Abstract
Background
Despite strong evidence of efficacy of 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.
Aims
Using ten years (2009-2018) of routinely collected Scottish data of moderate-severely depressed patients (n=2,074) receiving ECT we tested two hypotheses – (1) there were significant group level associations between post-ECT clinical outcomes and pre-ECT clinical variables and (2) it was possible to develop a method for predicting illness remission for individual patients using machine learning.
Methods
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.
Results
ECT is a highly effective for moderate-severe depression, with a response rate of 73% and remission rate of 51%. ECT response is associated with older age, psychotic symptoms, urgency necessity, severe distress, psychomotor retardation, previous good response, lack of medication resistance, and consent status. Remission has the same associations except for urgent necessity and in addition history of recurrent depression and low suicide risk. It is possible to predict remission with ECT with an accuracy of 61%.
Conclusions
Pre-ECT clinical variables are associated with both response and remission and can help predict individual response to ECT. This predictive tool could help support 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.
Despite strong evidence of efficacy of 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.
Aims
Using ten years (2009-2018) of routinely collected Scottish data of moderate-severely depressed patients (n=2,074) receiving ECT we tested two hypotheses – (1) there were significant group level associations between post-ECT clinical outcomes and pre-ECT clinical variables and (2) it was possible to develop a method for predicting illness remission for individual patients using machine learning.
Methods
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.
Results
ECT is a highly effective for moderate-severe depression, with a response rate of 73% and remission rate of 51%. ECT response is associated with older age, psychotic symptoms, urgency necessity, severe distress, psychomotor retardation, previous good response, lack of medication resistance, and consent status. Remission has the same associations except for urgent necessity and in addition history of recurrent depression and low suicide risk. It is possible to predict remission with ECT with an accuracy of 61%.
Conclusions
Pre-ECT clinical variables are associated with both response and remission and can help predict individual response to ECT. This predictive tool could help support 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.
Original language | English |
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Journal | British Journal of Psychiatry |
Publication status | Accepted/In press - 30 Jul 2024 |
Keywords
- Electroconvulsive therapy
- efficacy
- machine learning
- prediction