Literature DB >> 30473402

Predicting treatment response to antidepressant medication using early changes in emotional processing.

Michael Browning1, Jonathan Kingslake2, Colin T Dourish2, Guy M Goodwin3, Catherine J Harmer3, Gerard R Dawson2.   

Abstract

Antidepressants must be taken for weeks before response can be assessed with many patients not responding to the first medication prescribed. This often results in long delays before effective treatment is started. Antidepressants induce changes in the processing of emotional stimuli early in the course of treatment. In the current study we assessed whether changes in emotional processing and subjective symptoms over the first week of antidepressant treatment predicted clinical response after 4-8 weeks of treatment. Such a predictive test may shorten the time taken to initiate effective treatment in depressed patients. Seventy-four depressed primary care patients completed measures of emotional bias and subjective symptoms before starting antidepressant treatment and then again 1 week later. Response to treatment was assessed after 4-6 weeks. The performance of classifiers based on these measures was assessed using a leave-one-out validation procedure with the best classifier then tested in an independent sample from a second study of 239 patients. The combination of a facial emotion recognition task and subjective symptoms predicted response with 77% accuracy in the training sample and 60% accuracy in the independent study, significantly better than possible using baseline response rates. The face based measure of emotional bias provided good quality data with high acceptability ratings. Changes in emotional processing can provide a sensitive early measure of antidepressant efficacy for individual patients. Early treatment induced changes in emotional processing may be used to guide antidepressant therapy and reduce the time taken for depressed patients to return to good mental health.
Copyright © 2018. Published by Elsevier B.V.

Entities:  

Keywords:  Antidepressant; Depression; Emotional bias; Machine learning; Prediction; Treatment

Mesh:

Substances:

Year:  2018        PMID: 30473402     DOI: 10.1016/j.euroneuro.2018.11.1102

Source DB:  PubMed          Journal:  Eur Neuropsychopharmacol        ISSN: 0924-977X            Impact factor:   4.600


  12 in total

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9.  Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex.

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10.  Ketamine modulates fronto-striatal circuitry in depressed and healthy individuals.

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