Department of Population Health Sciences, Weill Cornell Medicine, New York, USA.
Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
Psychol Med. 2024 Apr;54(6):1142-1151. doi: 10.1017/S0033291723002945. Epub 2023 Oct 11.
Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory.
One hundred and twenty-six persons aged 18-85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics.
Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model.
Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.
缓解期精神病性抑郁症（MDDPsy）的结局存在异质性。本研究旨在确定缓解期 MDDPsy 患者在继续治疗期间抑郁严重程度具有不同轨迹的亚组，并检测预测病情恶化轨迹的指标。
126 名年龄在 18-85 岁之间的患者参加了一项 36 周的随机安慰剂对照试验（RCT），该试验研究了在 MDDPsy 缓解后，继续使用奥氮平联合舍曲林治疗对抑郁严重程度的临床影响。采用潜在类别混合模型确定 RCT 期间具有不同抑郁严重程度轨迹的亚组。机器学习用于基于参与者的预轨迹特征预测轨迹成员身份。
71 名（56.3%）参与者属于抑郁评分稳定轨迹的亚组，55 名（43.7%）属于病情恶化轨迹的亚组。具有高预测精度（AUC 为 0.812）的随机森林模型发现，预测恶化亚组成员身份的最强指标是缓解开始时的残留抑郁症状，其次是 RCT 基线时的焦虑评分和首次终生抑郁发作的发病年龄。在一个将缓解开始时的抑郁评分作为唯一预测变量的逻辑回归模型中，AUC（0.778）接近机器学习模型。
缓解开始时的残留抑郁具有预测缓解期 MDDPsy 恶化结局的高度准确性。需要研究如何最好地优化残留症状的精神病性 MDDPsy 的结局。
