Psychotherapies are efficacious in the treatment of depression, albeit only with a moderate effect size. It is hoped that personalization of treatment can lead to better outcomes. The network theory of psychopathology offers a novel approach suggesting that symptom interactions as displayed in person-specific symptom networks could guide treatment planning for an individual patient. In a sample of 254 patients with chronic depression treated with either disorder-specific or non-specific psychotherapy for 48 weeks, we investigated if person-specific symptom networks predicted observer-rated depression severity at the end of treatment and one and two years after treatment termination. Person-specific symptom networks were constructed based on a time-varying multilevel vector autoregressive model of patient-rated symptom data. We used statistical parameters that describe the structure of these person-specific networks to predict therapy outcome. First, we used symptom centrality measures as predictors. Second, we used a machine learning approach to select parameters that describe the strength of pairwise symptom associations. We found that information on person-specific symptom networks strongly improved the accuracy of the prediction of observer-rated depression severity at treatment termination compared to common covariates recorded at baseline. This was also shown for predicting observer-rated depression severity at one- and two-year follow-up. Pairwise symptom associations were better predictors than symptom centrality parameters for depression severity at the end of therapy and one year later. Replication and external validation of our findings, methodological developments, and work on possible ways of implementation are needed before person-specific networks can be reliably used in clinical practice. Nevertheless, our results indicate that the structure of person-specific symptom networks can provide valuable information for the personalization of treatment for chronic depression.

中文翻译：

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通过个人特定症状网络预测慢性抑郁症心理治疗的结果


心理疗法在治疗抑郁症方面是有效的，尽管效果适中。希望个性化治疗能够带来更好的结果。精神病理学的网络理论提供了一种新颖的方法，表明个体特定症状网络中显示的症状相互作用可以指导个体患者的治疗计划。在 254 名慢性抑郁症患者接受为期 48 周的特定疾病或非特定心理治疗的样本中，我们调查了个体特定症状网络是否可以预测治疗结束时以及治疗后一年和两年时观察者评定的抑郁症严重程度终止。基于患者评价症状数据的时变多级向量自回归模型构建了特定于人的症状网络。我们使用描述这些特定于人的网络结构的统计参数来预测治疗结果。首先，我们使用症状中心性度量作为预测因子。其次，我们使用机器学习方法来选择描述成对症状关联强度的参数。我们发现，与基线记录的常见协变量相比，有关个人特定症状网络的信息极大地提高了治疗终止时观察者评定的抑郁严重程度的预测准确性。这也被证明可以预测一年和两年随访时观察者评定的抑郁症严重程度。对于治疗结束时和一年后的抑郁严重程度，成对症状关联比症状中心参数是更好的预测因子。 在将特定于个人的网络可靠地用于临床实践之前，需要对我们的研究结果、方法学发展以及可能的实施方式进行复制和外部验证。尽管如此，我们的结果表明，个体特定症状网络的结构可以为慢性抑郁症的个性化治疗提供有价值的信息。