Department of Psychiatry, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea.
J Affect Disord. 2012 Mar;137(1-3):61-9. doi: 10.1016/j.jad.2011.12.026. Epub 2012 Jan 12.
Although many demographic and clinical characteristics have been suggested to predict treatment outcome of depression, they provide only a weak prediction for clinical response. Based on the predictive values of trauma and biological markers involved in stress response, we investigated the roles of baseline trait anxiety and resilience, which were assumed as vulnerability and resilience factors, respectively, in predicting treatment response in naturalistically treated outpatients with depressive disorders.
A total of 178 outpatients with depressive disorders were consecutively recruited and completed measures of trauma experiences, psychological symptoms, and resilience at baseline. Response was defined by Clinical Global Impression (CGI)-Improvement score ≤2 at last visit during a 6month-treatment period. Univariate analyses and multiple logistic regression analysis were performed to determine predictors of treatment response.
Among demographic and clinical variables, treatment response was associated with increased age, longer treatment duration, higher resilience, and lower trait anxiety. In logistic regression analysis, resilience, trait anxiety, and their interaction significantly predicted treatment response after adjusting for age and treatment duration. Interaction between resilience and trait anxiety remained significant in the final model. Examining the interaction between the two, patients with low trait anxiety were only significantly affected by the level of resilience in response rate.
Low trait anxiety, high resilience, and their interaction might contribute to better treatment response in depressed patients. Our result suggested that individual differences in responding to stress might be important in predicting treatment outcome of depression in addition to other demographic and clinical factors.
尽管有许多人口统计学和临床特征被认为可以预测抑郁症的治疗效果，但它们对临床反应的预测能力较弱。基于涉及应激反应的创伤和生物标志物的预测值，我们研究了基线特质焦虑和韧性的作用，分别假设为易感性和韧性因素，以预测自然治疗的抑郁障碍门诊患者的治疗反应。
共连续招募了 178 名抑郁障碍门诊患者，并在基线时完成了创伤经历、心理症状和韧性的测量。反应定义为在 6 个月治疗期间的最后一次就诊时临床总体印象（CGI）-改善评分≤2。进行单变量分析和多逻辑回归分析，以确定治疗反应的预测因素。
在人口统计学和临床变量中，治疗反应与年龄增加、治疗时间延长、韧性增加和特质焦虑降低有关。在逻辑回归分析中，调整年龄和治疗时间后，韧性、特质焦虑及其交互作用显著预测了治疗反应。在最终模型中，韧性和特质焦虑及其交互作用仍然显著。在考察两者的交互作用时，低特质焦虑的患者仅在反应率方面受到韧性水平的显著影响。
低特质焦虑、高韧性及其交互作用可能有助于抑郁患者更好地治疗反应。我们的结果表明，除了其他人口统计学和临床因素外，个体对压力的反应差异可能对预测抑郁症的治疗效果很重要。
