Reliable predictors for electroconvulsive therapy (ECT) effectiveness would allow a more precise and personalized approach for the treatment of major depressive disorder (MDD). Prediction models were created using a priori selected clinical variables based on previous meta-analyses. Multivariable linear regression analysis was used, applying backwards selection to determine predictor variables while allowing non-linear relations, to develop a prediction model for depression outcome post-ECT (and logistic regression for remission and response as secondary outcome measures). Internal validation and internal-external cross-validation were used to examine overfitting and generalizability of the model’s predictive performance. In total, 1892 adult patients with MDD were included from 22 clinical and research cohorts of the twelve sites within the Dutch ECT Consortium. The final primary prediction model showed several factors that significantly predicted a lower depression score post-ECT: higher age, shorter duration of the current depressive episode, severe MDD with psychotic features, lower level of previous antidepressant resistance in the current episode, higher pre-ECT global cognitive functioning, absence of a comorbid personality disorder, and a lower level of failed psychotherapy in the current episode. The optimism-adjusted R² of the final model was 19%. This prediction model based on readily available clinical information can reduce uncertainty of ECT outcomes and hereby inform clinical decision-making, as prompt referral for ECT may be particularly beneficial for individuals with the above-mentioned characteristics. However, despite including a large number of pretreatment factors, a large proportion of the variance in depression outcome post-ECT remained unpredictable.

电休克疗法 （ECT） 有效性的可靠预测因子将为重度抑郁症 （MDD） 的治疗提供更精确和个性化的方法。预测模型是根据先前的荟萃分析使用先验选择的临床变量创建的。使用多变量线性回归分析，应用反向选择来确定预测变量，同时允许非线性关系，以开发 ECT 后抑郁结局的预测模型（以及缓解和反应的 logistic 回归作为次要结局指标）。内部验证和内部-外部交叉验证用于检查模型预测性能的过拟合和泛化性。总共包括来自荷兰 ECT 联盟内 12 个地点的 22 个临床和研究队列的 1892 名成年 MDD 患者。最终的主要预测模型显示了显著预测 ECT 后抑郁评分较低的几个因素：年龄较高、当前抑郁发作持续时间较短、具有精神病特征的严重 MDD、当前发作中既往抗抑郁药耐药水平较低、ECT 前整体认知功能较高、无共病人格障碍以及当前发作中心理治疗失败水平较低。最终模型的乐观调整后 R² 为 19%。这种基于现成临床信息的预测模型可以减少 ECT 结果的不确定性，从而为临床决策提供信息，因为及时转诊 ECT 可能对具有上述特征的个体特别有益。 然而，尽管包括大量的治疗前因素，但 ECT 后抑郁结局的很大一部分方差仍然不可预测。