Craig A, Rodrigues D, Tran Y, Guest R, Bartrop R, Middleton J
Rehabilitation Studies Unit, Sydney Medical School-Northern, The University of Sydney, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia.
1] Rehabilitation Studies Unit, Sydney Medical School-Northern, The University of Sydney, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia [2] Key University Centre for Health Technologies, University of Technology, Sydney, New South Wales, Australia.
Spinal Cord. 2014 May;52(5):413-6. doi: 10.1038/sc.2014.25. Epub 2014 Mar 11.
Cross-section design.
The development of reliable screen technology for predicting those at risk of depression in the long-term remains a challenge. The objective of this research was to determine factors that classify correctly adults with spinal cord injury (SCI) with depressed mood and to develop a diagnostic algorithm that could be applied for prediction of depressed mood in the long-term.
SCI rehabilitation unit, rehabilitation outpatient clinic and Australian community.
Participants included 107 adults with SCI. The assessment regimen included demographic and injury variables, negative mood states, pain intensity, health-related quality of life and self-efficacy. Participants were divided into those with 'normal' mood versus those with elevated depressed mood. Discriminant function analysis (DFA) was then used to isolate factors that in combination, best classify the presence or absence of depressed mood.
At the time of assessment, 24 participants (22.4%) had elevated depressed mood. DFA identified six factors that discriminated between those with depressed mood (P<0.01) and those with normal mood, explaining 61% of the variance. Factors consisted of pain intensity, mental health, emotional and social functioning, self-efficacy and fatigue. DFA correctly classified 91.7% (n=22 of 24) of those with depressed mood and 95.2% (n=79 of 83) of those without. Demographic, injury and physical health function variables were not found to discriminate depressed mood.
Clinical implications of applying a diagnostic algorithm for detecting depression in adults with SCI are discussed. Prospective research is needed to test the predictive efficacy of the algorithm.
横断面设计。
开发可靠的筛查技术以长期预测有抑郁风险的人群仍然是一项挑战。本研究的目的是确定能够正确分类脊髓损伤（SCI）且情绪低落的成年人的因素，并开发一种可用于长期预测情绪低落的诊断算法。
SCI康复单元、康复门诊及澳大利亚社区。
参与者包括107名成年SCI患者。评估方案包括人口统计学和损伤变量、负面情绪状态、疼痛强度、健康相关生活质量和自我效能感。参与者被分为“正常”情绪组和情绪低落组。然后使用判别函数分析（DFA）来分离出能最佳组合分类情绪低落存在与否的因素。
在评估时，24名参与者（22.4%）情绪低落。DFA确定了六个区分情绪低落组（P<0.01）和正常情绪组的因素，解释了61%的方差。这些因素包括疼痛强度、心理健康、情感和社会功能、自我效能感和疲劳。DFA正确分类了91.7%（24人中的22人）情绪低落的参与者和95.2%（83人中的79人）无情绪低落的参与者。未发现人口统计学、损伤和身体健康功能变量能区分情绪低落。
讨论了应用诊断算法检测成年SCI患者抑郁的临床意义。需要进行前瞻性研究以测试该算法的预测效能。
