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Electroconvulsive therapy response and remission in moderate to severe depressive illness: a decade of national Scottish data

Published online by Cambridge University Press:  18 September 2024

David M. Semple Open the ORCID record for David M. Semple [Opens in a new window] ,
Szabolcs Suveges  and
J. Douglas Steele Open the ORCID record for J. Douglas Steele [Opens in a new window]
David M. Semple*
Affiliation:
University Hospital Hairmyres, NHS Lanarkshire, Glasgow, UK
Szabolcs Suveges
Affiliation:
School of Medicine, University of Dundee, Dundee, UK
J. Douglas Steele
Affiliation:
School of Medicine, University of Dundee, Dundee, UK
*
Correspondence: David Semple. Email: dsemple2@ed.ac.uk
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Abstract

Background

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.

Aims

Using 10 years (2009–2018) of routinely collected Scottish data of people with moderate to severe depression (n = 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.

Method

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 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%.

Conclusions

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.

Keywords

Electroconvulsive therapydepressive disorderefficacymachine learningprediction
Type
Original Article
Information
The British Journal of Psychiatry , Volume 225 , Issue 6 , December 2024 , pp. 547 - 555
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of Royal College of Psychiatrists

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