humans; Kidney Failure, Chronic; Male; Middle Aged; Neural Networks (Computer); Parathyroid Hormone; Predictive Value of Tests; Prospective Studies; Renal Dialysis; Retrospective Studies; ROC Curve
Type
journal article