Clinical predictors of treatment resistant depression

作者：Alessandro Serretti, Siegfried Kasper, Lucie Bartova, Joseph Zohar, Daniel Souery, Stuart Montgomery, Panagiotis Ferentinos, Dan Rujescu, Alexander Kautzky, Francesco Attanasio, Raffaella Zanardi, Chiara Fabbri, Bernhard T. Baune, Raffaele Ferri, Julien Mendlewicz · 发表于：European Neuropsychopharmacology · 年份：2025 · DOI：10.1016/j.euroneuro.2025.06.011 · 被引用次数：14 · 研究领域：Treatment of Major Depression、Tryptophan and brain disorders、Mental Health Research Topics

Despite advances in the treatment of major depressive disorder (MDD) yet a substantial proportion of patients fail to achieve remission and instead develop treatment-resistant depression (TRD). Identifying robust clinical predictors of response is essential for early, personalized interventions. We analyzed a large, multicenter sample (N = 2953) from the Group for the Study of Resistant Depression (GSRD) project, which included previously studied cohorts (TRD I-III) and a newly recruited cohort (TRD IV, N = 294). Patients were categorized as responders, non-responders, or TRD. Sociodemographic and clinical variables, including current and retrospective MADRS items, were used to train an XGBoost classifier. Primary outcomes were the multi-class metrics area under the curve (AUC), accuracy, and F1-scores. Previously reported predictors were mainly confirmed in the new TRD IV sample. The XGBoost model showed a mean ROC AUC of 0.80 and an accuracy of 61 %, significantly above chance. Misclassification was more frequent among responders versus non-responders, while TRD was predicted most accurately (precision=0.73; recall=0.73). Measures of illness chronicity, such as duration of current episode, duration of disease lifetime, number of hospitalizations, and number of depressive episodes, as well as severity features, BMI and level of functioning were among the most important predictors. Secondary analyses using earlier cohorts to train and the new TRD IV sample to test confirmed s...