Division of Mood Disorders & Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
Department of Psychiatry and Psychology, Shanghai Deji Hospital, affiliated to Qingdao University, Shanghai 200331, China.
J Affect Disord. 2022 Jan 15;297:68-75. doi: 10.1016/j.jad.2021.10.011. Epub 2021 Oct 17.
In spite of numerous options, the most efficacious treatment for major depressive disorder (MDD) remains elusive. Algorithm-guided treatments (AGTs) are proposed to address inadequate remission and optimize treatment delivery. This study aimed to evaluate the clinical benefit of AGTs for MDD, and to explore specific moderators of treatment outcomes for individual patients.
The study recruited 987 patients with MDD across eight hospitals who were randomly assigned to AGT with escitalopram (AGT-E), AGT with mirtazapine (AGT-M), or treatment-as-usual (TAU). The outcomes were symptom remission, response rate, early improvement rate, subsymptom clusters improvement over time, the mean time to first remission, relapse rate at 6-months posttreatment follow-up, quality of life (QOL), and adverse events.
No significant differences were observed across groups in outcome, except that TAU showed significantly poorer QOL, higher relapse rates at 6-months posttreatment follow-up, and marginally significantly worse maximal burden of adverse events than the AGT groups. After 6 weeks of treatment initiation, remission rate did not significantly increase with extended treatment. AGT-M outperformed the TAU and AGT-E in treating sleep symptoms. AGT-E was less effective than AGT-M and TAU in patients with severe depression and somatic symptoms (DSSS). The superiority of TAU over AGTs was observed in recurrent MDD patients.
Although the superiority of AGTs over TAU was limited by failure of alternative subsequent treatment, AGTs outperformed in QOL and relapse rate. Types of disease episode and DSSS were regarded as specific moderators in treatment of depression. These findings might contribute to future research on targeted antidepressant treatment.
尽管有多种选择，但对于重度抑郁症（MDD），最有效的治疗方法仍然难以捉摸。算法指导治疗（AGT）被提出以解决缓解不足和优化治疗方案。本研究旨在评估 AGT 治疗 MDD 的临床疗效，并探索患者个体治疗结果的具体调节因素。
本研究在 8 家医院招募了 987 名 MDD 患者，他们被随机分配到艾司西酞普兰的 AGT（AGT-E）、米氮平的 AGT（AGT-M）或常规治疗（TAU）。结果是症状缓解、反应率、早期改善率、亚症状群随时间改善、首次缓解的平均时间、治疗后 6 个月的复发率、生活质量（QOL）和不良反应。
各组之间的结果没有显著差异，除了 TAU 的 QOL 明显较差，治疗后 6 个月的复发率较高，以及不良反应的最大负担略差于 AGT 组。在治疗开始后的 6 周内，延长治疗并不能显著提高缓解率。AGT-M 在治疗睡眠症状方面优于 TAU 和 AGT-E。在重度抑郁症和躯体症状（DSSS）患者中，AGT-E 的疗效不如 AGT-M 和 TAU。TAU 在复发性 MDD 患者中的疗效优于 AGTs。
尽管 AGT 治疗优于 TAU 的优势受到替代后续治疗失败的限制，但 AGT 在 QOL 和复发率方面表现更好。疾病发作类型和 DSSS 被认为是抑郁症治疗的特定调节因素。这些发现可能有助于未来针对抗抑郁治疗的研究。
