From the Department of Neuropsychiatry, Keio University School of Medicine.
J ECT. 2020 Sep;36(3):205-210. doi: 10.1097/YCT.0000000000000669.
To identify important clinical or imaging features predictive of an individual's response to electroconvulsive therapy (ECT) by utilizing a machine learning approach.
Twenty-seven depressed patients who received ECT were recruited. Clinical demographics and pretreatment structural magnetic resonance imaging (MRI) data were used as candidate features to build models to predict remission and post-ECT Hamilton Depression Rating Scale scores. Support vector machine and support vector regression with elastic-net regularization were used to build models using (i) only clinical features, (ii) only MRI features, and (iii) both clinical and MRI features. Consistently selected features across all individuals were identified through leave-one-out cross-validation.
Compared with models that include only clinical variables, the models including MRI data improved the prediction of ECT remission: the prediction accuracy improved from 70% to 93%. Features selected consistently across all individuals included volumes in the gyrus rectus, the right anterior lateral temporal lobe, the cuneus, and the third ventricle, as well as 2 clinical features: psychotic features and family history of mood disorder.
Pretreatment structural MRI data improved the individual predictive accuracy of ECT remission, and only a small subset of features was important for prediction.
通过机器学习方法，确定对个体电惊厥治疗（ECT）反应有预测价值的重要临床或影像学特征。
招募了 27 名接受 ECT 的抑郁患者。将临床人口统计学和治疗前结构磁共振成像（MRI）数据用作候选特征，以构建预测缓解和 ECT 后汉密尔顿抑郁量表评分的模型。支持向量机和带有弹性网络正则化的支持向量回归用于构建仅使用（i）临床特征、（ii）MRI 特征和（iii）临床和 MRI 特征的模型。通过留一交叉验证确定所有个体中一致选择的特征。
与仅包含临床变量的模型相比，包含 MRI 数据的模型改善了 ECT 缓解的预测：预测准确性从 70%提高到 93%。所有个体中一致选择的特征包括直回、右侧前外侧颞叶、楔叶和第三脑室的体积，以及 2 个临床特征：精神病特征和心境障碍家族史。
治疗前结构 MRI 数据提高了 ECT 缓解的个体预测准确性，并且仅一小部分特征对预测很重要。
