Prediction of traumatic wound infection with a neural network-derived decision model☆☆☆★

Abstract 

The objective of this study was to develop and validate a decision model, using an artificial neural network, that predicts infection in uncomplicated, traumatic, sutured wounds. The study was a prospective, cohort study of all patients presenting to the emergency department of a county teaching hospital with uncomplicated wounds that required suturing. In evaluating and treating wounds, emergency medicine (EM) faculty and residents, resident physicians in primary-care specialties, and supervised medical students on EM clerkships followed a standardized wound-management protocol. Clinicians estimated the likelihood of subsequent infection using a 5-point scale. Wound healing was followed until sutures were removed. Wound outcome data were collected by medical personnel blinded to the initial prediction. Student's t-tests and Pearson's chi-square statistic were used to identify independent predictors that served as input variables. Wound infection was the single output variable. Neural network analysis was used to assign weights to input variables and derive a decision equation. A total of 1,142 wounds were analyzed in the study. The overall infection rate was 7.2%. The most predictive factors for wound infection were wound location, wound age, depth, configuration, contamination, and patient age. To derive a decision equation for the model, the network was trained on data from half of the subjects and tested on the remainder. When used as a diagnostic test for wound infection, the decision model had a sensitivity of 70%, as compared to 54% for physicians, and a specificity of 76%, as compared to 78% for physicians. We conclude that through the use of combinations of 7 clinical variables available at the time of initial wound management, a neural network-derived decision model may be used to identify uncomplicated, traumatic wounds at higher risk for infection. (Am J Emerg Med 2003;21:000-000. Copyright 2003, Elsevier Science (USA). All rights reserved.)

Keywords:  Wound, wound management, wound infection, pattern recognition, neural network