Zhukovsky Peter, Trivedi Madhukar H, Weissman Myrna, Parsey Ramin, Kennedy Sidney, Pizzagalli Diego A
Center for Depression, Anxiety and Stress Research, Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, Massachusetts.
Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas.
JAMA Netw Open. 2025 Mar 3;8(3):e251310. doi: 10.1001/jamanetworkopen.2025.1310.
Although several predictive models for response to antidepressant treatment have emerged on the basis of individual clinical trials, it is unclear whether such models generalize to different clinical and geographical contexts.
To assess whether neuroimaging and clinical features predict response to sertraline and escitalopram in patients with major depressive disorder (MDD) across 2 multisite studies using machine learning and to predict change in depression severity in 2 independent studies.
DESIGN, SETTING, AND PARTICIPANTS: This prognostic study included structural and functional resting-state magnetic resonance imaging and clinical and demographic data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) randomized clinical trial (RCT), which administered sertraline (in stage 1 and stage 2) and placebo, and the Canadian Biomarker Integration Network in Depression (CANBIND-1) RCT, which administered escitalopram. EMBARC recruited participants with MDD (aged 18-65 years) at 4 academic sites across the US between August 2011 and December 2015. CANBIND-1 recruited participants with MDD from 6 outpatient centers across Canada between August 2013 and December 2016. Data were analyzed from October 2023 to May 2024.
Prediction performance for treatment response was assessed using balanced classification accuracy and area under the curve (AUC). In secondary analyses, prediction performance was assessed using observed vs predicted correlations between change in depression severity.
In 363 adult patients (225 from EMBARC and 138 from CANBIND-1; mean [SD] age, 36.6 [13.1] years; 235 women [64.7%]), the best-performing models using pretreatment clinical features and functional connectivity of the dorsal anterior cingulate had moderate cross-trial generalizability for antidepressant treatment (trained on CANBIND-1 and tested on EMBARC, AUC = 0.62 for stage 1 and AUC = 0.67 for stage 2; trained on EMBARC stage 1 and tested on CANBIND-1, AUC = 0.66). The addition of neuroimaging features improved the prediction performance of antidepressant response compared with clinical features only. The use of early-treatment (week 2) instead of pretreatment depression severity scores resulted in the best generalization performance, comparable to within-trial performance. Multivariate regressions showed substantial cross-trial generalizability in change in depression severity (predicted vs observed r ranging from 0.31 to 0.39).
In this prognostic study of depression outcomes, models predicting response to antidepressants show substantial generalizability across different RCTs of adult MDD.
尽管基于个别临床试验已出现了几种抗抑郁治疗反应的预测模型，但尚不清楚这些模型是否能推广到不同的临床和地理环境。
通过机器学习评估神经影像学和临床特征是否能预测两项多中心研究中重度抑郁症（MDD）患者对舍曲林和艾司西酞普兰的反应，并在两项独立研究中预测抑郁严重程度的变化。
设计、设置和参与者：这项预后研究纳入了来自临床护理中抗抑郁反应的调节因素和生物标志物确立（EMBARC）随机临床试验（RCT）的静息态结构和功能磁共振成像以及临床和人口统计学数据，该试验给予舍曲林（在第1阶段和第2阶段）和安慰剂，以及加拿大抑郁症生物标志物整合网络（CANBIND - 1）RCT，该试验给予艾司西酞普兰。EMBARC在2011年8月至2015年12月期间在美国4个学术地点招募了MDD患者（年龄18 - 65岁）。CANBIND - 1在2013年8月至2016年12月期间从加拿大6个门诊中心招募了MDD患者。数据于2023年10月至2024年5月进行分析。
使用平衡分类准确率和曲线下面积（AUC）评估治疗反应的预测性能。在二次分析中，使用抑郁严重程度变化之间的观察值与预测值相关性评估预测性能。
在363名成年患者中（225名来自EMBARC，138名来自CANBIND - 1；平均[标准差]年龄为36.6[13.1]岁；235名女性[64.7%]），使用治疗前临床特征和背侧前扣带回功能连接性的最佳表现模型对抗抑郁治疗具有中等程度的跨试验可推广性（在CANBIND - 1上训练并在EMBARC上测试，第1阶段AUC = 0.62，第2阶段AUC = 0.67；在EMBARC第1阶段训练并在CANBIND - 1上测试，AUC = 0.66）。与仅使用临床特征相比，添加神经影像学特征改善了抗抑郁反应的预测性能。使用早期治疗（第2周）而非治疗前抑郁严重程度评分产生了最佳的推广性能，与试验内性能相当。多变量回归显示抑郁严重程度变化具有显著的跨试验可推广性（预测值与观察值的r范围为0.31至0.39）。
在这项抑郁症预后研究中，预测抗抑郁药反应的模型在不同的成年MDD随机对照试验中显示出显著的可推广性。
