Abstract

Objectives: Machine learning models predicting treatment outcomes for individual patients may yield high clinical utility. However, few studies tested the utility of easy to acquire and low-cost sociodemographic and clinical data. In previous work, we reported significant predictions still insufficient for immediate clinical use in a sample with broad diagnostic spectrum. We here examined whether predictions will improve in a diagnostically more homogeneous yet large and naturalistic obsessive-compulsive disorder (OCD) sample. Methods: We used sociodemographic and clinical data routinely acquired during CBT treatment of n = 533 OCD subjects in a specialized outpatient clinic. Results: Remission was predicted with 65% (p = 0.001) balanced accuracy on unseen data for the best model. Higher OCD symptom severity predicted non-remission, while higher age of onset of first OCD symptoms and higher socioeconomic status predicted remission. For dimensional change, prediction achieved r = 0.31 (p = 0.001) between predicted and actual values. Conclusions: The comparison with our previous work suggests that predictions within a diagnostically homogeneous sample, here OCD, are not per se superior to a more diverse sample including several diagnostic groups. Using refined psychological predictors associated with disorder etiology and maintenance or adding further data modalities as neuroimaging or ecological momentary assessments are promising in order to further increase prediction accuracy.

摘要

目标：预测个体患者治疗效果的机器学习模型可能会产生很高的临床效用。但是，很少有研究测试易于获得的低成本社会人口统计学和临床​​数据的效用。在以前的工作中，我们报告了具有广泛诊断范围的样本中的重大预测仍不足以立即用于临床。我们在这里检查了预测是否会在诊断上更均一但又大而自然的强迫症（OCD）样本中改善。方法：我们使用 在专门的门诊部对n = 533 OCD受试者进行CBT治疗期间常规获得的社会人口统计学和临床​​数据。结果：预测缓解率为65％（p = 0.001）最佳模型在看不见的数据上的平衡精度。较高的强迫症症状严重程度预示着症状不会缓解，而较高的首次强迫症症状发作年龄和较高的社会经济地位则预示了症状缓解。对于尺寸变化，预测 值在预测值和实际值之间达到r  = 0.31（p = 0.001）。结论：与我们先前工作的比较表明，在诊断均一的样本（此处为OCD）中的预测本身并不优于包括多个诊断组在内的更多样化的样本。为了进一步提高预测准确性，使用与疾病病因和维持相关的完善的心理预测因子或添加更多的数据形式作为神经影像或生态瞬时评估是有希望的。