Semple David M, Suveges Szabolcs, Steele J Douglas
University Hospital Hairmyres, NHS Lanarkshire, Glasgow, UK.
School of Medicine, University of Dundee, Dundee, UK.
Br J Psychiatry. 2024 Dec;225(6):547-555. doi: 10.1192/bjp.2024.126.
Despite strong evidence of efficacy of electroconvulsive therapy (ECT) in the treatment of depression, no sensitive and specific predictors of ECT response have been identified. Previous meta-analyses have suggested some pre-treatment associations with response at a population level.
Using 10 years (2009-2018) of routinely collected Scottish data of people with moderate to severe depression ( = 2074) receiving ECT we tested two hypotheses: (a) that there were significant group-level associations between post-ECT clinical outcomes and pre-ECT clinical variables and (b) that it was possible to develop a method for predicting illness remission for individual patients using machine learning.
Data were analysed on a group level using descriptive statistics and association analyses as well as using individual patient prediction with machine learning methodologies, including cross-validation.
ECT is highly effective for moderate to severe depression, with a response rate of 73% and remission rate of 51%. ECT response is associated with older age, psychotic symptoms, necessity for urgent intervention, severe distress, psychomotor retardation, previous good response, lack of medication resistance, and consent status. Remission has the same associations except for necessity for urgent intervention and, in addition, history of recurrent depression and low suicide risk. It is possible to predict remission with ECT with an accuracy of 61%.
Pre-ECT clinical variables are associated with both response and remission and can help predict individual response to ECT. This predictive tool could inform shared decision-making, prevent the unnecessary use of ECT when it is unlikely to be beneficial and ensure prompt use of ECT when it is likely to be effective.
尽管有强有力的证据表明电休克疗法（ECT）在治疗抑郁症方面有效，但尚未确定ECT反应的敏感且特异的预测指标。以往的荟萃分析表明，在总体水平上，一些治疗前因素与反应存在关联。
利用2009年至2018年期间苏格兰常规收集的2074例中重度抑郁症患者接受ECT治疗的数据，我们检验了两个假设：（a）ECT治疗后的临床结局与ECT治疗前的临床变量之间存在显著的组水平关联；（b）有可能使用机器学习开发一种预测个体患者疾病缓解的方法。
使用描述性统计和关联分析在组水平上对数据进行分析，并使用包括交叉验证在内的机器学习方法对个体患者进行预测。
ECT对中重度抑郁症非常有效，有效率为73%，缓解率为51%。ECT反应与年龄较大、精神病性症状、紧急干预的必要性、严重痛苦、精神运动迟缓、既往良好反应、无药物抵抗以及同意状态有关。缓解除了与紧急干预的必要性无关外，还与复发性抑郁症病史和低自杀风险有关。使用ECT预测缓解的准确率为61%。
ECT治疗前的临床变量与反应和缓解均相关，有助于预测个体对ECT的反应。这种预测工具可为共同决策提供参考，在ECT不太可能有益时防止不必要的使用，并在ECT可能有效时确保及时使用。
