Zhu Yuncheng, Wang Fang, Wang Fan, Liu Hongmei, Guo Xiaoyun, Wang Zuowei, He Ruoqiao, Wu Xiaohui, Cao Lan, Wu Zhiguo, Peng Daihui, Fang Yiru
Division of Mood Disorders, Shanghai Hongkou Mental Health Center, Shanghai, 200083, China.
Clinical Research Center, Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
Heliyon. 2023 Oct 17;9(11):e20951. doi: 10.1016/j.heliyon.2023.e20951. eCollection 2023 Nov.
This research was designed to investigate Algorithm Guided Treatment (AGT) and clinical traits for the prediction of antidepressant treatment outcomes in Chinese patients with major depressive disorder (MDD).
This study included 581 patients who had reached treatment response and 406 patients remained non-responded observed after three months of treatment. Sociodemographic factors, clinical traits, and psychiatric rating scales for evaluating therapeutic responses between the two groups were compared. Logistic regression analysis was adopted to determine the risk factors of unresponsive to antidepressant (URA) in MDD. Kaplan-Meier survival analysis was utilized to compare the therapeutic response between AGT and treatment as usual (TAU).
Compared to the MDD responsive to antidepressant (RA) group, the URA group had significantly lower rates of the following clinical traits: married status, anxious distress, moderate to severe depressive symptoms, and higher rates of comorbidity ( < 0.05). Logistic Regression Analysis showed that eight clinical traits from psychiatric rating scales, such as anxious characteristics, were correlated positively with URA, while the other eight symptoms, such as autonomic symptoms, were negatively correlated. Time to symptomatic remission was longer in TAU without statistically significant ( = 0.11) by log-rank testing.
The factors may affect the therapeutic responses and compliance of patients, increasing the non-response risk for antidepressants. Therapeutic responses might be improved by increasing the clarification and elucidation of different symptom clusters of patients. Benefits on treatment response to AGT were not found in our study, indicating a one-size-fits-all approach may not work. We registered as a clinical trial at the International Clinical Trials Registry Platform (No. NCT01764867) and obtained ethical approval 2012-42 from SMHC.
本研究旨在探讨算法引导治疗（AGT）及临床特征对中国重度抑郁症（MDD）患者抗抑郁治疗结局的预测作用。
本研究纳入了581例达到治疗反应的患者以及406例治疗三个月后仍无反应的患者。比较了两组患者的社会人口学因素、临床特征以及评估治疗反应的精神科评定量表。采用逻辑回归分析确定MDD患者对抗抑郁药无反应（URA）的危险因素。利用Kaplan-Meier生存分析比较AGT与常规治疗（TAU）之间的治疗反应。
与抗抑郁药反应性（RA）的MDD组相比，URA组在以下临床特征方面的发生率显著较低：婚姻状况、焦虑痛苦、中度至重度抑郁症状，且合并症发生率较高（<0.05）。逻辑回归分析显示，精神科评定量表中的八个临床特征，如焦虑特征，与URA呈正相关，而其他八个症状，如自主神经症状，则呈负相关。通过对数秩检验，TAU组症状缓解时间较长，但无统计学意义（=0.11）。
这些因素可能影响患者的治疗反应和依从性，增加抗抑郁药无反应的风险。通过增加对患者不同症状群的明确和阐释，可能改善治疗反应。在我们的研究中未发现AGT对治疗反应有获益，表明一刀切的方法可能行不通。我们已在国际临床试验注册平台注册为一项临床试验（编号：NCT01764867），并于2012年获得上海交通大学医学院附属精神卫生中心伦理批准（批准号：2012 - 42）。
