In the Journals

EEG data effective treatment planning tool for patients with depression

Electroencephalographic, or EEG, data may serve as an effective treatment planning tool for patients with depression, according to results of a prognostic study published in JAMA Network Open. Researchers analyzed this data’s effectiveness regarding the antidepressant escitalopram.

“This study provided a proof-of-concept pipeline for predicting changes in depression severity after the start of escitalopram treatment,” Andrey Zhdanov, PhD, of the School of Mechatronic Systems Engineering at Simon Fraser University in Canada, and colleagues wrote. “Developed into a proper clinical application, such a pipeline may provide a valuable treatment planning tool. For large data sets that include several groups of patients, each receiving a different treatment option (pharmacologic and nonpharmacologic antidepressants), an approach similar to the one taken by this study may be useful in developing a model that can match each patient to the most effective treatment.”

According to Zhdanov and colleagues, the development of a tool capable of reliably predicting an individual patient’s treatment response would mitigate the economic and social costs heightened by long periods of time practitioners may currently spend identifying tailored depression treatments. They used EEG data from 122 patients diagnosed with major depressive disorder who were included in the first Canadian Biomarker Integration Network in Depression study to estimate EEG data’s predictive accuracy for escitalopram treatment outcomes.

Using support vector machine classifiers, the researchers found that use of EEG data recorded after the first 2 weeks of treatments resulted in an accuracy of 82.4% for predicting escitalopram treatment outcome, with a sensitivity of 79.2% and a specificity of 85.5%. For patients with only baseline EEG data available, the accuracy was 79.2%, with a sensitivity of 67.3% and a specificity of 91%.

“When complemented by appropriate analysis methods, resting-state [EEG] recordings may be instrumental in improving treatment of patients with depression,” the researchers wrote. – by Joe Gramigna

Disclosures: Zhdanov reports no relevant financial disclosures. Please see the study for all other authors’ relevant financial disclosures.

Electroencephalographic, or EEG, data may serve as an effective treatment planning tool for patients with depression, according to results of a prognostic study published in JAMA Network Open. Researchers analyzed this data’s effectiveness regarding the antidepressant escitalopram.

“This study provided a proof-of-concept pipeline for predicting changes in depression severity after the start of escitalopram treatment,” Andrey Zhdanov, PhD, of the School of Mechatronic Systems Engineering at Simon Fraser University in Canada, and colleagues wrote. “Developed into a proper clinical application, such a pipeline may provide a valuable treatment planning tool. For large data sets that include several groups of patients, each receiving a different treatment option (pharmacologic and nonpharmacologic antidepressants), an approach similar to the one taken by this study may be useful in developing a model that can match each patient to the most effective treatment.”

According to Zhdanov and colleagues, the development of a tool capable of reliably predicting an individual patient’s treatment response would mitigate the economic and social costs heightened by long periods of time practitioners may currently spend identifying tailored depression treatments. They used EEG data from 122 patients diagnosed with major depressive disorder who were included in the first Canadian Biomarker Integration Network in Depression study to estimate EEG data’s predictive accuracy for escitalopram treatment outcomes.

Using support vector machine classifiers, the researchers found that use of EEG data recorded after the first 2 weeks of treatments resulted in an accuracy of 82.4% for predicting escitalopram treatment outcome, with a sensitivity of 79.2% and a specificity of 85.5%. For patients with only baseline EEG data available, the accuracy was 79.2%, with a sensitivity of 67.3% and a specificity of 91%.

“When complemented by appropriate analysis methods, resting-state [EEG] recordings may be instrumental in improving treatment of patients with depression,” the researchers wrote. – by Joe Gramigna

Disclosures: Zhdanov reports no relevant financial disclosures. Please see the study for all other authors’ relevant financial disclosures.