ABSTRACT

Objective: Because individual patients with persistent somatic symptoms (PSS) respond differently to treatments, a better understanding of the factors that predict therapy outcomes are of high importance. Aggregating a wide selection of information into the treatment-decision process is a challenge for clinicians. Using the Personalized Advantage Index (PAI) this study aims to deal with this. Methods: Data from a multicentre RCT comparing CBT (N = 128) versus CBT enriched with emotion regulation training (ENCERT) (N = 126) for patients diagnosed with somatic symptom disorder were used to identify based on two machine learning approaches predictors of therapy outcomes. The identified predictors were used to calculate the PAI. Results: Five treatment unspecific predictors (pre-treatment somatic symptom severity, depression, symptom disability, health-related quality of life, age) and five treatment specific moderators (global functioning, early childhood traumatic events, gender, health anxiety, emotion regulation skills) were identified. Individuals assigned to their PAI-indicated optimal treatment had significantly lower somatic symptom severity at the end of therapy compared to those randomised to their non-optimal condition. Conclusion: Allowing patients to choose a personalised treatment seems to be meaningful. This could help to improve outcomes for PSS and reduce its high costs to the health care system.

摘要

目的：由于具有持续躯体症状 (PSS) 的个体患者对治疗的反应不同，因此更好地了解预测治疗结果的因素非常重要。将广泛选择的信息整合到治疗决策过程中是临床医生面临的挑战。本研究使用个性化优势指数 (PAI) 来解决这个问题。方法：来自多中心 RCT 的数据比较 CBT (N = 128) 与 CBT 丰富的情绪调节训练 (ENCERT) (N = 126) 对于诊断为躯体症状障碍的患者用于识别基于两种机器学习方法的治疗结果预测因子. 确定的预测因子用于计算 PAI。结果：五个治疗非特异性预测因子（治疗前躯体症状严重程度、抑郁、症状残疾、健康相关生活质量、年龄）和五个治疗特异性调节因子（整体功能、儿童早期创伤事件、性别、健康焦虑、情绪调节技能）是确定。与随机分配到非最佳状态的个体相比，分配到 PAI 指示的最佳治疗的个体在治疗结束时的躯体症状严重程度显着降低。结论：允许患者选择个性化治疗似乎是有意义的。这可能有助于改善 PSS 的结果并降低其对医疗保健系统的高成本。