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Editorial
June 2016

Determining Electroconvulsive Therapy Response With Machine Learning

Author Affiliations
  • 1Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque
  • 2National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  • 3The Mind Research Network, Albuquerque, New Mexico
JAMA Psychiatry. 2016;73(6):545-546. doi:10.1001/jamapsychiatry.2016.0348

In this issue of JAMA Psychiatry, Redlich et al1 assess the predictive potential of baseline (before electroconvulsive therapy [ECT]) structural neuroimaging with a set of machine-learning–based approaches. In the pre-ECT evaluation and consent process, the ECT health care professional must balance the anticipated benefits and risks for an individual patient. Ideally, the ECT health care professional would be able to accurately determine a patient’s likelihood of response based on demographics, symptom severity, phenomenology, and treatment history. A number of indices were created as early as 1950 based on clinical variables that included melancholia, family history, symptom severity, age, sex, and the presence of psychosis and personality disorders. Unfortunately, a recent meta-analysis of factors associated with ECT2 has shown that these clinical factors are imperfect. Only longer duration of symptoms and antidepressant treatment resistance are reliably associated with ECT nonresponse.

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