Novel model for predicting carpal tunnel syndrome and abnormal nerve conduction

Carpal Tunnel Syndrome (CTS) occurs with a relatively high prevalence in manufacturing type facilities. The costs, both financial and human, associated with CTS can be high. Prevention of CTS requires that risk factors be properly identified and combined and/or ranked in order to direct the abatement process. Though it is known that certain personal and physiological factors contribute to the development of CTS, a strong, predictive correlation between job physical factors and CTS is still a developing area. A model was built to predict CTS and the related symptoms of numbness/tingling in areas innervated by the median nerve and associated abnormal nerve conduction outcomes. This model utilizes physical factors such as effort, intensity, detailed posture components, "busy time," and efforts per minute among other observable physical job attributes. Data were collected as part of a large prospective epidemiological study and analyzed using the Utah Ergonomic Analysis (UEA) method and program. Data suggest that the model as it currently stands has significant correlation with outcome components, although it requires further development. Prediction of left hand health outcomes is significant for most of the outcomes studied, but the prediction of right hand outcomes was problematic. The predictive ability of personal and physiological factors was reinforced. This paper discusses the development of this model, the data produced by the model, the findings resulting from the development process, and the direction of future work in model development.