Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches.
Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches.
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使用个体症状数据预测抑郁症的成年人的预后：建模方法的比较。
DOI：
10.1017/s0033291721001616
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发表时间：
2023-01
影响因子：
6.9
通讯作者：
Pilling, S.
中科院分区：
文献类型：
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作者：
Buckman, J. E. J.;Cohen, Z. D.;O'Driscoll, C.;Fried, E., I;Saunders, R.;Ambler, G.;DeRubeis, R. J.;Gilbody, S.;Hollon, S. D.;Kendrick, T.;Watkins, E.;Eley, T. C.;Peel, A. J.;Rayner, C.;Kessler, D.;Wiles, N.;Lewis, G.;Pilling, S.
本研究旨在开发，验证和比较基于治疗前数据预测抑郁症成人治疗后结局的模型的性能。所有6项合格的随机对照试验的个体患者数据用于开发（k = 3，n = 1722）和测试（k = 3，n = 918）9个模型。预测因素包括抑郁和焦虑症状、社会支持、生活事件和饮酒。加权总分是使用来自网络中心性统计（模型1-3）的系数权重和来自验证性因子分析（模型4）的因子负荷来开发的。使用弹性网络正则化（ENR）和普通最小二乘（OLS）回归（模型5和6）测试未加权总和评分模型。然后将个别项目列入环境和自然资源调查和最后生活调查（模式7和8）。将所有模型相互比较，并与空模型（训练数据中的基线后贝克抑郁量表第二版（BDI-II）平均评分：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都优于空模型和模型8。模型1-6之间的模型性能非常相似，这意味着应用于基线总和评分的差异权重几乎没有影响。任何建模技术（模型1-7）可用于告知抑郁症成人的预后预测，根据治疗后抑郁症状的预测严重程度，达到缓解的患者比例存在差异。然而，预后的大部分差异仍然无法解释。这可能是必要的，包括更广泛的生物心理社会变量，以更好地判断之间的竞争模型，并获得更大的临床实用性为寻求治疗的成年抑郁症的模型。
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 (k = 3, n = 1722) and test (k = 3, n = 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.