Quantitative EEG Can Guide Treatment of Depression

Nancy A. Melville

November 05, 2009

November 5, 2009 (Las Vegas, Nevada) — Quantitative electroencephalogram (EEG) results offer a better picture of treatment effects with antidepressant medications than a medication algorithm based on the National Institutes of Mental Health Sequenced Treatment Alternatives to Relieve Depression (Star*D) trial, a conventional treatment guidance tool, according to a study presented here at US Psychiatric and Mental Health Congress 2009.

The technology, called referenced EEG (rEEG; CNS Response, Inc.), is said to represent the first neurophysical referencing tool. The rEEG uses a treatment database of more than 12,000 patients to identify physiological markers and the specific pharmacotherapies that have been successful in treating them.

"Patients are screened and then the scans are compared with results from a database of patients who have responded to some medications and not to others," said Charles DeBattista, MD, lead author of the study and professor of psychiatry and behavioral health sciences at Stanford University School of Medicine in California.

"The rEEG will give specific recommendations — it will rank sensitivity to medication classes and even [drugs] within classes," he said.

The multicenter study involved 465 patients who were screened at 12 centers in the United States, including patients with depression treatment failure of 1 or more selective serotonin reuptake inhibitors and those with failure of at least 2 classes of antidepressants. From those patients, 114 were divided equally to receive treatment based on the rEEG recommendations or the Star*D guidance.

Eighty-nine patients completed the trial — 40 in the rEEG group and 49 in the Star*D group. After 12 weeks, both primary end points — a mean change from baseline for Quick Inventory of Depressive Symptomatology-Self Report-16 (QIDS-16-SR) and Quality of Life Enjoyment and Satisfaction Questionnaire Short Form (Q-LES-Q-SF) — were statistically significant in favor of the rEEG treatment group (P = .05).

The QIDS-16-SR scores were reduced by a mean of 6.77 points for the rEEG group and 4.50 points for the Star*D group, and maximum satisfaction in terms of Q-LES-Q-SF increased by a mean of 18 percentage points in the rEEG group and just 8.95 percentage points in the Star*D group.

Secondary end points that were statistically significant in favor of the rEEG treatment group included Clinical Global Impression Improvement and QIDS-16-SR measures in response and remission.

"The results showed that the rEEG treatment was consistently better at guessing what worked for patients," Dr. DeBattista said. "It was consistent across various measures and was better at determining these things than I would have expected."

rEEG assessment is appropriate for most psychiatric and addiction patients. The treatment database currently does not include patients with primary psychotic disorders, but a future study is planned to address that patient population.

The screening would cost about $800, and there is currently no indication of how or if insurance companies would reimburse for rEEG assessment.

But because the process of landing on just the right treatment for a patient with depression is often a lengthy one, the results could justify the cost, said Robert Dolgoff, MD, medical director of the Berkeley Therapy Institute in California.

"A typical medication trial lasts anywhere from 4 to 6 weeks, or even more, and if it doesn't work, you've had 4 to 8 weeks of suffering, you've wasted your time, and you've wasted the money on that medication that didn't work," he said.

"If you had a tool to help make a better guess at what is going to work, it would certainly make you and your patient happy. So it's a promising technology, and the big question will be how effective it turns out to be."

The study received financial support from CNS Response Inc. Dr. DeBattista receives research support from CNS Response INc. Dr. Dolgoff is on the speaker's bureau for AstraZeneca and Bristol-Myers Squibb.

US Psychiatric and Mental Health Congress 2009: Abstract 114. Presented November 2, 2009.