Date of Award


Degree Type


Degree Name

Doctor of Philosophy


This study was undertaken to develop a clinically useful model for predicting electrophysiological abnormality in the nerve conduction tests used in the diagnosis of carpal tunnel syndrome (CTS). The prediction model combined commonly recorded clinical history, signs, and symptoms in a multivariate analysis. Electrophysiological abnormality and adjustments for the influencing variables (sex, distance, and temperature) were based on normative nerve conduction values obtained in a control group (n = 104). This was the first study in this topic area in which prediction bands were used to adjust the criteria for assigning abnormality to account for influencing variables. The variables used to represent nerve conduction abnormality were selected based on the literature, clinical usage, and investigation of their interrelationships.;Two hundred and eighty-five subjects with signs and symptoms of CTS who were referred for nerve conduction testing were evaluated by interview and physical examination by an investigator who was blind to the electrodiagnosis. Electrodiagnosis was performed without knowledge of the clinical information. The univariate relationships of the clinical variables with nerve conduction abnormality were examined by Chi square analyses to determine the variables to be included in the model.;The model most highly predictive of nerve conduction abnormality included the variables Flick sign, nocturnal discomfort, and family history (P = 0.910). Two other specifications of abnormality, based on the numbers of abnormal variables, were used to test the robustness of the prediction model with similar results, but symptom duration was also included in the model. Tests such as Tinel's sign and Phalen's test which are often recommended in diagnosing CTS were less useful in this study in the prediction of abnormality than information obtained from the history. It was noted that prediction based upon the inclusion of a number of variables in a multivariate analysis, was better than prediction based upon any single variable. This study strongly suggests that the probability of nerve conduction abnormality can be predicted from clinical signs and symptoms and thus could be particularly useful to clinicians who lack easy access to nerve conduction testing.



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