Zhou Yanling, Zhang Zhipei, Wang ChengYu, Lan Xiaofeng, Li Weicheng, Zhang Muqin, Lao Guohui, Wu Kai, Chen Jun, Li Guixiang, Ning Yuping
Department of Psychiatry, Department of Neurology, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China.
Department of Psychiatry, Department of Neurology, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China; Southern Medical University, Guangzhou, China.
J Affect Disord. 2022 May 1;304:59-65. doi: 10.1016/j.jad.2022.02.029. Epub 2022 Feb 13.
Pretreatment characteristics of patients, symptom and function could be associated with antidepressant treatment outcome, but its predictive ability is not adequate. Our study aimed to identify predictors of acute antidepressant efficacy in patients with first-episode Major Depressive Disorder (MDD).
187 patients with first-episode MDD were included and assessed clinical symptoms, cognitive function and global functioning using the 17-item Hamilton Depression Inventory (HAMD-17), MATRICS Consensus Cognitive Battery (MCCB) and Global Assessment of Functioning (GAF). Participants received treatment with a SSRI (escitalopram or venlafaxine) for 4 weeks. Logistic regression was used to analyze the association between patients' characteristics, symptom profiles, cognitive performance, and global functioning and the antidepressant outcome at the end of 4 weeks, and ROC curve analysis was performed for predictive accuracy with area under the receiver operating curve (AUC).
Antidepressant improvement, response and remission rate at week 4 was 87.7%, 64.7% and 42.8%, respectively. The combination of pretreatment clinical profiles, speed of processing and global functioning showed moderate discrimination of acute improvement, response and remission with AUCs of 0.863, 0.812 and 0.734, respectively.
The major limitation of the present study is the study did not combine pharmacogenomics from the perspective of antidepressant drug metabolism.
Aside from the baseline clinical symptoms, cognitive function and global functioning could be predictors of acute treatment outcome in first episode MDD using escitalopram or venlafaxine. This relatively simple application based on clinical symptoms and function seems to be cost-effective method to identify individuals who are more likely to respond to antidepressant treatment.
患者的预处理特征、症状及功能可能与抗抑郁治疗结果相关，但其预测能力不足。我们的研究旨在确定首发重度抑郁症（MDD）患者急性抗抑郁疗效的预测因素。
纳入187例首发MDD患者，使用17项汉密尔顿抑郁量表（HAMD - 17）、MATRICS共识认知成套测验（MCCB）和功能总体评定量表（GAF）评估临床症状、认知功能和整体功能。参与者接受选择性5-羟色胺再摄取抑制剂（艾司西酞普兰或文拉法辛）治疗4周。采用逻辑回归分析患者特征、症状谱、认知表现和整体功能与4周结束时抗抑郁结果之间的关联，并通过受试者工作特征曲线下面积（AUC）进行ROC曲线分析以评估预测准确性。
第4周时抗抑郁改善、有效和缓解率分别为87.7%、64.7%和42.8%。预处理临床特征、加工速度和整体功能的组合对急性改善、有效和缓解具有中度区分能力，AUC分别为0.863、0.812和0.734。
本研究的主要局限性在于未从抗抑郁药物代谢角度结合药物基因组学。
除基线临床症状外，认知功能和整体功能可能是使用艾司西酞普兰或文拉法辛治疗首发MDD急性治疗结果的预测因素。这种基于临床症状和功能的相对简单应用似乎是一种经济有效的方法，可用于识别更可能对抗抑郁治疗有反应的个体。
