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.
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.
Authors: Ecaterina Vasluian; Ingrid G M de Jong; Wim G M Janssen; Margriet J Poelma; Iris van Wijk; Heleen A Reinders-Messelink; Corry K van der Sluis Journal: PLoS One Date: 2013-06-24 Impact factor: 3.240