Multivariate prediction of upper limb prosthesis acceptance or rejection

Abstract

Objective. To develop a model for prediction of upper limb prosthesis use or rejection.

Design. A questionnaire exploring factors in prosthesis acceptance was distributed internationally to individuals with upper limb absence through community-based support groups and rehabilitation hospitals.

Subjects. A total of 191 participants (59 prosthesis rejecters and 132 prosthesis wearers) were included in this study.

Methods. A logistic regression model, a C5.0 decision tree, and a radial basis function neural network were developed and compared in terms of sensitivity (prediction of prosthesis rejecters), specificity (prediction of prosthesis wearers), and overall cross-validation accuracy.

Results. The logistic regression and neural network provided comparable overall accuracies of approximately 84 ± 3%, specificity of 93%, and sensitivity of 61%. Fitting time-frame emerged as the predominant predictor. Individuals fitted within two years of birth (congenital) or six months of amputation (acquired) were 16 times more likely to continue prosthesis use.

Conclusions. To increase rates of prosthesis acceptance, clinical directives should focus on timely, client-centred fitting strategies and the development of improved prostheses and healthcare for individuals with high-level or bilateral limb absence. Multivariate analyses are useful in determining the relative importance of the many factors involved in prosthesis acceptance and rejection.

Keywords:

• Upper extremity
• prostheses
• artificial limbs
• multivariate analysis
• healthcare quality
• access
• evaluation

Notes

Supported by the Natural Science and Engineering Research Council, Canada. We certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated.