Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Lenggstrasse 31, 8032, Zurich, Switzerland.
Vagelos School of Physicians and Surgeons, Department of Emergency Medicine, Columbia University Medical Centre, New York, NY, USA.
Sci Rep. 2022 Mar 31;12(1):5455. doi: 10.1038/s41598-022-09226-5.
A considerable number of depressed patients do not respond to treatment. Accurate prediction of non-response to routine clinical care may help in treatment planning and improve results. A longitudinal sample of N = 239 depressed patients was assessed at admission to multi-modal day clinic treatment, after six weeks, and at discharge. First, patient's treatment response was modelled by identifying longitudinal trajectories using the Hamilton Depression Rating Scale (HDRS-17). Then, individual items of the HDRS-17 at admission as well as individual patient characteristics were entered as predictors of response/non-response trajectories into the binary classification model (eXtremeGradient Boosting; XGBoost). The model was evaluated on a hold-out set and explained in human-interpretable form by SHapley Additive explanation (SHAP) values. The prediction model yielded a multi-class AUC = 0.80 in the hold-out set. The predictive power for the binary classification yielded an AUC = 0.83 (sensitivity = .80, specificity = .77). Most relevant predictors for non-response were insomnia symptoms, younger age, anxiety symptoms, depressed mood, being unemployed, suicidal ideation and somatic symptoms of depressive disorder. Non-responders to routine treatment for depression can be identified and screened for potential next-generation treatments. Such predictors may help personalize treatment and improve treatment response.
相当数量的抑郁症患者对治疗没有反应。准确预测对常规临床护理的无反应可能有助于治疗计划并改善结果。对接受多模式日间诊所治疗的 N=239 名抑郁症患者进行了纵向样本评估，在入院时、六周后和出院时进行评估。首先，通过使用汉密尔顿抑郁评定量表（HDRS-17）识别纵向轨迹来对患者的治疗反应进行建模。然后，将 HDRS-17 的入院时的各个项目以及各个患者特征作为反应/无反应轨迹的预测因子输入到二分类模型（极端梯度提升；XGBoost）中。该模型在保留集上进行了评估，并通过 SHapley Additive explanation (SHAP) 值以人类可解释的形式进行了解释。预测模型在保留集上的多类 AUC=0.80。用于二分类的预测能力产生 AUC=0.83（敏感性=.80，特异性=.77）。对常规治疗无反应的最相关预测因子是失眠症状、年龄较小、焦虑症状、抑郁情绪、失业、自杀意念和抑郁障碍的躯体症状。可以识别出对常规治疗抑郁症无反应的患者，并对其进行潜在下一代治疗的筛查。这些预测因子可能有助于个性化治疗并提高治疗反应。
