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BioMed Research International
Volume 2014, Article ID 786418, 6 pages
http://dx.doi.org/10.1155/2014/786418
Research Article

Predicting Postoperative Vomiting for Orthopedic Patients Receiving Patient-Controlled Epidural Analgesia with the Application of an Artificial Neural Network

1Department of Electrical Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, Taoyuan 333, Taiwan
2Portable Energy System Group, Green Technology Research Center, College of Engineering, Chang Gung University, Taoyuan 333, Taiwan
3Department of Biomedical Engineering, College of Basic Medical Sciences, China Medical University, Shenyang, Liaoning 110001, China
4Department of Anesthesiology, Taipei Veterans General Hospital and National Yang-Ming University, No. 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan
5Section of Anesthesiology, Ton-Yen General Hospital, Hsinchu 302, Taiwan

Received 28 February 2014; Revised 19 June 2014; Accepted 16 July 2014; Published 5 August 2014

Academic Editor: Balachundhar Subramaniam

Copyright © 2014 Cihun-Siyong Alex Gong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Patient-controlled epidural analgesia (PCEA) was used in many patients receiving orthopedic surgery to reduce postoperative pain but is accompanied with certain incidence of vomiting. Predictions of the vomiting event, however, were addressed by only a few authors using logistic regression (LR) models. Artificial neural networks (ANN) are pattern-recognition tools that can be used to detect complex patterns within data sets. The purpose of this study was to develop the ANN based predictive model to identify patients with high risk of vomiting during PCEA used. From January to March 2007, the PCEA records of 195 patients receiving PCEA after orthopedic surgery were used to develop the two predicting models. The ANN model had a largest area under curve (AUC) in receiver operating characteristic (ROC) curve. The areas under ROC curves of ANN and LR models were 0.900 and 0.761, respectively. The computer-based predictive model should be useful in increasing vigilance in those patients most at risk for vomiting while PCEA is used, allowing for patient-specific therapeutic intervention, or even in suggesting the use of alternative methods of analgesia.