Research Department of Clinical, Educational & Health Psychology, Centre for Outcomes Research and Effectiveness (CORE), University College London, 1-19 Torrington Place, London, UK.
iCope - Camden & Islington Psychological Therapies Services - Camden & Islington NHS Foundation Trust, St Pancras Hospital, London, UK.
Psychol Med. 2023 Jan;53(2):408-418. doi: 10.1017/S0033291721001616. Epub 2021 May 6.
This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data.
Individual patient data from all six eligible randomised controlled trials were used to develop ( = 3, = 1722) and test ( = 3, = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months.
Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact.
Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.
本研究旨在基于治疗前数据开发、验证和比较用于预测抑郁成年患者治疗后结局的模型，并比较这些模型的性能。
使用来自所有六项合格的随机对照试验的个体患者数据来开发（=3，=1722）和测试（=3，=918）九个模型。预测因子包括抑郁和焦虑症状、社会支持、生活事件和酒精使用。加权总分是使用来自网络中心性统计的系数权重（模型 1-3）和验证性因素分析的因子负荷（模型 4）开发的。未加权总分模型使用弹性网络正则（ENR）和普通最小二乘法（OLS）回归（模型 5 和 6）进行测试。然后将各个项目纳入 ENR 和 OLS（模型 7 和 8）。将所有模型相互比较，并与零模型（训练数据中基线后贝克抑郁量表第二版（BDI-II）的平均得分：模型 9）进行比较。主要结局：3-4 个月时的 BDI-II 评分。
模型 1-7 均优于零模型和模型 8。模型 1-6 之间的模型性能非常相似，这意味着对基线总分应用不同的权重对预后没有显著影响。
任何建模技术（模型 1-7）都可以用于为抑郁成年患者提供预后预测，根据治疗后抑郁症状的严重程度，不同的模型可以预测患者达到缓解的比例。然而，预后的大部分差异仍无法解释。可能需要纳入更广泛的生物心理社会变量，以更好地甄别竞争模型，并为寻求治疗的抑郁成年患者开发更具临床实用性的模型。
