ENHANCING THE ACCURACY OF DEPRESSION DIAGNOSIS IN PATIENTS WITH SPINAL-CORD INJURY USING BAYESIAN-ANALYSIS

The accurate diagnosis of depression among patients with spinal cord injury (SCI) is critical to their rehabilitation. Overlapping symptoms of depression and SCI may obscure the diagnosis, however. It may be useful to evaluate the likelihood of the diagnosis of depression given the presence or absence of symptoms. This study compared depressive symptoms of patients with paraplegia (N=80) and patients with quadriplegia (N=53) using Bayesian analysis. Predictive powers and efficiency of symptoms were examined. Differences in the efficiency of predictability for individuals with paraplegia and quadriplegia existed for dysphoric mood, energy, and suicidal ideation. The results suggest that differential weighing of symptoms of depression may reduce misdiagnosis, thereby enhancing rehabilitation efforts. Bayesian analysis shows promise as an alternative approach to evaluating and diagnosing depression in patients undergoing rehabilitation.