While being highly effective on average, exposure-based treatments are not equally effective in all patients. The a priori identification of patients with a poor prognosis may enable the application of more personalized psychotherapeutic interventions. We aimed at identifying sociodemographic and clinical pre-treatment predictors for treatment response in spider phobia (SP).

N = 174 patients with SP underwent a highly standardized virtual reality exposure therapy (VRET) at two independent sites. Analyses on group-level were used to test the efficacy. We applied a state-of-the-art machine learning protocol (Random Forests) to evaluate the predictive utility of clinical and sociodemographic predictors for a priori identification of individual treatment response assessed directly after treatment and at 6-month follow-up. The reliability and generalizability of predictive models was tested via external cross-validation.

Our study shows that one session of VRET is highly effective on a group-level and is among the first to reveal long-term stability of this treatment effect. Individual short-term symptom reductions could be predicted above chance, but accuracies dropped to non-significance in our between-site prediction and for predictions of long-term outcomes. With performance metrics hardly exceeding chance level and the lack of generalizability in the employed between-site replication approach, our study suggests limited clinical utility of clinical and sociodemographic predictors. Predictive models including multimodal predictors may be more promising.

虽然平均而言非常有效，但基于暴露的治疗并非对所有患者都同样有效。对预后不良的患者的先验识别可能使更个性化的心理治疗干预的应用成为可能。我们旨在确定蜘蛛恐惧症 (SP) 治疗反应的社会人口学和临床治疗前预测因素。

N = 174 名 SP 患者在两个独立的地点接受了高度标准化的虚拟现实暴露疗法 (VRET)。使用组级分析来测试疗效。我们应用了最先进的机器学习协议（随机森林）来评估临床和社会人口学预测因子的预测效用，用于先验识别治疗后和 6 个月随访时直接评估的个体治疗反应。通过外部交叉验证测试了预测模型的可靠性和通用性。

我们的研究表明，一次 VRET 在组级别上非常有效，并且是最早揭示这种治疗效果长期稳定性的试验之一。个别短期症状的减轻可以预测为高于偶然性，但在我们的站点间预测和长期结果的预测中，准确性下降到无意义。由于性能指标几乎没有超过机会水平，并且所采用的站点间复制方法缺乏普遍性，我们的研究表明临床和社会人口学预测指标的临床效用有限。包括多模态预测器在内的预测模型可能更有前景。