An individualized treatment rule to optimize probability of remission by continuation, switching, or combining antidepressant medications after failing a first-line antidepressant in a two-stage randomized trial
An individualized treatment rule to optimize probability of remission by continuation, switching, or combining antidepressant medications after failing a first-line antidepressant in a two-stage randomized trial
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DOI：
10.1017/s0033291721000027
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发表时间：
2021-03
影响因子：
6.9
通讯作者：
R. Kessler;T. Furukawa;Tadashi Kato;Alexander Luedtke;M. Petukhova;E. Sadikova;N. Sampson
中科院分区：
文献类型：
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作者：
R. Kessler;T. Furukawa;Tadashi Kato;Alexander Luedtke;M. Petukhova;E. Sadikova;N. Sampson
摘要背景越来越多的人对使用复合个体化治疗规则（ITR）来指导抑郁症治疗选择感兴趣，但这样做的最佳方法并不广为人知。我们基于最近发表的二线抗抑郁药物选择试验的二次分析，使用尖端的集成机器学习方法开发了抑郁缓解的ITR。方法数据来自SUN（^_^）D试验，这是一项开放标签、评估者盲法的实用性试验，研究对象为来自日本48家诊所的既往未经治疗的重度抑郁症患者。最初的临床水平随机分配患者50或100 mg/天舍曲林。我们关注1549例在3周内未能缓解的患者，然后在个体水平上重新随机分配至继续舍曲林、改用米氮平或米氮平与舍曲林联合治疗。结局为基线后9周缓解。预测因素包括社会人口统计学、临床特征、基线症状、基线和第3周之间的症状变化以及第3周的副作用。结果与随机化（30.1-30.8%）相比，优化治疗与两个样本中交叉验证的第9周缓解率显著增加相关[5.3%（2.4%），p = 0.016 50 mg/天样本; 5.1%（2.7%），p = 0.031 100 mg/天样本]。与继续治疗相比，优化治疗还使两份样本的缓解率显著增加[两份样本均为24.7%：11.2%（3.8%），p = 0.002 50 mg/天样本; 11.7%（3.9%），p = 0.001 100 mg/天样本]。非显着的收益相比，切换或合并优化。结论：ITR可用于改善二线抗抑郁药的选择，但在更大规模的研究中使用更全面的基线预测因子进行复制可能会产生更强和更稳定的结果。
Abstract Background There is growing interest in using composite individualized treatment rules (ITRs) to guide depression treatment selection, but best approaches for doing this are not widely known. We develop an ITR for depression remission based on secondary analysis of a recently published trial for second-line antidepression medication selection using a cutting-edge ensemble machine learning method. Methods Data come from the SUN(^_^)D trial, an open-label, assessor blinded pragmatic trial of previously-untreated patients with major depressive disorder from 48 clinics in Japan. Initial clinic-level randomization assigned patients to 50 or 100 mg/day sertraline. We focus on the 1549 patients who failed to remit within 3 weeks and were then rerandomized at the individual-level to continuation with sertraline, switching to mirtazapine, or combining mirtazapine with sertraline. The outcome was remission 9 weeks post-baseline. Predictors included socio-demographics, clinical characteristics, baseline symptoms, changes in symptoms between baseline and week 3, and week 3 side effects. Results Optimized treatment was associated with significantly increased cross-validated week 9 remission rates in both samples [5.3% (2.4%), p = 0.016 50 mg/day sample; 5.1% (2.7%), p = 0.031 100 mg/day sample] compared to randomization (30.1–30.8%). Optimization was also associated with significantly increased remission in both samples compared to continuation [24.7% in both: 11.2% (3.8%), p = 0.002 50 mg/day sample; 11.7% (3.9%), p = 0.001 100 mg/day sample]. Non-significant gains were found for optimization compared to switching or combining. Conclusions An ITR can be developed to improve second-line antidepressant selection, but replication in a larger study with more comprehensive baseline predictors might produce stronger and more stable results.