PDF(Pubmed)
Abstract：
Depression is a highly common and recurrent condition. Predicting who is at most risk of relapse or recurrence can inform clinical practice. Applying machine-learning methods to Individual Participant Data (IPD) can be promising to improve the accuracy of risk predictions.
Individual data of four Randomized Controlled Trials (RCTs) evaluating antidepressant treatment compared to psychological interventions with tapering ([Formula: see text]) were used to identify predictors of relapse and/or recurrence. Ten baseline predictors were assessed. Decision trees with and without gradient boosting were applied. To study the robustness of decision-tree classifications, we also performed a complementary logistic regression analysis.
The combination of age, age of onset of depression, and depression severity significantly enhances the prediction of relapse risk when compared to classifiers solely based on depression severity. The studied decision trees can (i) identify relapse patients at intake with an accuracy, specificity, and sensitivity of about 55% (without gradient boosting) and 58% (with gradient boosting), and (ii) slightly outperform classifiers that are based on logistic regression.
Decision tree classifiers based on multiple-rather than single-risk indicators may be useful for developing treatment stratification strategies. These classification models have the potential to contribute to the development of methods aimed at effectively prioritizing treatment for those individuals who require it the most. Our results also underline the existing gaps in understanding how to accurately predict depressive relapse.
Individual data of four Randomized Controlled Trials (RCTs) evaluating antidepressant treatment compared to psychological interventions with tapering ([Formula: see text]) were used to identify predictors of relapse and/or recurrence. Ten baseline predictors were assessed. Decision trees with and without gradient boosting were applied. To study the robustness of decision-tree classifications, we also performed a complementary logistic regression analysis.
The combination of age, age of onset of depression, and depression severity significantly enhances the prediction of relapse risk when compared to classifiers solely based on depression severity. The studied decision trees can (i) identify relapse patients at intake with an accuracy, specificity, and sensitivity of about 55% (without gradient boosting) and 58% (with gradient boosting), and (ii) slightly outperform classifiers that are based on logistic regression.
Decision tree classifiers based on multiple-rather than single-risk indicators may be useful for developing treatment stratification strategies. These classification models have the potential to contribute to the development of methods aimed at effectively prioritizing treatment for those individuals who require it the most. Our results also underline the existing gaps in understanding how to accurately predict depressive relapse.
摘要：
背景：抑郁症是一种非常常见和反复发作的疾病。预测谁是复发或复发的最大风险可以为临床实践提供信息。将机器学习方法应用于个人参与者数据（IPD）可以有望提高风险预测的准确性。
方法：使用四个随机对照试验（RCT）的个体数据来评估抗抑郁药治疗与逐渐减少的心理干预（[公式：见正文]），以确定复发和/或复发的预测因子。评估了十个基线预测因子。应用具有和不具有梯度提升的决策树。为了研究决策树分类的鲁棒性，我们还进行了补充逻辑回归分析.
结果：年龄的组合，抑郁症的发病年龄，与仅基于抑郁严重程度的分类器相比，抑郁严重程度显着增强了对复发风险的预测。研究的决策树可以(I)准确地识别摄入的复发患者，特异性，灵敏度约为55%(无梯度增强)和58%(有梯度增强)，和(Ii)略微优于基于逻辑回归的分类器。
结论：基于多个而非单风险指标的决策树分类器可能有助于制定治疗分层策略。这些分类模型有可能有助于开发旨在有效地优先考虑最需要治疗的个人的方法。我们的结果还强调了在理解如何准确预测抑郁症复发方面存在的差距。
方法：使用四个随机对照试验（RCT）的个体数据来评估抗抑郁药治疗与逐渐减少的心理干预（[公式：见正文]），以确定复发和/或复发的预测因子。评估了十个基线预测因子。应用具有和不具有梯度提升的决策树。为了研究决策树分类的鲁棒性，我们还进行了补充逻辑回归分析.
结果：年龄的组合，抑郁症的发病年龄，与仅基于抑郁严重程度的分类器相比，抑郁严重程度显着增强了对复发风险的预测。研究的决策树可以(I)准确地识别摄入的复发患者，特异性，灵敏度约为55%(无梯度增强)和58%(有梯度增强)，和(Ii)略微优于基于逻辑回归的分类器。
结论：基于多个而非单风险指标的决策树分类器可能有助于制定治疗分层策略。这些分类模型有可能有助于开发旨在有效地优先考虑最需要治疗的个人的方法。我们的结果还强调了在理解如何准确预测抑郁症复发方面存在的差距。
