Depression in Individuals With Traumatic and Nontraumatic Spinal Cord Injury Living in the Community.

OBJECTIVE: To assess depression in a sample of individuals with spinal cord injury (SCI) living in the community, and to determine the prevalence of probable major depressive disorder (PMDD) among those with traumatic spinal cord injury (T-SCI) and those with nontraumatic spinal cord injury (NT-SCI). DESIGN: Cross-sectional. SETTING: Data were collected on individuals with SCI now living in the community, who completed a comprehensive follow-up assessment at the hospital. PARTICIPANTS: Individuals with T-SCI or NT-SCI (N=831) completed the Patient Health Questionnaire-9 (PHQ-9) and were included. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: The PHQ-9 was used to detect the presence of PMDD and to measure the severity of the depression. RESULTS: The most frequent etiology of SCI was T-SCI (66.9%). Overall, 16.2% of participants met the criteria for PMDD; however, a higher prevalence was noted for individuals with NT-SCI (21.1%) than for individuals with T-SCI (13.8%). Risk factors between T-SCI and NT-SCI did not differ greatly. Female sex, chronic pain, and lower levels of/difficulties in participation were associated with the presence of PMDD. CONCLUSIONS: PMDD appears to occur at a higher rate in individuals with NT-SCI, with greater symptom severity. The finding that problems with participation are directly associated with depression raises the need for specific treatment goals, with the aim of empowering individuals with SCI to reintegrate into the community. Potential stress factors (eg, environmental barriers, limited participation options) should be addressed accordingly.

Knowledge discovery about quality of life changes of spinal cord injury patients: clustering based on rules by states.

In this paper, an integral Knowledge Discovery Methodology, named Clustering based on rules by States, which incorporates artificial intelligence (AI) and statistical methods as well as interpretation-oriented tools, is used for extracting knowledge patterns about the evolution over time of the Quality of Life (QoL) of patients with Spinal Cord Injury. The methodology incorporates the interaction with experts as a crucial element with the clustering methodology to guarantee usefulness of the results. Four typical patterns are discovered by taking into account prior expert knowledge. Several hypotheses are elaborated about the reasons for psychological distress or decreases in QoL of patients over time. The knowledge discovery from data (KDD) approach turns out, once again, to be a suitable formal framework for handling multidimensional complexity of the health domains.