预测慢性背痛患者的基于互联网的抑郁症干预措施的异质治疗效应:两项随机对照试验的二次分析
Mathias Harrer1,2, David Daniel Ebert1, Paula Kuper1,3
1Psychology & Digital Mental Health Care, Technical University Munich, Munich, Germany.
Internet interventions
|August 28, 2023
概括
互联网干预有效地减少慢性背痛患者的抑郁症. 一个决策树模型确定,那些中度抑郁症和低疼痛自我效能的人从这种治疗中受益最多.
科学领域:
- 精神病学和行为科学 精神病学和行为科学
- 数字健康数字健康
- 疼痛管理 疼痛管理
背景情况:
- 抑郁症经常与慢性背痛同时发生.
- 基于互联网的干预措施显示出治疗抑郁症在这个人群中的希望.
- 确定哪些患者从数字干预中获益最多仍然不清楚.
研究的目的:
- 在慢性背痛患者的基于互联网的抑郁症干预中探索异质治疗效果的预测因素.
- 使用先进的统计方法开发个性化治疗效益的预测模型.
主要方法:
- 两项随机试验的分析 (N=504) 涉及慢性背痛和抑郁症患者.
- 探索单变的治疗与调节者的相互作用.
- 应用基于多层模型的递归分区来创建决策树模型.
主要成果:
- 该干预对抑郁症状 (PHQ-9) 的平均效果大小 (d) 为-0.43.
- 递归分区识别了一个决策树模型,解释了治疗效应中45%的差异.
- 预测的子组影响范围从d=0.24到-1.31不等,外部验证显示R2=33%.
结论:
- 基于互联网的干预对一些,但并非所有患有抑郁症的慢性背痛患者有效.
- 一个预测模型表明,中度抑郁症和低疼痛自效率的患者受益最多.
- 开发的决策树算法可能会指导个性化治疗的临床决策.
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