Key result
Machine learning using imaging, clinical, and EEG data identifies predictors of electroconvulsive therapy response.
Why the study?
It remains difficult to reliably predict who will benefit from electroconvulsive therapy, making it a good candidate for machine learning approaches.
Can machine learning approaches reliably predict response to electroconvulsive therapy (ECT)?
Systematic Review (n=26)
Can machine learning approaches reliably predict response to electroconvulsive therapy (ECT)?
Machine learning approaches using diverse data types show promise in predicting patient response to electroconvulsive therapy, though further transdisciplinary research is needed.
Machine learning may aid ECT response prediction; extends multimodal evidence synthesis but leaves prospective validation open.
Despite years of research, we are still not able to reliably predict who might benefit from electroconvulsive therapy (ECT) treatment. As we exhaust what is possible using traditional statistical analysis, ECT remains a good candidate for machine learning approaches due to the large data sets with data captured through electroencephalography (EEG) and other objective measures. A systematic review of 6 databases led to the full-text examination of 26 articles using machine learning approaches in examining data predicting response to ECT treatment. The identified articles used a wide variety of data types covering structural and functional imaging data (n = 15), clinical data (n = 5), a combination of clinical and imaging data (n = 2), EEG (n = 3), and social media posts (n = 1). The clinical indications in which response prediction was assessed were depression (n = 21) and psychosis (n = 4). Changes in multiple anatomical regions in the brain were identified as holding a predictive value for response to ECT. These primarily centered on the limbic system and associated networks. Clinical features predicting good response to ECT in depression included shorter duration, lower severity, higher medication dose, psychotic features, low cortisol levels, and positive family history. It has also been possible to predict the likelihood of relapse of readmission with psychosis after ECT treatment, including a better response if higher transfer entropy was calculated from EEG signals. A transdisciplinary approach with an international consortium collecting a wide range of retrospective and prospective data may help to refine and extend these outcomes and translate them into clinical practice.
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Lundin et al. (2024) conducted a systematic review in Depression and psychosis (n=26). Machine learning approaches was evaluated on Prediction of response to ECT treatment. A systematic review of 26 articles demonstrated that machine learning approaches using imaging, clinical, and EEG data can identify predictors of response to electroconvulsive therapy.
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