Mena Sergio, Coutts Fiona, von Trott Jana, Ucur Esin, Vetter Clara, Kahn René R, Fleischhacker W Wolfgang, Kane John M, Howes Oliver D, Upthegrove Rachel, Lalousis Paris A, Koutsouleris Nikolaos
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany.
Psychol Med. 2025 Jul 30;55:e221. doi: 10.1017/S0033291725100950.
Depressive symptoms are highly prevalent in first-episode psychosis (FEP) and worsen clinical outcomes. It is currently difficult to determine which patients will have persistent depressive symptoms based on a clinical assessment. We aimed to determine whether depressive symptoms and post-psychotic depressive episodes can be predicted from baseline clinical data, quality of life, and blood-based biomarkers, and to assess the geographical generalizability of these models.
Two FEP trials were analyzed: European First-Episode Schizophrenia Trial (EUFEST) ( = 498; 2002-2006) and Recovery After an Initial Schizophrenia Episode Early Treatment Program (RAISE-ETP) ( = 404; 2010-2012). Participants included those aged 15-40 years, meeting Diagnostic and Statistical Manual of Mental Disorders IV criteria for schizophrenia spectrum disorders. We developed support vector regressors and classifiers to predict changes in depressive symptoms at 6 and 12 months and depressive episodes within the first 6 months. These models were trained in one sample and externally validated in another for geographical generalizability.
A total of 320 EUFEST and 234 RAISE-ETP participants were included (mean [SD] age: 25.93 [5.60] years, 56.56% male; 23.90 [5.27] years, 73.50% male). Models predicted changes in depressive symptoms at 6 months with balanced accuracy (BAC) of 66.26% (RAISE-ETP) and 75.09% (EUFEST), and at 12 months with BAC of 67.88% (RAISE-ETP) and 77.61% (EUFEST). Depressive episodes were predicted with BAC of 66.67% (RAISE-ETP) and 69.01% (EUFEST), showing fair external predictive performance.
Predictive models using clinical data, quality of life, and biomarkers accurately forecast depressive events in FEP, demonstrating generalization across populations.
抑郁症状在首发精神病（FEP）中极为普遍，且会使临床结局恶化。目前，基于临床评估很难确定哪些患者会出现持续性抑郁症状。我们旨在确定能否根据基线临床数据、生活质量和血液生物标志物预测抑郁症状及精神病后抑郁发作，并评估这些模型在不同地区的通用性。
分析了两项FEP试验：欧洲首发精神分裂症试验（EUFEST）（n = 498；2002 - 2006年）和首发精神分裂症发作后早期治疗项目恢复试验（RAISE - ETP）（n = 404；2010 - 2012年）。参与者为年龄在15 - 40岁、符合《精神疾病诊断与统计手册》第四版精神分裂症谱系障碍标准的患者。我们开发了支持向量回归器和分类器，以预测6个月和12个月时抑郁症状的变化以及前6个月内的抑郁发作。这些模型在一个样本中进行训练，并在另一个样本中进行外部验证以评估地理通用性。
共纳入320名EUFEST参与者和234名RAISE - ETP参与者（平均[标准差]年龄：25.93[5.60]岁，男性占56.56%；23.90[5.27]岁，男性占73.50%）。模型预测6个月时抑郁症状变化的平衡准确率（BAC）在RAISE - ETP中为66.26%，在EUFEST中为75.09%；预测12个月时抑郁症状变化的BAC在RAISE - ETP中为67.88%，在EUFEST中为77.61%。预测抑郁发作的BAC在RAISE - ETP中为66.67%，在EUFEST中为69.01%，显示出较好的外部预测性能。
使用临床数据、生活质量和生物标志物的预测模型能够准确预测FEP中的抑郁事件，表明在不同人群中具有通用性。
