预测数字压力干预对患有抑郁症状的患者的影响:使用个人参与者数据开发和验证元分析预后模型
Mathias Harrer1, Harald Baumeister2, Pim Cuijpers3
1Department of Health Sciences, Psychology and Digital Mental Health Care, Technical University of Munich.
Journal of consulting and clinical psychology
|December 21, 2023
概括
数字压力干预措施对抑郁症有希望,但个性化是关键. 新的模型预测哪些患者受益最多,识别那些需要更密集的治疗以获得更好的结果.
科学领域:
- 数字心理健康干预数字心理健康干预
- 抑郁症治疗疗效 抑郁症治疗疗效
- 个性化医疗在精神病学中的个人化医疗.
背景情况:
- 数字压力干预提供了抑郁症的间接治疗途径.
- 确定谁将从这些干预中获益最多的个人仍然是一个挑战.
研究的目的:
- 开发针对数字压力干预对抑郁症状的个性化益处的预测模型.
- 评估这些模型在6个月后期预测治疗反应的有效性.
主要方法:
- 利用了6项随机试验中1,525名患有抑郁症状的患者的数据,将数字压力干预与等候名单对照进行了比较.
- 开发了使用多层次最小绝对收缩和选择运算机和增强算法的预测模型.
- 通过引导偏差校正和内部-外部交叉验证验证验证模型,估计基于治疗分配切割点的预期效应.
主要成果:
- 在乐观度调整后,模型的初始性能 (R2 = 21.0%-23.4%) 略有下降 (R2 = 17.0%-19.6%).
- 包括中期抑郁症状评估显著改善了预测 (乐观度调整后的R2=32.6%-35.6%).
- 使用最小重要的差异切割点,84.6%-93.3%的患者表现出益处,而较小的子组 (39.3%-46.2%) 显示出相当大的预期益处.
结论:
- 超分析性预后模型可以预测抑郁症数字压力干预措施的差异性益处.
- 开发的模型可以作为查工具来识别可能需要更密集的抑郁治疗的患者.
- 大多数患者似乎从数字压力干预中受益,但个性化预测有助于优化治疗选择.
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